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MULTIPOS

D2.11 Version 1.0

Recommendations to EU mobile operators, governments, data protection authorities and companies in the location value chain on how to generate

a take-off of mobile network user data

Contractual Date of Delivery: T0 + x (months)

Actual Date of Delivery:

Editor: Editor(s) name (s)

Author(s): Paolo Pileggi

Participant(s): PTOLEMUS Consulting Group

Work package: WP2 – Recommendations to EU mobile operators, governments, data protection authorities and companies in the location value chain on how to generate a take-off of mobile network user data

Version: 1.0

Total number of pages: 0

Abstract: The business of mass mobile user network data in Europe is still in its nascent stage. This same business is growing to a much larger extent in other parts of the world, particularly in the United States of America. There are some obstacles that impede the take off of this business in Europe. In this report, we explore important technical aspects of the business in order to identify and understand the key obstacles that need to be dealt with. The key stakeholders are identified, and recommendations are made to them as initial suggestions in order to overcome the obstacles and see a take off of the mass mobile user network location business in Europe. Disclaimer:

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Document Control Version Details of Change Review Owner Approved Date 1.0 Report version 1.0 PP

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Executive Summary This document describes the current state of the business of mass mobile user network location (that we refer to simply as the business of MASS) in Europe and gives insights into this business from a global perspective. The objective is to identify the key obstacles that are preventing this business from taking off in Europe.

The data is typically procured by mobile network operators and available to them en masse. The document describes the nature of the data and the characteristics of the systems in place to manage them.

The technologies are described in such a way as to give insight into the evolution of network-based positioning technologies. As communication technologies have entered their fourth generation status, many are already speculating and planning what a fifth generation technology entails. This report focuses specifically on location-enabling technologies.

The value chain of the business is used to identify and describe the key stakeholder in this business. The value propagation through the chain is described and strategic business forces are considered.

This report ultimately provides recommendations to

1. European mobile operators,

2. European governments,

3. European Data Protection Authorities, and

4. companies in the location value chain,

as to how they can actively and effectively participate in the take off of the mass mobile user network location business in Europe, such that a global business would naturally be able to follow.

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Authors

Partner Name Phone / Fax / e-mail

PTOLEMUS Paolo Pileggi e-mail: ppileggi@gmail.com

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Table of Contents

Document Control ............................................................................................ 3

List of Acronyms and Abbreviations .............................................................. 8

1. Introduction ................................................................................................. 9

1.1 Applications and benefits of cellular-enabled location analytics................................................. 9 1.2 Document outline ...................................................................................................................... 11

2. Big location data generated by mobile network operators ................... 12

2.1 The building blocks of big location data records ....................................................................... 12 2.2 Management of the data ............................................................................................................ 13

2.2.1 Ownership of the data ....................................................................................................... 14 2.2.2 How the value of location data changes over time ............................................................ 15

2.2.2.1 Real time access to location data ........................................................................... 15 2.2.2.2 Historical location datasets .................................................................................... 16

2.2.3 The degree of data accessibility ........................................................................................ 16 2.2.4 Quantifying the amount of data ......................................................................................... 17 2.2.5 Ways to augment the data ................................................................................................. 18

2.3 Data legislation as an enabler or a barrier.................................................................................. 20 2.3.1.1 How is location data privacy accounted for by the law ......................................... 21 2.3.1.2 The General Data Protection Regulation as an enabler of MASS in Europe ......... 22 2.3.1.3 The blocking of MASS reflected by the need for regulation ................................. 23

3. The evolution of network-based positioning technologies ................... 25

3.1 Comparison of positioning methods .......................................................................................... 25 3.1.1 Network-based methods .................................................................................................... 25

3.1.1.1 Cell-ID ................................................................................................................... 25 3.1.1.2 Enhanced-Cell-ID (E-Cell-ID, i.e., Cell-ID with TA and NMRs) ......................... 28 3.1.1.3 Enhanced-Observed Time Difference (E-OTD) .................................................... 29 3.1.1.4 Time of Arrival (ToA) ........................................................................................... 29 3.1.1.5 Uplink-Time Difference of Arrival (U-TDoA) ...................................................... 29

3.1.2 Performance criteria .......................................................................................................... 30 3.1.2.1 Accuracy ................................................................................................................ 31 3.1.2.2 Coverage ................................................................................................................ 33 3.1.2.3 Reliability .............................................................................................................. 34 3.1.2.4 Speed ..................................................................................................................... 35 3.1.2.5 Cost ........................................................................................................................ 36 3.1.2.6 Power consumption ................................................................................................ 37

3.2 How network positioning technologies have evolved over the generations .............................. 38 3.2.1 After-the-fact location for 2G and 3G ............................................................................... 38 3.2.2 Built-in positioning for 4G LTE ....................................................................................... 38 3.2.3 Hybrid methods for network positioning .......................................................................... 40

3.3 The 5G outlook for positioning technologies ............................................................................ 40

4. Value propagation and strategic business forces ................................. 42

4.1 The cellular-enabled MASS location value chain ..................................................................... 42 4.1.1 Location technology provider ........................................................................................... 42 4.1.2 Network infrastructure provider ........................................................................................ 42 4.1.3 Mobile cellular network operator ...................................................................................... 43 4.1.4 Location data aggregator ................................................................................................... 43 4.1.5 Data analyst ....................................................................................................................... 43 4.1.6 Data customer / consumer ................................................................................................. 44

4.2 Power and control in the value chain ......................................................................................... 44 4.3 Expected trends and strategic moves ......................................................................................... 44

5. Conclusions and recommendations ....................................................... 45

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5.1 Recommendation to mobile operators ....................................................................................... 45 5.2 Recommendations to governments and data protection authorities ........................................... 45 5.3 Recommendation to companies in the location value chain ...................................................... 46

6. References ................................................................................................. 47

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List of Acronyms and Abbreviations Term Description AoA Angle of Arrival CDR Call Detail Record E-OTD Enhanced-Observed Time Difference GDPR General Data Protection Regulation GTD Geometric Time Difference IMEI International Mobile Equipment Identifier IoT Internet of Things LAI Location Area Identifier LBS Location-Based Service LIS Location Insight Service LMU Location Measurement Unit LPP LTE Positioning Protocol LTE Long Term Evolution MASS MASS mobile user network location (business) MNO Mobile Network Operator MPC Mobile Position Centre NMR Network Management Record OMA Open Mobile Alliance QoS Quality of Service RTD Real Time Difference SUPL Secure User-Plane Location TA Timing Advance TDoA Time Difference of Arrival ToA Time of Arrival WiFi Wireless Fidelity

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1. Introduction Positioning services fall into either of two broad categories, namely Location-Based Services (LBS) or Location Insight Services (LIS). The notion of LBS is far more familiar to the average business man and reader than that of LIS. LBS covers the group of on-demand positioning services that typically target users individually. Differently, LIS offers predictive power through the management and analysis of location data for a significantly larger number of users and for extended periods of time [1]. This enables the extraction of insight and value from big location data. Of the many types of positioning methods that exist, the mobile network-based methods are probably the most well-known. In this study, our particular focus is on the business of exploiting mobile user (cellular) network location data of very large numbers of users. Moreover, we investigate this business, which we shall refer to as the business of mass mobile user network location data (MASS), in Europe. We call on international references within the European continent, as well as overseas cases, to explain how the business functions and to identify the obstacles that are preventing the take-off of MASS in Europe. The challenges we identified are not only related to the technologies used. Legal issues, particularly those that involve public acceptance, are also a challenge to be addressed and warrant a discussion of its own. Key stakeholders in the MASS value chain need to be motivated. In particular, the mobile network operator connects large numbers of users (i.e., mobile subscribers) with the necessary network technologies, making the operator essential stakeholder. It is paramount then that a viable business case be clear is discovered. We need to explain how each stakeholder can benefit relative to their participation. This document gives insight into the core challenges and breakthroughs in the MASS business. Moreover, we provide some recommendations to key stakeholders in this business as an initial best practice framework. By properly addressing the issues presented in this document, we believe that a sound and highly profitable business case can emerge naturally in Europe.

1.1 Applications and benefits of cellular-enabled location analytics Location analytics is the study of location datasets to gain insight about activities in the physical world. Information obtained by using smartphones, Wireless Fidelity (WiFi) networks, and a number of other technologies can be exploited so that location analytics solutions are operational quickly and with minimal costs. Mobile cellular network operators have a (largely) untapped resource within their direct reach. In Europe alone, cellular network subscribers exceed 10% of the almost 7 billion mobile subscribers worldwide in 2014 [2]. Without any significant additional effort then, operators can estimate the physical location of their (millions of) subscribers, including roaming users just as well. Therefore,

• at a significantly lower cost than sourcing location data using other technologies, and • without requiring subscribers to actively participate,

mobile operators can make huge amounts of location available for location analytics purposes. Although some stakeholders may object to the relatively lower levels of accuracy achieved using these methods, the utility of this data has been proven many times over before, as you will read further on in this report.

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Location analytics has become a very big business that can benefit significantly from the business of MASS. MarketsAndMarkets1 estimates the global market to be worth $6.83 billion in 2014, with a compound annual growth rate of 11.6% from 2014 to 2019, growing to a staggering $11.84 billion. In the business of location analytics, analysis is applied to huge volumes of location data. Firms like RetailNext track hundreds of millions of retail shoppers each year. Each customer can generate over 10 000 unique data points per visit alone and data can be obtained from a very wide range of sources, as shown for RetailNext in Figure 1-1. This effectively results in the collection of billions, e.g., 6 billion, customer measurements daily for location analytics companies (like Euclid Analytics). As a result of this huge quantity of data obtained using a variety of techniques, location analysis can give very reliable insights.

Figure 1-1 RetailNext, a specialist company in big data solutions, makes data readily available for a large variety of applications from a wide range of data sources

Moreover, location analytics is more than just a mere exercise involving data-enriched maps. Beyond using the available location data, you can (re-)combine different information to gain new insights. For example, Esri is a big player in the location analytics business with updated demographics, consumer spending, lifestyle and business data. As shown in Figure 1-2, Esri’s location analytics solutions address different layers of complementary information, such as demographics, personal data, and so on.

Figure 1-2 Layers of complementary information as addressed by Esri’s location analytics solutions 1 MarketsAndMarkets is a full-service market research company and consulting firm that produces many reports.

Online at http://www.marketsandmarkets.com/. Last accessed on 12/08/2014.

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1.2 Document outline

This report is structured as follows.

2. Big location data generated by mobile network operators In Section 2, we concentrate on the nature of the data. The mobile network operator plays a central role by connecting mobile users and offering technologies that allow network-based positioning. Therefore, we describe what location data is actually collected and report how different companies manage the data. We estimate the scale of the data and discuss the ways in which location data can be augmented in order to understand better the context of the environment.

3. The evolution of network-based positioning technology In Section 3, we describe network-based positioning methods, starting with the basic Cell-ID method. Comparisons are compared with reference to important performance parameter. The essential performance parameters concerned with MASS are discussed as well, and we comment on the evolution of positioning technologies as we enter the era of 5th generation technology.

4. Value propagation and strategic business forces In Section 4, we present the MASS value chain in terms of the key stakeholders and their primary responsibilities. We comment on the power distribution of stakeholders and highlight the trend of MASS business operations.

5. Conclusions and recommendations

In our conclusion, we summarise the driving forces behind the MASS business, how this is done, and who should become more actively involved for there to be a take off of this business in Europe. We give our recommendations to the key stakeholders, where it is most needed.

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2. Big location data generated by mobile network operators Generating location data using the Cell-ID positioning method makes it possible to find the location of a device without its own knowledge thereof. In fact, Cell-ID and its variations are the only methods that make this possible because the SIM card installed in the device is owned by the mobile network operator. Using the Cell-ID family of methods, network operators can collect data from the cell towers with which the device communicates and, depending on the specific type of method, it can use different timing information to determine the angles of arrivals at each tower. Techniques, such as triangulation and multi-lateration, make it possible to estimate the position of the device, called the position estimate. A major disadvantage of this method, however, is that the accuracy of the method depends on the density of the network deployments in the area, as well as the sensitivity of the technology used. Typically, the Cell-ID method is known to provide low accuracy of 100 metres under relatively favourable conditions and considerably more, reaching up to 30 kilometres – even after the significant improvements are considered, made over the last few years. Even so, these position estimates prove to be valuable when many estimates are available, as statistical analysis has demonstrated that it is possible to identify and provide insight into mass mobile user trends and experiences.

2.1 The building blocks of big location data records To make a location request or query, one needs to provide the identity (ID) of the device being located. The ID depends on the network itself. For example, it may use a hashtag, mobile phone number or International Mobile Equipment Identity (IMEI) for GSM or UMTS mobile phones, as Polaris Wireless’s OmniLocate location platform allows. The location data point is given in a format predefined by the network operator. From the format, the latitude and longitude (LatLong) data (i-.e., the x- and y-co-ordinate) are obtained. Together with mapping software, these data provide a meaningful position information. In some cases, these co-ordinates constitute the position estimate of the device but it is also possible that, for example, as explained by Mobile Commerce in the UK, the co-ordinates provided indicate the position of the cell tower. An additional value is then necessary, indicating the estimated distance from the cell tower to the device itself. The bearing of this distance from the tower is unknown unless additional information can be guaranteed, such as cell-sector information. The time and date of the estimate are important information because it may be that a device has been switched off for a significant period of time. Typically, in the UK, operators keep the last position record for a device. It is therefore not guaranteed that the position reported is the position at the time of the request; rather, it is the most recent position from the time of the request. Airsage processes more than 15 billion phone locations daily in the USA as LatLong data. The resulting meaningful and actionable data produced, is time- and date-stamped. Some positioning middleware platforms, such as the hybrid positioning technology of Polaris Wireless, can also provide information regarding the type of technology being used. Depending on the capability of the device, it is possible to determine which technology has been used in the position calculation. Intersec manages to report information about the model of the mobile phone being used. Skyhook integrates different positioning systems in their hybrid positioning system, which includes cellular network positioning. They offer a certified location service. This service, although more expensive, provides positioning data (including some contextual information, where possible) as well as

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an indication of confidence in the accuracy. Since they offer a hybrid solution, it makes sense that they may offer a wide range of additional information when available, such as the altitude of a device. A management data approach, such as control-plane fingerprinting, demonstrated by Cellint Traffic Solutions, provides highly accurate positioning for road networks. The sequences of network management messages are recorded during a learning phase of the system. The virtual sensors generated are consequently used to provide not only the co-ordinates on the road, but also the direction in which the device (or vehicle) is travelling. Due to the growing privacy concerns, Deveryware has developed a system that includes consent information to the information that can be obtained from the network. The consent information is then the permission configuration agreed to (i.e., configured) by the mobile user. This requires that the mobile user installs and configures at least one of their LBS applications. In essence, the key data that constitute the three basic building blocks of a location data record indicate the location that can be referenced on a map, i.e., the geographical coordinates, for whom or what the location is being reported, and an indication of when the position was estimated. From the examples, other data, such as a qualifier of the level of accuracy, may prove to be useful but it depends on the application itself as to how much value such data can bring to the analysis. Jasper Wireless, who specialise in providing middleware for connected devices, support the communication of all kinds of data. Therefore, if the information can be acquired at some point in the ecosystem, there are sophisticated technologies already in place to support the communication of such data. Figure 2-1 shows an example of the cellID information returned from the OpenCellID database that must still be converted into useful location co-ordinates.

Figure 2-1 An example of the cell ID information returned from the OpenCellID database that must still be converted into useful location co-ordinates

2.2 Management of the data

The key processes involved in data management are • Collection

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• Storage • Analysis • Presentation

The importance of each is essential to obtaining the most value from the location data systems while, at the same time, minimising costs. These processes apply especially to big data systems. Of the key factors that have been identified in a study by Ventana Research [3], visibility was found to be most important, with 60 percent of the organisations included in the study mentioning it. These results are reproduced and shown in Figure 2-2. There is clearly an emphasis on the presentation of the of the knowledge derived from the data.

0%

20%

40%

60%Visibility

Functionality

Reliability

Return on InvestmentManageability

Adaptability

Validation

Ratings of key management evaluation criteria as reported by Ventana Research

Figure 2-2 Ratings of key management evaluation criteria [3]

However, the other criteria, namely functionality, reliability, manageability, adaptability, and validation, are not to be neglected. Making sure the collection, storage and analysis processes are essential to having these other criteria implemented properly. These processes are to be implemented by different players in the business and it is therefore key to understand who are responsible and what the consequences are regarding the value of the data and to whom this value belongs. It is surprising that validation is considered least important of these criteria, with just under 20% of the companies seeing much value in it. This could be due to the typically complex nature to validate correctly and accurately and that this is usually a time-consuming task for a specialist.

2.2.1 Ownership of the data The topic of location data ownership can be rather complicated due to the fact that the use of the data raises very serious data privacy concerns but ultimately, in practice the lines are fairly clear: It depends on the contract in place. For example, Polaris Wireless offers the positioning middleware platform together with some infrastructure provider – i.e., Alcatel-Lucent in their case – to make the collection, storage and analysis of location data possible for the customer, i.e., the mobile operator. They offer their software as a basic tool and make modifications to suit the needs of the operator. Eventually the software operates behind the

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firewalls of the operator, collects location information for the operator and the data belongs to the operator. In some special cases, such as a foreign operator requesting Polaris Wireless’s product for national security purposes, typically they would offer their services to train staff and consult with a local company of the foreign nation. In the same way, Polaris Wireless understand that they provide services to accommodate the collection, storage and analysis procedures but they choose distance themselves from owning the data. Similarly, Intersec offers products and services that facilitate these management procedures. However, the ownership of the data is that of the operator. Location data may be a valuable asset to own but, at the same time, owning such data introduces great risk into one’s business. There are strict legal issues, discussed later on in this section, that may lead to significant financial penalties, not to mention putting one’s reputation at risk. There are some companies, such as Mobile Commerce, who believe that the ownership of the location data ultimately should be that of the person whose location has been estimated.

2.2.2 How the value of location data changes over time Timing is a crucial aspect because knowing that someone has been in certain location – although useful – is considerably less valuable as to knowing when that person was in that location. This raises two types of timing issues to consider, namely knowing when the person is in a certain position and how quickly the positioning data can be accessed, i.e., timeliness, which is a component of availability. In particular, the timeliness depends on the capability of the technology combined with any rules or conditions the provider of the data would like to enforce. However, as Mobile Commerce explain, their customers can only access the most recent position estimate of a device since this is the only data that their suppliers, i.e., mobile operators, make available. Therefore, a question is raised about the value of the data as it ages.

2.2.2.1 Real time access to location data

Real time data refers to data that is available immediately, corresponding to what is happening at the present moment. However, typically real time is synonymous with quasi-real time since there is always an unavoidable access time delay introduced by the technologies that calculate and are deployed to disseminate the location information. In the KPMG Technology Innovation survey of 2013, they report that companies feel that both mobile and cloud technologies have as the top benefit “… the provision of easier access to real-time personalised information.” [4]. Included in that information is location data. Intersec have managed to deliver a mass-scale view on the whole subscriber base in real time. Similarly, Mobile Commerce and Skyhook provide access to the data in an as good as real time fashion. Polaris Wireless emphasise that real time localisation is essential, particularly for the emergency services such as the E911 service that they specialise in, and hence it is supported by their platform. Cellint Traffic Solutions, Deveryware and Airsage are also all offering real time solutions. In some cases, like Skyhook for instance, the real time nature of accessing the data may be compromised. Their location references are updated in scheduled batches. This means that the position estimate is for the most recently updated version of this system. However, the physical location of cell towers and WiFi access points are not frequently moved much, if at all.

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It is evident that the nature of location data services necessitates real time access to the data itself. Clearly this challenge is not a significant obstacle that prevents the take-off of such the MASS business.

2.2.2.2 Historical location datasets

Depending on the ownership and on their obedience to current legislation with respect to personal data management, data is kept for varying periods of time. A way to make sure at least some value can be retained – in the case of legal concern – Cellint Traffic Solutions explain that they only keep the analysis results of the location information. This not only aggregates the information in an anonymous way but also significantly reduces the amount of secondary storage space required. They therefore do not need to delete any of their results – ever. A disadvantage of this approach is that one cannot perform additional analyses on the recorded location information once it has been removed. It is therefore important to plan the necessary analyses well beforehand. Another way to be able to keep the data would be if one were to anonymise the data. Positium LBS claim that this is one of the more challenging tasks. One needs to anonymise data so that the original personal information cannot be obtained by a reversal of the anonymisation process. As for Deveryware in France, data is kept for the recommended time as specified by French law. It depends on the application for which the location data is used since it may not be kept for longer than the time it is required to provide the service. For their friend-finder example, the position estimate is not kept past the last position of the user’s device. For other services it is kept longer. Polaris Wireless does not maintain any data personally. They do, however, offer the feature by way of their platform. They say that it depends on the customer; some keep data for 90 days, others for 30 days, depending on the application. Some mobile operators keep data for about 5 to 10 minutes, as Mobile Commerce have learnt from their partnerships with some mobile operators. Ultimately, businesses, government agencies and many other organisations can use aggregated and anonymous location-related information to model, evaluate and analyse the movement and flow of users, such as customers entering a store, spectators entering a sport stadium, or drivers using busy road networks in a city. Airsage have many references that demonstrate how a history of data points is essential. However, depending on the precise nature of the application, the time-frame is a necessary input, making data beyond a certain point in the past less valuable.

2.2.3 The degree of data accessibility AirSage partners with operators, giving them complete access to the location data. However, they do not provide these ‘raw’ trace data to their customers. They process the data first. Their argument for not providing raw data to their customers is that it may be possible to reverse engineer the data points such that they are not anonymous anymore. Accessibility is therefore impacted by anonymity of the data. An API is typically provided, as in the case of Mobile Commerce. It seems that in the UK, many operators have come to some form of agreement. This makes it easier for companies such as Mobile Commerce to provide access to data from various operators. However, as CellVision point out, a select few operators are starting to commercialise their location datasets and, what is more problematic, is that they are doing so in a federated way. Already it is challenging to have operators collaborate nationally. This problem is compounded when one goes on to consider the pan-European context.

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2.2.4 Quantifying the amount of data Telefonica Dynamic Insights has a large customer base of over 3 million customers worldwide. Collecting and storing location data for such large customer bases requires significant processing and storage resource. Depending on the role of the participating company, there are different ways of thinking about the volume of data. Generally, when considering the collection of the data and processing of large location datasets, the throughput or sampling frequency is a good measure. The amount of secondary storage is a good measure particularly for those companies that store the actual data to either analyse later or to make available to other parties at a later stage. In the USA, Airsage processes over 15 billion data points daily. On average, they see a unique device 150 times a day but it depends on the device type, such as a smartphone operating various applications, an earlier generation feature phone, etc., as well as the technology used. Considering that Airsage owns a trace file of location data with records since 2009, their data would be to the order of Petabytes (PB) or more. However, they give access to this information indirectly by only providing their customers with a small spreadsheet with statistic results. Polaris Wireless point out that the sampling frequency of the position estimate of the device depends on the population and the application. Their middleware allows one to record events in an on-demand fashion or one can have periodic scheduling over a specified period. They explain that their see customers typically dealing with Gigabytes (GB) or Terabytes (TB) of location information. The amount of data also depends on how long the data is kept. As Deveryware explain, their friend-finder LBS application maintains only the most recent position of the device, whereas for other applications, the position estimates of a device over time are kept for as long as they are necessary for the service to function. Deveryware collect and store data but do not sell their data yet. They cannot disclose the amount of location data they process and how much of that they store, since it is confidential, but they emphasise that they maintain all of their data on servers located in France. Therefore, they are sure of the law that applies to how they manage their data. For a metropolitan area servicing 8 million people, Cellint Traffic Solutions have managed to reduce storage space requirements to a mere 1 GB of space daily. They achieve this by storing the statistics instead of the raw location data. Since Mobile Commerce takes on a brokerage role for operators, on a per-record basis, they are not aware of how much data is eventually recorded beyond the most recent position estimate for a particular device. Some companies, such as Skyhook, do not focus on how much data they are processing in terms of location requests. Rather they give an idea of the size of their reference system. Even though they have over 30 million cell towers in their system, as shown in Figure 2-3, WiFi access points are by far the main source of location references, having over a billion access points registered.

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Figure 2-3 Components and dimension of Skyhook’s location reference system

Similarly, Navizon have more WiFi locations than cell towers in their reference system. However, they have a couple of million towers and their location database is updated daily, with more than a million contributors. Intersec’s mass-scale geolocation middleware, called IGLOO, whose architecture is shown in Figure 2-4, manages to capture around 250 000 events per second from mobiles using a standard server. Anonymous data about the current location is collected for several hundred millions of subscribers. However, since there is no storage capacity for IGLOO as a standalone product, Intersec were only confronted with the challenge to process such a large amount of events, which depends on the subscriber base of their mobile operator customer. Nevertheless, the capability to store the history of the location data is complementary with another Intersec product or can simply be recorded by the operators themselves.

Figure 2-4 The architecture of Intersec’s IGLOO geolocation middleware

Ultimately, we are talking about a couple of billion events daily, depending on the operator’s size and the market characteristics. The figures would vary greatly between the different players and markets.

2.2.5 Ways to augment the data Augmentation of the location data can be done in two distinctly different ways:

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The first would be by augmenting the positioning results with information obtained from a different positioning technique, often referred to as hybrid positioning. Many big positioning middleware companies have developed their patented hybrid positioning technologies. For example, Polaris Wireless technology draws on positioning results from various technology types. Certain combinations of methods are more suited to certain use cases, which is important to take into careful consideration because some methods are more expensive than others. In general, the more accurate one expects the location to be, the costlier it becomes. Another way to augment data is by considering additional data types. Transport for London makes available location data of its hourly bicycle rental service via open feeds on their website. This is intended to allow independent application developers to create their own applications around the city infrastructure. Combining this data with information retrieved from services such as Twitter, Foursquare and Google Plus, a person’s particular and regular journeys can be identified and tracked. Moreover, as explained by software engineer James Siddle, without too much effort and a deeper inspection of dates, times, locations, and so on, one can reveal much more detail – perhaps even enough to identifying individuals by name [5]. As a simple practical example of data augmentation relating to location data, one can always consider digital photographs that have been uploaded to Facebook. Matching images in these photos for a specific user to the time of uploading them could give a rough indication or confirmation of the person’s physical location. Intersec consider different sorts of events collected, not only location data. For example, they consider call detail records (CDR) as well. In practice, a CDR includes metadata such as

• Subscribers’ phone numbers (numbers of the caller and the subscriber being called) • Starting time and duration of the call • Number of subscriber to be charged for the call • Call type, i.e., voice call, SMS, etc. • Any faults encountered

Smart alerts and escalations are involved in the real-time geolocation done by Deveryware. They therefore know who or what object they are referring to and when they are at that location. For data analytics, data augmentation is an essential component for Polaris Wireless. Although they do not explicitly solicit any additional information, they can make it possible to augment location data using metadata such as accident data, user interests, GIS information and CDRs as well. They offer an analytics package with their positioning middleware even though, in some cases, customers have their own analytic tools. Cellint Traffic Solutions offer road traffic location by way of software sensors. They do not include any additional information to augment the system’s contextual awareness. However this does not necessarily mean that their customers do not combine the location data with other information to do their own analyses. Figure 2-5 shows an example of sensor quality traffic information for relevant roads through a Cellint interface over Google Earth.

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Figure 2-5 Traffic information with sensor quality for all relevant roads through a Cellint Traffic Solution interface over Google Earth

Even though Skyhook removes user-sensitive information upon entering the database to ensure a high degree of anonymity, they still have sufficient information to determine demographic information based on the location information itself.

2.3 Data legislation as an enabler or a barrier

EU legislation on position information is addressed in the broader category of data security. One aspect is the dependability of a system with its attributes being availability, reliability, safety, confidentiality, integrity and maintainability [6]. The more relevant aspect to our discussion is the notion of data privacy. Privacy is the ability of an individual or group of individuals to seclude themselves or information about themselves, whereby revealing themselves selectively. This has evolved historically, where the distinction between public and private domains have been central concepts. Privacy may be physical in nature or it may be informative. The latter is data privacy, and refers to the evolving relationship between technology and the legal right to, or public expectation of, privacy in the collection and sharing of personal data. Enforcing privacy is a challenge for legislators, as governments must carefully define the border between private and public domains, and then follow through with the enforcement thereof. Consequently, the rule of law should apply to all individuals, companies and governments equally in its legislation. For national and perhaps international security reasons, governments should be able to intrude, such as in the case of a criminal investigation. However, this sort of intrusion should be clearly define, not only in terms of what can be intruded upon but also under which conditions and to which extent the intrusion may take place. Moreover, to protect individuals, they should not be punishable for information they disclose private information beyond that which is suggested or required by the law.

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2.3.1.1 How is location data privacy accounted for by the law

Mobile and cloud technologies may be considered the key enabler of easier access to real-time personalized information, such as location data, but KPMG found in their survey that companies feel that the biggest challenge is privacy and transparency concerns [7]. A big challenge for privacy legislation is that the borders are understood differently in different parts of the world. In the EU, an extensive legislation on data privacy was established in 1995 already. It was an initiative following the logic that everyone has the right to respect for his private and family life, his home and his correspondence. The data protection directive (Directive 95/46/EC) embodies this sentiment. It applies to any operation or set of operations that is performed on personal information, i.e., the processing of data. This includes the collection, storage and disclosure of the data. Furthermore, it applies to data processed by automated means, such as a computer database of customers, as well as to data being a part of, or intended to be a part of, non-automated filing systems. Directive 97/66/EC was drawn up to deal specifically with the protection of privacy and telecommunications. Member states are obliged to guarantee confidentiality of communication through national regulations. Any unauthorized listening, tapping storage of, or other kinds of interception or surveillance of communications is illegal. In 2002, the directive on privacy and electronic communications (Directive 2002/58/EC) was drawn up for the processing of personal data and the protection of privacy in the electronic communications sector. Positioning data privacy is included in the protected area. The data retention directive (Directive 2006/24/EC) was drawn up in 2006. It amended Directive 2002/58/EC and concentrated on the rules of the retention of data generated or processed in connection with the provision of public available electronic communications services of public communications networks. It applies to data gathered referring to communication events of every individual and business and it includes metadata such as the base stations used, the numbers calling and called, IP-addresses, duration and timestamps. The retention directive ensures that data is kept only for as long as it is needed for the service by which is it used. In fact, it forces companies to retain the information, in order to help investigations of serious crimes and terrorism. Even though the purpose is claimed to be in the interest of public safety, many journalists, human rights groups, IT security firms and physicians, amongst others, raised some serious concerns about the violation of human rights by the directive [8]. However, at the early in April 2014, Brussels’ highest court invalidated the retention directive [9]. Wide-ranging and particularly serious interference were found with the fundamental rights to respect for private life and the protection of personal data, making the same argument as that of the letter to the European Commissioner for Home Affairs in 2010. Even though national laws implementing the retention directive might still be in place, such as in Sweden, four of Sweden’s telecommunication companies have stopped storing customer data after the directive was overthrown [10]. Swedish ISP Bahnhof have deleted all records wrongfully retained since inception in 2006. They have also called on other mobiles and Internet service providers to do the same. The data protection directive does not apply to data processed for purely personal reasons or household activities. For example, it is not illegal to maintain an electronic personal diary or a file with details of family and friends.

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However, very stringent rules apply to processing sensitive data, i.e., data that pertains to racial or ethnic origin, political, religious or philosophical beliefs, trade union membership, data concerning health or sexual preference, and so on. In principle, such data cannot be processed yet derogation is tolerated under certain circumstances, such as with explicit consent to process the sensitive data, and the processing of information mandated by employment law. EU privacy legislation makes the following assertions:

• You have the right to be informed of any data processing when you are the data subject. • You have the right to access data about you. • You must also have access to the logic on which automated decisions are based.

Up until the present day, for the Internet and World Wide Web technological features pervasive, the key issues addressed are artificial intelligence, database-related privacy, exchange of records, behavioural analysis through user produced online data, and uncontrolled dissemination of personal information.

2.3.1.2 The General Data Protection Regulation as an enabler of MASS in Europe

In July 2011, the largest survey ever to be conducted regarding European citizens’ behaviours and attitudes concerning identity management, data protection and privacy was concluded [11]. Key findings include:

• More than two thirds of Europeans (67%) do not trust phone companies, mobile phone companies and Internet service providers;

• The majority of Europeans (75%) want to be able to delete personal information on a website whenever they decide to do so.

It is clear that Europeans have little confidence in big companies managing their personal data and would like to have more control over its management. At the beginning of 2012, the European Commission (EC) proposed a comprehensive reform of the current data protection rules. The reform will take shape in as the general data protection regulation (GDPR) and its main aims are

• to strengthen online privacy rights as technologies lead to the era of the Internet of Things (IoT), and

• to boost Europe’s digital economy. The way in which data is collected, accessed and used has profoundly changed due to technological progress and globalisation. This growing globalisation of data flow (via media such as social networks, cloud computing, search engines and location-based services) makes it increasingly likely that people can lose control of their own data. The reform will therefore continue to address the privacy issues of the past but extend its effort by addressing issues about false positives, false negatives, surveillance, personal profiling, function creep, and identity theft, amongst others. Moreover, the current data protection laws are somewhat dated, considering that the iPhone was launched only in 2007, more than a decade after the data protection regulation was put into effect. Now the Internet – and specifically the mobile Internet – is in widespread use, where about a quarter of a billion people use the Internet daily in Europe. The GDPR will ensure effective protection of the fundamental right to data protection and improve certainty as to the law for companies, which is essential to boost Europe’s digital economy. The current national laws that came from individual interpretations of the directives will be replaced with a single new set of rules. Across the EU, companies will be able to move around more easily and citizens’ rights will be enforced equally.

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The divergence in enforcement of the directive of 1995 in the (then 27) member states led to a high degree of fragmentation and costly administrative burdens as well. By introducing a single law, they hope to do away with the current fragmentation and costly administrative burdens. This was expected to lead to savings for businesses of over 2 billion Euros annually. The reform would help reinforce consumer confidence in online services, providing a much-needed boost to growth, jobs and innovation in Europe. The data protection reform will ensure more effective control of people over their personal data, and make it easier for businesses to operate and innovate in the EU’s Single Market. In particular, the issues addressed by the GDPR that are not covered by the Directive of 1995, are as follows.

• User consent must be explicit, affirmative and can be withdrawn – and these sentiments should not be inferred.

• Security of personal data is compulsory, i.e., controllers are bound to adopt the state of the art technical and organizational measures with respect to the nature of the costs and risks.

• Controllers are obliged to communicate any data breaches without any undue delay and within 24 hours where possible.

• Social, technical, business and organizational aspects should be considered for privacy by including stakeholders in the process, as well as the considering the analysis of the entire personal data analysis process and analyse the risk posed.

• There will be a strong accountability or enforcement mechanism through the appointment of supervisory authorities in each member state, where their duties are to handle complaints against non-adherence or provisions of the regulation.

• There will be a union level authority for monitoring the implementation of the regulation.

2.3.1.3 The blocking of MASS reflected by the need for regulation

Vice-President of the European Commission responsible for the Digital Agenda, Neelie Kroes, said in his speech at the IAPP Europe Data Protection Congress in 2013 that,

“… on their own, data protection is not about putting barriers in the way of well-meaning business, or limiting the options of innovators; it is about safeguarding fundamental rights, building trust, and ensuring a system built on fairness, transparency and user control.” [12]

With all the good intentions of the EC, as well as the desire of businesses to operate within the rules set forth by the Commission, updating big systems (into which companies invested large sums of money) to operate within the new rules may be very challenging. Companies are given – what is considered to be – a fair amount of time to make the necessary changes. However, when it comes to companies who deploy huge systems, such as Google does, it is not a simple matter. Moreover, the typical penalties in place were not significant enough to justify the restructuring costs – it was financially more sensible to be fined than to invest in the changes needed. For example, France’s national data protection authority called CNIL fined Google a record amount of 150 000 Euros after Google ignored a three-month ultimatum to bring its practices on positioning and storing user data in line with the French law [13]. The penalties that most EU countries can impose are small compared to say a net profit of 10.7 billion US dollars that Google earned in 2012. Spain can impose fines of a maximum of 1 million Euros; Germany fines up to 300 000 Euros. This means that very big companies, such as the Google, are still not significantly affected by the laws, whereas such severe fines could be the end of smaller business trying to hold on to a small share of the digital market dominated by the likes of Google and Amazon.

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CellVision, a positioning middleware provider doing business with mobile operators in the mass public warning system domain, have explained that they do their best to ensure that user privacy is respected by the operator in terms of location service. However they cannot take responsibility for the operator’s decision to process, share or sell the actual data they obtain. CellVision considers privacy more as an important aspect to be managed, rather than a big obstacle. In fact, interviews with other location middleware providers reflect the same value – that privacy is an important topic to be addressed because companies can face prosecution if they do not oblige by the rules set. But they do not believe it is a big obstacle. Rather, the obstacle seems to be an issue of public participation. Mobile operators are reluctant to risk their reputation for a respective revenue stream that is currently non-existent, as CellVision explained.

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3. The evolution of network-based positioning technologies

3.1 Comparison of positioning methods

3.1.1 Network-based methods Mobile devices equipped with cellular technology are universal. In the USA alone, over 91% of the population own a cellular device [14]. Cellular technology captures signals from all mobile devices in real-time. Consequently, movement insights can be unlimited and results are quickly available - typically within weeks. No additional equipment is needed - beyond the mobile and the cellular network equipped with the necessary location infrastructure. Since the equipment and technologies in different geographical locations, spanning nations and continents, cellular technology captures both residents as well as foreign mobiles. However, although cellular data allows one to detect unique devices inherently, it is not always possible to distinguish the mode of travel of that unique user; neither can it detect details of the travel itself, such as the vehicle class, the intention. To learn such information, addition information about the context would be needed and additional inferences would have to be made. For the sake of exposition, consider Figure 3-1 as an abstract representation of the cellular network. In the network, we have one cell tower (or simply tower) for every cell. Mobile user devices (or simply mobiles) connect to the network by associating and consequently connecting with towers. Mobiles not only transfer user data, such as the voice data while they are calling; they frequently send control signals for network management procedures.

Figure 3-1 An abstract representation of the cellular network

3.1.1.1 Cell-ID

The biggest strength of the Cell-ID method is its simplicity: mobiles communicate with towers that in turn provide a backhaul connection to other networks. Each tower has a unique identifier, called the cell-ID. Whenever any data is communicated between the mobile and the tower, whether control- or user-plane information, the network knows the cell in which the mobile is to be found. In fact, it knows which cell towers the mobile can communicate with thanks to association routines. Triangulation can be used to localise the position relative to three known tower locations.

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This method does not require any network or terminal upgrades, making it economic and immediate to deploy [15]. A Location Area Indicator (LAI) and cell-ID are broadcasted by each cell tower. Mobiles receive this information and thus the associated cell-IDs are known to the mobile. Depending on the cell sizes, the accuracy of a Cell-ID position estimate can vary widely, i.e., from hundreds of metres to several kilometres. The accuracy of Cell-ID can be improved using fading phenomena and predictive techniques. In some cases, complexity does not increase [16] but as for the predictive model by Nypan et al [17], a significant amount of processing overhead can result. By incorporating signal strength measurements from radio stations, a simple algorithm can be derived that computes the position estimate [18]. From an LBS perspective, DiFonzo stresses the five major advantages that the network-based method offers over the satellite-based method, which is typically favoured due to the relatively high expected penetration rate of satellite-enabled smartphones [19]. They are

• Ease of use: To access satellite LBS, users must know how to operate the smartphone or a tablet; they need to know how to download, install, configure and update the application when necessary.

• Timeliness: The latency of the location fix can be somewhat problematic for the satellite-based method. The network-based method relies on a real-time update of the device.

• Reach: Building and locations with skyscrapers and other tall structures typically block or cause significant interference with the satellite signal. Typically, networks have coverage inside buildings.

• Power consumption: Satellite technology drains the battery of mobile devices. The network method leverages the duties the network already performs. No additional power is consumed by the mobile device.

• Security: Mobile devices and applications can be hacked or spoofed. This may lead to inaccurate locations being reported. The cellular network minimizes this since the location is reported by a secure cell tower, rather than the mobile or an application.

In summary, the SWOT analysis of the Cell-ID method in general is as follows.

Strengths Weaknesses • Very fast time to fix • Relatively very low power needed • Highly scalable in that all mobiles

subscribed can be localised • Entirely network-based technology, i.e.,

no technical need to have explicit input from the mobile user

• Relatively very inexpensive due to no device or equipment upgrades

• Uniformity and simplicity privacy, authorisation and billing procedures

• Passive positioning of the mobile is possible, i.e., no need for application on mobile

• There is a strong business case for the tracking application rather than just broadcasting location

• Relatively low accuracy • Some Mobile Network Operators (MNO)

do not implement it • The networking infrastructure is more

complex • Some old handsets from HTC, Motorola,

Blackberry and Samsung are incompatible • No compelling business case developed

by or for mobile operators – even though many trials have been done and are still underway

• Location roaming and geofencing are absent by default

• Even though it offers low accuracy, it is very much monitored by European privacy regulators

Opportunities Threats • Location provision and control as a

differentiating factor for operators • More device-centric location technologies

are coming to market, making the

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• Ability to change from volume-based to revenue-sharing pricing

• Ability to provide almost always-on location-triggered services

• Ability to derive statistical information for mass mobile users

• Ability to provide constant location (in real-time) for contextual analysis with relatively low power requirements

network-centric location more difficult • Google, Nokia and Apple undermine the

business case for network-centric positioning because they offer MNO-independent navigation, routing and location data for free

• European users understand device-based location services, while network assistance is not widely understood

Geofencing is one of the applications that has seen a rapidly accelerated growth in network-centric location. Geofencing requires an update in most location centres and is not available by default. Tracking mobiles is a key network-centric application. Therefore geofencing enables service providers to offer improved quality of service. In the following table, an updated version of the list presented in PTOLEMUS’s Location Study of 2010, we list the mobile operators for each country, indicating the vendor, positioning method used and the positioning middleware provider – where available.

Country Network Vendor Location technology available Middleware

Austria A1

(Mobilkom) Cell-ID

Austria 3 TCS Cell-ID, AG.P.S. Austria T-Mobile Cell-ID

Belgium Belgacom Alcatel Lucent Cell-ID

Belgium Mobistar (Orange)

Ericsson Cell-ID

Belgium BASE (KPN) Cell-ID Bulgaria Mobilkom Cell-ID Croatia T-Mobile Cell-ID

Czech Rep. Telefonica Ericsson Sector-ID or "best BTS server" Reach-U Czech Rep. T-Mobile Sector-ID or "best BTS server" Czech Rep. Vodafone Sector-ID or "best BTS server"

Cyprus Cyta Cell-ID Denmark 3 TCS Cell-ID, A-G.P.S. Denmark Telenor Cell-ID + TA Denmark Telia Sonera Cell-ID + TA Estonia EMT Ericsson Cell-ID Reach-U Finland Elisa Nokia Siemens Cell-ID Finland Telia Sonera Ericsson Cell-ID + NMR Mobilaris France SFR Nokia Siemens Cell-ID France Orange Ericsson A-G.P.S. Mobilaris France Bouygues Cell-ID LocatioNet

Germany Telefonica /

O2 Cell-ID

Germany T-Mobile Ericsson Cell-ID Germany Vodafone Ericsson Cell-ID Greece Vodafone Cell-ID

Hungary Telenor Cell-ID Hungary T-Mobile Ericsson Cell-ID Mobilaris Hungary Vodafone Nokia Siemens Cell-ID Holland KPN Cell-ID + geofencing Holland T-Mobile Cell-ID

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Holland Vodafone Ericsson Cell-ID (AG.P.S. testing) Ireland 3 TCS Cell-ID AG.P.S. Ireland Telefonica Cell-ID Ireland Vodafone Ericsson Cell-ID

Italy Wind Ericsson Cell-ID Italy 3 TCS Cell-ID, AG.P.S. Italy TIM Ericsson e-Cell-ID Italy Vodafone Cell-ID

Latvia Telia Sonera Cell-ID +NMR Lithuania Telia Sonera Ericsson Cell-ID Mobilaris

Luxembourg LUXGSM Cell-ID Malta Vodafone Cell-ID

Norway Telenor Ericsson eCell-ID CellVision Norway Telia Sonera Mobile Art Cell-ID, CAMEL Norway Tele2 Cell-ID CellVision Poland Polkomtel Nokia Siemens Cell-ID +TA Poland Orange Cell-ID +TA Poland T-Mobile Cell-ID +TA

Portugal Optimus Ericsson Cell-ID Genasys Portugal Vodafone Cell-ID +radius Romania Orange Ericsson Cell-ID Reach-U Romania Vodafone Ericsson Cell-ID LocatioNet Slovakia Orange Ericsson Cell-ID Reach-U Slovakia T-Mobile Ericsson Cell-ID Slovakia Telefonica Cell-ID Slovenia Mobitel Cell-ID

Spain 3 TCS Cell-ID AG.P.S. Spain Telefonica Ericsson Cell-ID Genasys Spain Orange Ericsson Cell-ID Spain Telia Sonera Cell-ID Spain Vodafone Schlumberger Cell-ID (testing A-G.P.S.) Genasys Spain Yoigo nothing Spain Euskaltel nothing

Sweden 3 TCS Cell-ID AG.P.S. Sweden Telenor Ericsson NMR CellVision Sweden Telia Sonera Ericsson Cell-ID Mobilaris

UK 3 TCS Cell-ID A-G.P.S. UK Orange Nokia Siemens Cell-ID

UK O2/

Telefonica Cell-ID

UK T-Mobile Ericsson Cell-ID UK Vodafone Ericsson Cell-ID/ eCell-ID on 2,5G

3.1.1.2 Enhanced-Cell-ID (E-Cell-ID, i.e., Cell-ID with TA and NMRs)

Using timing advance (TA) and network management records (NMR) of the mobile network, E-Cell-ID manages to improve the accuracy achieved by the Cell-ID method. The TA mechanism is the round-trip time between the mobile and the tower. It adds the time measured from the start of a radio frame and a data burst to improve the position estimate. The accuracy depends on the size of the cell but it is a slight improvement over the traditonal Cell-ID method. E-Cell-ID is used in roaming and calculating costs.

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3.1.1.3 Enhanced-Observed Time Difference (E-OTD)

Cell towers broadcast ad-hoc messages in each cell. The mobiles receive these messages and compute the relative times of arrival. This allows the mobiles to calculate the distance from each visible cell tower. Trilateration is used to calculate the final position. In order for E-OTD to work, the mobile needs to be upgraded to manage these ad-hoc messages correctly. Synchronised clocks also need to be installed to provide a reference time that is used to calculate the final position of the mobile. The two key components then essential for E-OTD are the

• Real time difference (RTD), i.e., the synchronisation difference between the two reference towers, and

• Geometric time difference (GTD), i.e., the propagation difference between the two reference towers.

In the 1990s, Ericsson positioning system featured the E-OTD method as a part of its cellular positioning technology [20]. In the product review, Swedberg explains how E-OTD can be either network-assisted or handset-assisted, and achieves an accuracy of about 60 metres in rural areas and 200 metres in unfavourable urban areas.

3.1.1.4 Time of Arrival (ToA)

A mobile device sends access bursts to the cell tower. Location measurement units (LMU) installed in the system evaluates differences in arrival times to estimate the distance between the mobile and the tower. Trilateration is used to compute the final position from a set of towers. Since the LMU needs to be installed at each tower, this is an expensive positioning solution. The time of arrival (ToA) positioning method measures the distance of a mobile device with respect to a set of Location Measurement Units (LMUs). An LMU needs to be installed at each cell tower, making this an expensive method. The method calculates the difference between the arrival times of the access burst sent by the mobile device. Since signals travel with a known velocity, the time of arrival can be used to calculate distance. Using trilateration, the network computes the final position of the mobile. Rolling out ToA for an entire network is estimated to cost as much as ten times the price of an E-OTD system. It is also expensive to maintain, is labour intensive and slow to rollout, and provides relatively low accuracy in urban non line-of-sight environments.

3.1.1.5 Uplink-Time Difference of Arrival (U-TDoA)

The time difference of arrival (TDoA) method is different from ToA in that TDoA does not measure the absolute time of arrival at a specific tower. Rather, it uses the time difference between two towers to compute the position estimate. Ideally, the signal is a training sequence of a random access burst but it could also be a normal burst. No modifications are necessary on the mobiles but LMUs are still necessary. The LMUs, located at the towers, receive the bursts to measuure the vakye of the arrival times, The mobile position centre (MPC) then calculates the TDoA by subtracting pairs of ToA values. For this method to work, it is essential that the geographic coordinates of the LMUs are known, as well as the timing offset between them. This can be ensured by usinging absolute GPS time at the LMU, or by using reference measurement terminals to calculate the RTD.

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The position estimate is delivered by the MPC with a measure of uncertainty. The environment affects the accuracy of this method, which varies typically from 50 metres in a rural setting to 150 metres in a undesired urban environment, depending also on the size of the cell and the implementation method. When combined with the assisted-GPS method, it is possible to achieve an error of less than 25 metres. As the case of ToA, the network hardware changes are expensive to rollout and maintain, labour intensive and slow to rollout. Moreover, it provides relatively inaccurate position estimation in urban non line-of-sight and rural environments. U-TDoA has a software-only version, namely observed TDoA (OTDoA). Four towers are required and their time needs to be synchronised. Two hyperbolic loci are calculated using ToA and the intersection of these are used as the position estimate. A major advantage of OTDoA is that it is expected to perform similarly to U-TDoA but without the need for LMUs. This will offer accuracy reaching the range of 15 to 150 metres, depending on the size of the cells and the implementation method, at a much lower cost. Under the 3GPP standardisation organisation, Ericsson, Alcatel Lucent, Huawei, Motorola, Nokia, Nortel, Qualcomm and Samsung have all been involved on the OTDoA standard.

3.1.2 Performance criteria Users and executives from location technology providers, device vendors and operators were asked by PTOLEMUS in December 2009 to rate the main location positioning technologies on a scale from 0 to 5. The survey was conducted with the location-based services business in mind. As shown in Figure 3-2, users felt that the Cell-ID method offered the poorest signal coverage but offered similar speed and signal availability as the other technologies.

Figure 3-2 Main positioning technologies as rated by their users, taken from [21]

These results suggest that when users are relying on LBSs, the type of technology used have an impact on the perceived performance. Differently however, the business of MASS does not always involve performance to be perceived by the mobile user of the LBS.

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Key performance criteria for the business of MASS, i.e., related to network-based positioning, include • Accuracy • Coverage • Reliability • Speed • Cost • Power consumption

Some of these criteria, such as accuracy, are important for the analysis aspect of the business, while others, such as power consumption, are essential to make this a viable business, one in which mobile subscribers and operators would be incline to participate in from a practical standpoint as well.

3.1.2.1 Accuracy

There are five main factors that can impact the consistency and/or accuracy of device locations: • Network technology that produces different types of events. Some technologies only produce the

beginning and end of events, while others produce interim events. • Network density; densely populated areas typically have a heavy network infrastructure

permitting a device to use multiple antennas. In rural areas, it is less likely for a device to use more than one antenna.

• Device activity; the more a device is actively used, the greater the likelihood it will detect network infrastructure availability.

• Network activity; congested networks can load-balance as in peak commute times in a major city.

• Network infrastructure topology; Some geographic areas have restrictions as to where antennas may be placed due to zoning laws. For example, in the San Francisco Bay area, the body of water next to the city creates a scenario where calls may only be serviced by network infrastructure in a single direction.

Technical improvements were made to improve the accuracy of Cell-ID using fading phenomena and/or predictive techniques [16] [17]. The accuracy of the approximate location can range from 10 metres to 5 kilometres, depending on the availability and reliability of each of the input variables. Airsage provides two kinds of points, namely transient points and activity points. For individual events, there is a 90% certainty radius of between 250 and 900 metres. Transient points are those points used to determine the movement of a device, i.e., as a person goes to work from say his home. They provide such transient point accuracy within an average of 300 metres. Activity points are clusters of points that typically represent a stationary device, for example, while a person is in his or her office. Since they are able to refine to get a tighter location fix, for example, by removing outlier data points, they provide location fixes with less than a certainty radius of 100 metres. Overall, as shown in Figure 3-3, even though the basic Cell-ID method can result in large accuracies in error, depending on the type of environment and perhaps combining position estimates from other technologies can give useful estimates with only errors of tens of metres. Ultimately, it does depend on the setting of the use case scenario whether Cell-ID is sufficient. Moreover, considering that in the business of MASS, there will be a large number of samples taken, error in the estimations become less significant by applying some statistical analyses.

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Figure 3-3 Comparison between the accuracies achievable in different environments by various methods (showing satellite, WiFi and Bluetooth as well)

As an example of using crowd-sourced data in 3G networks, GloPos achieves positioning results that are accurate within 10 to 30 metres. Their system is ideal for indoor and metropolitan environments. Recently at the end of 2013, Mozilla has started a new crowd sourcing project where participants can download the MozStumbler application and contribute to the location database. The Mozilla Location Service collects geolocation data from both public WiFi networks and cellular towers. This means that users of this system can access location data without the need for GPS when navigating or trying to locate someone. Figure 3-4 shows the countries with geolocation data collected by this service.

Figure 3-4 A map of the world showing the countries with geolocation data collected by the Mozilla Location Service

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3.1.2.2 Coverage

A device can be localised over a greater geographical space when a network has wider coverage. However, the density of the network deployed affects the quality of the positioning. Combined, mobile networks can achieve a greater coverage. Overlaying these networks would make it seem as though the density would increase as well but this not the case. Typically a subscriber belongs to only a single network. Airsage has nationwide wireless carrier partners in the USA, which expands their reach to include greater coverage and more data points. Consequently, they process over 15 billion data points daily. They turn these data into meaningful actionable information for advertisers, marketers, urban planners, emergency responders, Departments of Traffic and other businesses. A case study of a game at the Gillette Stadium showed that, considering the number of unique devices attending the stadium versus the number of people attending the event, AirSage had a 30% visibility, which is very high in terms of a sample size considering that over 6000 people attended the event. In Figure 3-5, we show the coverage map of Navizon in Europe for all the technologies combined. It is clear that depending not only on the technologies used but also on the density of metropolitan cities, different qualities are obtained for different areas.

Figure 3-5 Coverage map of Navizon in Europe for all technologies used (as of 22 April 2014) In contrast to the coverage map of Navizon in Europe, national mobile operators typically have an almost complete coverage of the entire national geography. For example, consider BASE mobile in Belgium. As shown in Figure 3-6, almost the entire country is covered. Moreover, as reported by Sensorly [22], fairly high quality signal strength is offered throughout the country. Typically, where there is no coverage or rather poor coverage in a country, one would expect that there is not much need for it since it would be a priority on for the operator to provide coverage there. Where GPS and WiFi technologies are not always relied upon for estimating mobile position due to poor coverage, the Cell-ID method is the most practical solution that must be made available by the mobile operator.

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Figure 3-6 Coverage map of BASE mobile Belgium showing the 2G and 3G coverage and signal strength (reported by Sensorly http://www.sensorly.com/, as of 22 April 2014)

Another aspect of coverage is the environment in which the technology operates, since different environments have different channel quality profiles. As already nominated in Figure 3-3, one classification of the environment is whether it is a rural, sub-urban, urban, metropolitan or indoor setting. Each environment gives an idea of the geographical area covered, as well as the density of the deployment of the technology. For example, it is fair to expect that a mobile operator would deploy larger and fewer cells in rural areas, particularly since there are fewer customers, whereas in an indoor environment, such as in a busy shopping centre, there will be greater coverage and cells may be smaller. In August 2012, the In-Location Alliance was founded to drive innovation and market adoption of high-accuracy indoor positioning and related services. Since then there has been a significant effort done by member companies, such as HTC, AT&T, Nokia and Polaris Wireless, to mention only a few.

3.1.2.3 Reliability There are three types of reliability: A location data user needs assurance of the accuracy, timeliness and security of the data provided. The quality of the data points sourced needs to be communicated to make useful conclusions from the analysis done. This quality involves the degree of the accuracy. Airsage provides a certainty radius; Skyhook provides a certified location service. CellInt Traffic Solutions provide accuracy statistics for their data sensor technology. Another type of reliability involves the availability of complete location information. Mobile commerce explains that they include the timestamp of the queried location data point so as to understand when the mobile’s location was last estimated and recorded - in the case that it has been switch off for some time. Some companies, such as Skyhook and Polaris Wireless, develop hybrid technologies. This not only accommodates improved accuracy because of the availability of more accurate calculations using and combining different methods but also provides a location data point for some devices that may not necessarily been accessible using only the basic positioning method.

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3.1.2.4 Speed

An important performance indicator is the time to obtaining the position estimate, called the time-to-fix. Specifically for satellite technologies, the time to the first fix is an important measure because it is well known that this technology takes a relatively long time to obtain the first position estimate. For assisted-GPS, a cold start takes longer than 30 seconds for a cold start and up to 5 seconds for a hot start – much faster than GPS alone. However, for the network-based methods already discussed, the time-to-fix is significantly short enough for it to be considered negligible. This is a key advantage of these methods. Accompanying this advantage, however, is the fact that the accuracy of the estimate is compromised and, in the cases where the accuracy error can be improved upon, either more reference points or more complicated equipment needs to be installed, configure and maintained. Even though the accuracy suffers under higher speed performance, when dealing with the business of mass mobile user network location data, the accuracy is acceptable for many applications, since trends in mobile user behaviour are typically the objective of the studies. Hence, companies like Airsage provides real-time location data in almost every city in the nation. Also, companies with their own reference system such as Skyhook and Navizon manage to provide real time location even though they include satellite and WiFi reference points. They update their reference databases in batches hence the application accesses the latest information in a real time manner. In Figure 3-7, we show how Polaris Wireless have developed their positioning technology to be suited to various applications. As the wireless technologies advanced, they managed to improve both time-to-fix and location accuracy significantly. Particularly for LTE technology, they aim to provide an accuracy of less than 10 metres and a time-to-fix of 1 second in order to support indoor location and location search and mobile marketing services. Additionally, they are focusing on providing an estimate of the altitude of the position as well, which is well suited to an indoor environment, such as indicating on which floor of a shopping mall a device is located.

Figure 3-7 Speed and location accuracy suited to different application scenarios as offered by Polaris Wireless hybrid solution objectives

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3.1.2.5 Cost

In general, the data provisioning for location data is expected to be free to the user – included in the overall connection cost. For example, device vendors like Nokia, OS developers like Google Android and Microsoft Bing, and mobile operators like T-Mobile Germany, offer basic mobile navigation in a way that it is not expected to cost anything to the user. These device vendors, OS developers and mobile operators therefore have to carry the burden of the cost of positioning amongst themselves. When speaking of cost, we are referring to both capital and operating expenses (CAPEX and OPEX) for the types of technology. Some technologies are expensive to deploy in new or existing systems since they require additional hardware to be manufactured and installed, such as the LMUs that need to be installed at each cell tower in the case of he ToA method. The operator incurs these costs. In other cases, hardware needs to be installed in the mobiles themselves, such as installing the GNSS chipset for satellite positioning. In general, the following table summarises whether there is any additional operator investment required, who pays for the position estimate and the type of cost involved (CAPEX or OPEX) for each of the network-centric methods. Cell-ID A-GPS U-TDoA E-OTD E-Cell-ID AoA GloPos

Additional operator

investment YES NO YES YES YES YES NO

Who pays for the location?

3rd party service

provider

Mobile network operator

Mobile network operator

3rd party service

provider

3rd party service

provider

3rd party service

provider

Not applicable

Cost Type OPEX OPEX CAPEX +

OPEX OPEX OPEX OPEX OPEX

Considering the decreasing trend in the price of a single position estimate request, we expect to observe it to fall well below a Eurocent, as shown in Figure 3-8. However, even at one eurocent per request, the billions of data points necessary for the business of mass mobile user network location data analysis requires a rethinking of the charging model to access the location data of such scale.

1

2

3

4

5

6

7

8

9

10

Posi

tion

requ

est p

rice

(Eur

o ce

nts)

Figure 3-8 The typical price per position estimate request in Euro cents

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These prices are influenced by the costs to provide the technology and therefore their decrease is an indication of the market become more mature and initial CAPEX being overcome, as well as the development of location data services that are generating profit to justify the OPEX of location-enabled networks. Solutions that need to be installed on the device, such as GloPos, will compare even more favourably in terms of cost for the device or OS provider, since they are based on an installed software and server license contract. Most mobile operators favour the network-centric solution, since they have control of the technology and its application. Moreover, the operators have fixed annual costs to provide location to as many devices as required, i.e., it is a highly scalable approach to positioning.

3.1.2.6 Power consumption

The problem with location services, such as the Facebook “Nearby location” feature shown in Figure 3-9, is that applications using location technologies on the mobile device drain the battery. However, location relying on Cell-ID consumes far less power than GPS or WiFi because permanent communication with the network is already a part of the cellular network standards.

Figure 3-9 Example of the Facebook “Nearby location” feature Application developers easily overlook power consumption. It is a key performance measure of a positioning technology. Application developers should consider the accuracy needed for their application to function optimally and then decide whether the service provided by their application is sensible to deploy. However, from the standpoint of a business involved in MASS, the additional power drain is negligible – and non-existent for the most part. Just like the user is not aware that the device is being tracked by the network-based method, it will not experience any noticeable additional power drain.

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3.2 How network positioning technologies have evolved over the generations

3.2.1 After-the-fact location for 2G and 3G An important feature of mobile communication systems is to locate the position of a mobile device. This is a key factor for the development of new services. Yet, in 2- and 3G technologies, positioning requirements were not considered in the design phase [23]; separate positioning solutions had to be thought of for these technologies. Positioning has been challenging for telecommunication engineers because network-based positioning did not naturally evolve from the previous generations. As these technologies were developed, the accuracy was improved but not significantly enough to satisfy the performance requirements of the growing location-based service market. Other technologies, such as GPS-aided solutions [24], were providing highly accurate position estimates, e.g., errors of less than 13 metres for 95% of the estimates [25]. But these high-accuracy positioning methods introduced problems of their own:

• Indoor environments and canyon situations, such as locations with high walls and buildings, resulted in significantly less accurate position estimates.

• Poor latency performance plagued the time to obtaining the estimate, i.e., a hot and warm start took from 15 seconds to over 45 seconds, respectively, while a cold start took up to 4 minutes.

• The limited battery available on the mobile device managed to support the positioning feature for only a couple of hours.

The problems of poor indoor coverage and position estimation latency, as well as the high power consumption resulted in the need for GSM-based techniques, even though the accuracy of these techniques would range from tens of metres for picocells to hundreds of metres for microcells in urban environments. In rural settings, the accuracy error could reach several kilometres [26]. Consequently, the first location-based services came to the European market used Cell-ID as the positioning technology. But, most likely due to the lack of the necessary level of accuracy, initially there was very little commercial success. As Enhanced-Cell-ID could increase accuracy to and error ranging 550 metres, and other network-based techniques needing additional hardware were being developed, work continued in terms of improving accuracy, complexity and cost. For example, by adding the Location Measurement Unit (LMU) to every cell tower, the time difference of arrival (TDoA) can be measured and hyperbolic lateration can be done for methods like Enhanced-observed time difference (E-OTD) and uplink-TDOA. However, the high cost for rollout of these methods did not justify the medium accuracy position estimation [27].

3.2.2 Built-in positioning for 4G LTE In the US market, already in 2011 more than 50 percent of the population was covered by 4th Generation (4G) long term evolution (LTE) networks [28]. While LTE enables a wider range of services, better QoS and resource management, users, network operators, service providers and regulatory bodies are constantly demanding more accurate, reliable, faster and environment-agnostic positioning for both existing and new commercial and non-commercial services. Getting positioning information from 2G and 3G was more a kind of reverse-engineering activity. One had to think about what could be used with the technology to get the best possible positioning. With LTE, Ericsson see that positioning was taken into account during the development and standardisation of LTE. This results in some dedicated signalling channels specifically for positioning. For example, in a 2G network, there is a network even – such as making or receiving a call. Differently in LTE you have almost a continuous update of location information. This results in much more real-time location information.

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Positioning performance is enhanced in LTE by addressing the design philosophy behind LTE. The design philosophy behind LTE is characterised by decentralised radio-access network architecture, a minimised number of node levels, and that the positioning architecture should be transparent to the underlying radio network. Moreover, a positioning node is introduced to take on some tasks of LTE positioning functionality. These tasks are

• Decide which positioning method to use. • Build up and provide assistance to facilitate the measurement calculations. • Collect the necessary measurements. • Compute the position. • Communicate the results to the requesting client.

The LTE positioning protocol (LPP) is a point-to-point protocol that can be used in both user- and control-plane. Also, the procedures can be executed in parallel to reduce latency. In the user-plane, the secure user-plane location (SUPL) protocol is used, while the LPP Annex (LPPa) is for control-plane positioning but can also support user-plane positioning. Since there is no single method for good positioning in all environments, LTE offers an integrated solution of methods standardised in the various releases of the standard. The Cell-ID method is the basic method that can be used for the position estimate calculation. In release 9 of LTE, three additional device-assisted methods became available, namely the E-Cell-ID, OTDOA and A-GNSS. Specifically, the E-Cell-ID method, also being network-based, utilises cell IDs, radio frequency (RF) measurements from multiple cells, timing advance, and angle of arrival (AOA) measurements. Release 9 also include known methods that do not require any additional standardisation, namely RF fingerprinting, Adaptive E-Cell-ID (AECID) [29], and hybrid positioning. Hybrid positioning simply refers to the method of combining positioning results obtained from using different methods. Uplink TDoA (UTDoA) is standardised in Release 11 of the standard. It utilises the uplink ToA or TDoA at multiple receivers. As shown in Figure 3-10, the QoS achieved by the additional methods improves both the accuracy and the response time of the positioning with the Cell-ID method as the benchmark.

Figure 3-10 Quality of Service improvement for standalone positioning methods in LTE [28]

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In order to drive technology innovation and also remain synchronised with the evolution of technology standards, companies such as Polaris Wireless participate in location committees and other groups within the 3GPP and Open Mobile Alliance (OMA) standards setting organisations. They have contributed significantly to the location capabilities of 2G and 3G technologies, as well as the emerging 4G LTE. For example, Polaris Wireless has assumed a leadership role as the rapporteur for 3GPP control plane architecture and interfaces definition. Moreover, they are active participants in regulatory and industry bodies related to emergency services (E911), lawful surveillance and LBS. Similarly, Ericsson is actively driving standardisation in 3GPP and OMA groups as well.

3.2.3 Hybrid methods for network positioning As already mentioned, companies like Skyhook and Navizon provide localisation by offering access to their reference system that combines reference points from different positioning technologies. Cellint Traffic Solutions create their reference system that is sold as a software sensor system. Initially, their reference database is trained using satellite positioning and control signalling information from cellular networks. The resulting sensors are therefore a hybrid technology that has learnt from satellite positioning and cellular location. Polaris Wireless has patented their Wireless Location Signatures (WLS) technology. As a part of their hybrid solution of geolocation methods for comprehensive application needs, it combines software-only, indoor and urban ability of radio frequency pattern matching with precise A-GPS. It provides excellent positioning for a number of applications; including emergency call services, lawful location surveillance and commercial LBS. WLS can provide a reliability of a 100 percent location yield and a very low time to fix. Even non line-of-sight conditions are well localised. It works well across the various 2G, 3G and 4G LTE telecommunication standard-based technologies, offering a better price per performance, is faster and easier to deploy and offers advanced system maintenance tools and methods, compared to the traditional methods like Cell-ID, Cell-ID with timing, E-Cell-ID and TDoA. Their hybrid solution also manages to overcome some shortcomings of A-GPS. It enables high accuracy and location yield in all environments – even indoors – and immediately covers all customers – regardless of handset capability.

3.3 The 5G outlook for positioning technologies

Even though it is still too early to give a clear definition to what 5G positioning entails, as CellVision emphasis, some companies like Ericsson have a clear direction they expect and are encouraging for this technology. Ericsson see networks evolving to a dedicated positioning ecosystem, where heterogeneous systems and techniques are applied to provide seamless and high quality localisation, regardless of the device type or services supported by the various networks, such as Voice over LTE (VoLTE). Moreover, more small cell networks are expected, since the smaller the cell, the greater the accuracy achievable. This means that, by the time 5G heterogeneous networks of small cells are a reality, combining WiFi on the application level would not introduce much difficulty. So, similarly to Ericsson’s Mobile Positioning System (MPS), in 5G localisation systems, everything will be hidden by the network technology, effectively simplifying the interfacing for the applications. A big advantage of having such a platform – like MPS – that aligns the various network technologies for positioning, new technologies can be integrated to be supported by the platform as well.

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But for some, like the positioning middleware provider, the cell size does not seem to make a huge difference technically but it does allow for more accurate positioning performance since the cells are smaller in geographical area. However, smaller cells make the job of the network planner and mobile operator more challenging. Regarding the types of devices supported, the advances in machine-to-machine type of communication and the IoT are promising a highly connected future. Since devices will be able to communicate over large networks, the underlying positioning technologies will vary greatly but have to communicate seamlessly and efficiently. Jasper Wireless already allows companies such as Coca-Cola to register their devices for asset tracking purposes, making data communication possible. They explain that even though they are not responsible for localisation itself, they can communicate such information if it were to be calculated separately. Nissan, for example, delivers a wide range of premium telematics services to drivers, which include 24/7 emergency support for accidents and stolen vehicle tracking, using Jasper Wireless’s machine-to-machine mobility platform. These examples, amongst many others, demonstrate that there is a strong move towards a large scale IoT communication in which localisation is sure to play a crucial role. Polaris Wireless intends to maintain strong active relationships with standardisation bodies in order to have a technology that supports all developments, in whichever 5G wireless technology heads.

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4. Value propagation and strategic business forces

4.1 The cellular-enabled MASS location value chain

We have identified five key steps that bring added value through the MASS business. Key stakeholders interact in each step to create and propagate value to reach the data customer. As shown in Figure 4–1, there are six key stakeholders, including the data customer (or consumer) in this business. They are the

• location technology provider, • network infrastructure provider, • mobile cellular network operator, • location data aggregator, • data analyst, and • data customer.

Figure 4-1 Value propagation through the MASS value chain

The figure shows the key responsibilities of the stakeholders and their immediate dependence on each other, where it should be clear that all stakeholders are necessary for the business. Of course then it is possible for a specific company (such as Positium LBS also shown in the figure) to participate in this business as more than one stakeholder.

4.1.1 Location technology provider The location technology provider does not have to deviate from their usual business routine when manufacturing specifically for the MASS business. Of course, enabling devices to provide information that results in more accurate location estimates being calculated, without significantly complicating the technologies used or significantly increasing the power consumption of the devices, is desired for all businesses involving location estimation. Examples of a location technology provider is Broadcom and Andrew’s Commscope.

4.1.2 Network infrastructure provider Network infrastructure providers piece together technologies (both of hardware and software nature), integrating location technology (amongst others) to support communication that enables location services. Much has already been done for the MASS business, and advancements seem to be in the direction of

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making infrastructure more interoperable and customizable for the customer. Examples include Ascom, Ericsson, Alcatel-Lucent in partnership with Polaris Wireless, Intersec, and Cellint Traffic Solutions.

4.1.3 Mobile cellular network operator Mobile cellular network operators make huge revenues. Relatively, the revenues generated by projects related to the business of mass mobile user network location data is insignificant, as Mobile Commerce of the UK point out. In fact, CellVision, who deal mostly (but not exclusively) with MASS-related projects in Nordic markets, point out that operators are making practically nothing at present. They envision that there will definitely be some growth but that it will never result in even as little as 10% of their total revenues. Moreover, just like CellVision, Intersec have explained that it take initiative and drive from them to encourage operators to become more involved. It should come as no surprise then that location providers have been established. They do not only focus on being Cell-ID location providers but they offer hybrid solutions, often focusing products on target areas, such as the indoor environment. They typically combine other technologies, such as GPS and WiFi technology, to provide more accurate positioning with greater coverage and availability. For example, the hybrid technology of Sensewhere (formerly Satis Ltd.) delivers indoor position estimates with less than 10 metres of error. Mobile operators have the opportunity to capitalise on this untapped resource by investing in the MASS business but their fail to see a viable business case. Examples of mobile cellular network operators are SFR France, Belgacom Belgium, and Vodafone.

4.1.4 Location data aggregator Location data aggregators are responsible to collect location data and find a way to package it as a sellable unit. However, there are huge legal and reputation risks that have been mentioned earlier. It seems like the typical model is that the data aggregators of today have to seek out opportunity and encourage bigger stakeholders, such as mobile operators to participate in certain projects. The data or results may become the property of the operator then, relieving the data aggregator of the legal risk. For example, Airsage has network interfaces with Motorola, Samsung, Lucent and Nortel. Combined, these companies constitute a significant portion of the global mobile infrastructure equipment. These equipment providers have proprietary data formats and restricted access. Knowing what to ask for and how to extract the data is a unique strength that Airsage uses to provide its customers with the best and latest technology insights. Other location data aggregators, such as Polaris Wireless and Positium, offer platform and partner with infrastructure providers (amongst other types of customers) to participate in the MASS business.

4.1.5 Data analyst Data analysts are no different from any other companies that determine trends and other insights from large amounts of data. Sometimes the mobile operator, such as SFR, do their own analysis, while, at other times, the data aggregator does it, e.g., Cellint Traffic Solutions. Larger market research and consulting firms, such as Experian, have made it clear that much more value can be achieved if such location data would be more easily available.

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4.1.6 Data customer / consumer Finally, the data customer (or consumer) is the company or individual who can use the trends identified and improve their business offering. Key issues that currently make it difficult to assess the exact impact the MASS business would have in Europe, are the cost and scale of these projects. Since the data is not readily available and the prices is rather higher than what a small company can be expected to pay, the business cannot be put to the test in the way it could be ideally envisioned. Examples of data customers are national tourism agencies, public road planning and construction services, and supermarket chains, such as Carrefour.

4.2 Power and control in the value chain

The network operator is crucial to the take-off of the MASS business because they have the subscribers and own the technologies that make the collection of mass mobile user network location data. Therefore, if operators are not part of the take-off initiative, it will be a major challenge. In fact, as we see today, some companies such as Skyhook collect information from different sources - one type of information being Cell-ID based. Companies that collaborate with mobile network operators typically have to motivate projects by their own initiative because the operators do not see any financial incentives otherwise. As already mentioned, it is evident that there is much desire for the MASS business to take off by companies like Intersec, because they see the opportunity in Europe. However, they have explained that they are typically expected to make the additional effort of proposing and managing projects for operators because they are very reluctant to take the initiative.

4.3 Expected trends and strategic moves A general trend seems that partnerships need to be formed – if it is not one company taking on the responsibility of more than one stakeholder in the value chain. Figure 4-2 shows AirSage’s wireless signal extraction (WiSE) technology platform. There is a clear strong dependence on the wireless network operators who have their signalling data anonymised and aggregated so that it can be processed by AirSage’s WiSE analytic platform.

Figure 4-2 AirSage’s wireless signal extraction (WiSE) technology platform2

2 Source: AirSage. Online: http://www.airsage.com/Technology/How-it-works/, retrieved 2/04/2014.

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5. Conclusions and recommendations Sizing the market potential of the MASS business in Europe is challenging since it is still in a nascent phase. Moreover, understanding which changes need to be made and apply these changes will make it possible to give a fair and accurate view of the business prospects and opportunities of the business of MASS. Considering the European countries in isolations from the rest of the world, there are still many cultural differences that result in significant different business habits. This means that it is not only very early (too early) to expect all stakeholders to participate actively and aggressively, but there is also no general understanding of at least a basic shared vision of what the MASS business should entail so that it could be acceptable to all the participating EU countries. It is clear that the driving forces currently behind the business of MASS in Europe are the European companies that actively seek opportunity and propose partnerships to drive and perhaps create some momentum for other stakeholders to participate. If only more stakeholders were to follow their lead Europe could reach a point at which the business case will be clear and so exponential growth of this business can be expected. The same business in the USA is also still very young but much more progress has, and is, being made. They set a good example and provide a clear proof of concept for European companies who rather stubbornly remain sceptical and unconvinced of the potential success they could be a part of in Europe, and eventually, the world. An initial best practise framework should be the first step towards identifying the obstacles and making sure that there is in fact a practical business case for the MASS business throughout the whole of Europe. Our recommendations are the starting point for a best practice framework for the MASS business is as follows.

5.1 Recommendation to mobile operators

Even though current mobile operator revenues far exceed the initial revenues from pilot projects of the MASS business, these revenues are still significant. Operators are reluctant to invest but it seems as though there is much support and knowledge available from other enthusiastic stakeholders. Therefore, mobile operators are encouraged to participate. In the long terms, it is fair to expect much greater returns as there will be very little additional investments required to maintain the infrastructure and, as the business matures, more customers and applications will be discovered.

5.2 Recommendations to governments and data protection authorities

Most stakeholders are seemingly and understandably reluctant to participate in the MASS business. The first key reason in that there are directives and regulations in order that make participation costly and, especially in the case of a fault by a stakeholder, there are significant penalties. The second reason is that, in the case of the fault, the participating companies’ reputations are at stake, which is bad for their key business. By allowing the MASS business to take place in Europe, governments and data protection authorities need to assume greater responsibility. If they decide the rules of engagement in the MASS business, they should put the mechanisms in place a priori to any company’s participation in this business, without compromising the business opportunity.

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We recommend that the governments and data protection authorities take the lead in forming an association that does not necessarily regulate the MASS business completely, but sets guidelines that aim to gradually ease stakeholders into the business. This set of guidelines would make up the actual best practices of the business.

5.3 Recommendation to companies in the location value chain

Prices for location data requests should decrease. In the UK, even though a few Euro cents does not seem expensive for a single query, the business of MASS implies millions, if not billions, of requests required, which result in the cost to obtain the data to be too expensive. This recommendation is primarily for the mobile operator but is also for data aggregation companies who derive a variety of sources, including cellular networks. MASS customers should be incentivised to participate. Many mobile operators can already provide the data needed for the MASS business. However, there are not many companies coming to them with viable projects. It is the responsibility of the MASS data customer to voice the need so that they can provide the data. However, as is already done by some participants in the business, stakeholders should actively encourage and collaborate with each other. Finally, we agree that there is much risk for companies in terms of their reputation and legal implications that can lead to significant penalties in case of error. However, companies need to actively demonstrate their interest and could drive a working group that collectively defines the business modus operandi. In this way, they can also negotiate and more directly affect legal frameworks to ensure that they are just as protected as the European citizens. Sharing the responsibility (and the financial burdens that come along with it) is much less when approached collectively. Just as in the business of MASS, there is great value in numbers.

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