E X P L O R A T I O N O F R E L A T I V E LOCALIZATION U S I N G W I R E L E S S

F I D E L I T Y T E C H N O L O G I E S

ZAKARIA BOUGUETTAYA U4521530@ANU.EDU.AU

COMP3740: PROJECT WORK IN COMPUTING

SUPERVISORS: MICHAL SYMANZSKI: MICOB MD

CHRIS JOHNSON: SCHOOL OF COMPUTER SCIENCE, ANU

Abstract

The ubiquitous uptake and reliance on positioning systems highlights the importance of the ability to track objects or people. The two current and popular methods of geo-location based awareness involve triangulation using either satellites or mobile phone towers. While both methods are proven, there are several environments in which they cannot be used reliably. This creates the need for systems that address localization concerns in environments where conventional methods yield unreliable results.

This paper will explore the concept of location-based awareness by using existing Wireless Fidelity (Wi-Fi) networks. Through Wi-Fi, one may be able to track objects or people in real time, whilst adapting to changes in both the environment, and the Wi-Fi network, in a reliable, and accurate manner. There are many existing methods of using Wi-Fi to track objects or people, but this paper will focus on enhancements to existing methods by allowing for dynamic and decentralized methods of achieving location awareness by providing a positioning system that is relative to zones, rather than an absolute coordinate-based position.

1 . I N T R O D U C T I O N

There have been several studies into using wireless access points to provide an accurate and reliable system with which to track objects (Eduardo, 2010). The ubiquity of wireless systems along with the rise in the need for portability provides a natural avenue from which geo-location can be deployed across existing wireless systems. To explore the various methods, and to ensure that the lowest common environmental denominator was considered, a hospital environment was selected as the test setting. As with many businesses and workplaces, there is a need to track people, consumables, and equipment in a hospital environment. In particular, hospitals require that some equipment (which could be anything from printers to medicine cabinets) be tracked, such as to avoid theft. Hospitals also require patients to be tracked, to be informed if a patient is in an area that they are not meant to be in, or have left an area they are not meant to have left. A hospital environment offered many challenges, such as increased interference from hospital equipment. This ensured that an algorithm that considered interference would be tested in a rigorous manner. Furthermore, there are several limiting factors to ensure that the system had to be functional, cheap, and reliable.

Tracking objects in a hospital environment was an interesting test bed environment. The tracking of objects was to occur indoors, which meant that conventional methods such as GPS could not be used. Furthermore, hospitals are often large, and as such are made of material that inhibit far-reaching Wi-Fi signals. This is accentuated when considering that hospital equipment can emit on frequencies, which can interfere with an existing Wi-Fi network. Coupled with the fact that hospitals often house a lot of people, there is also interference from people moving and furniture for the people. Taking into account the difficulty surrounding Wi-Fi propagation, and the requirements of tracking patients or consumables, it was noted in starting the research that providing locations relative to zones, rather than relative to an item’s absolute location would provide sufficient reliability

to meet the tracking goals. In this manner, the gains of Wi-Fi triangulation could be leveraged, without sacrificing the inherent advantages of price and scalability, and bypassing the disadvantages of the required high maintenance.

In an attempt to review a number of available options, several forms of wireless positioning systems were considered. Namely, these systems were the Global Positioning System (GPS), radio-frequency identification (RFID) tags, and Wi-Fi received signal strength indicator analysis (RSSI) and triangulation. The immediate concerns with GPS were that while highly accurate, GPS reliability was a concern when considering that the majority of the tracking systems were to be deployed indoors. A GPS requires a direct line of sight to the satellites in the sky, to provide reference points for triangulation. The radio frequencies on which a GPS operates are noted for having poor penetration rates inside buildings (Bryant, 2005). This rendered the use of GPS to track objects or people inside a hospital prohibitively unreliable. The next method that was considered was the RFID tag method. This method involved placing RFID readers at certain points of interest, where RFID tagged items were detected as they passed by the points. The RFID tags are generally passive, require no external power source, and are small in size, making it inexpensive and easy to deploy. The downside was the range at which the tags could be read. Passive RFID systems require that the tags be in close proximity to the readers. This further requires that a large number of RFID readers be used if consumables or people are to be tracked across an expansive area. The costs could be also be substantially increased depending on the amount of maintenance required. The maintenance involves setting up various RFID readers in different points of interest, and supporting services to ensure that new RFID readers are installed when there are changes to the environment, and to occasionally check that the everyone of the installed RFID readers are still working.

The uncertainty in Wi-Fi triangulation by using RSSI to predict wireless propagation has barred any form of reliable system. The wireless frequencies used in Wi-Fi are affected by factors such as the type of material used in the buildings, the number of people in a building, interference from other equipment emitting at similar frequencies, the orientation of the receiving devices, and even the humidity of the environment (Musaloiu-E, 2008). In recent years however, numerous studies have been conducted on a concept dubbed fingerprinting. This process involves a sampling of an environment to obtain the nature of existing wireless propagation with known interferences, and using the output strength vectors to dynamically provide a location relative to historically collected fingerprints by using Euclidean distance formulas. As a result, this method provides a reliable use of wireless access points to derive an object’s absolute position. While accurate, this method requires a continual maintenance of sampled areas to cater for any environmental change, or change in the number of wireless access points. An algorithm that leveraged the advantages of fingerprinting, and overcame the inherent disadvantages, could make the use of Wi-Fi location based awareness more ubiquitous.

1 . 1 REPORT STRUCTURE

The report provides a background into the various location-based technologies, followed by an analysis of possible improvements. The algorithm is then introduced and explained, followed by a test scenario, alongside the results of the test. The test results are analyzed, and possible improvements are identified, if extensions on this work are conducted at a later stage.

1 .2 BACKGROUND

Depending on the specific needs of the environment, there are many different methods for geo-location that leverage existing Wi-Fi infrastructures.

1.2.1 Nearest Sensor

At its core, the most basic of methods is the nearest sensor method (Wexler, 2006). The strongest RSSI of a wireless access point is assessed against a database where the maximum ranges of a variety of wireless access points are stored. RSSI denotes a ratio of a measurement of the power present in a received radio signal, and generally range from -20 to -100 dBm. To put it differently, an RSSI measurement is an indication of the power level received by the antenna, where the larger number is an indication of a closer originating source (Wexler, 2006). As the maximum range indicates a circular perimeter due to the circular nature of propagation of Wi-Fi (Wexler, 2006), the receiving device is determined to be somewhere within the circular unit perimeter. The immediate advantage of such a method is the ease of implementation, and the relative lack of maintenance. While this method offers some advantages, it has a high margin of error, and lacks accuracy (Wexler, 2006). Most common wireless access points have a maximum range of 100 meters (Wexler, 2006), creating an approximate area range of 10,000m2. This range of accuracy is unlikely to be of use in an indoor environment, and there are more reliable methods of geo-location outdoors. Furthermore, due to the nature of Wi-Fi propagation, signals have been observed to piggyback on interference, effectively extending the maximum range of a wireless access point, creating the possibility that results may be misconstrued.

1.2.2 Triangulation/Trilateration

An extension of the Nearest Sensor method, this technique explores the use of multiple wireless access points to perform a triangulation of RSSI vectors. By using the signal strengths as distances, the angles of the RSSI vectors are derived. Using the angles, a common overlapping area is calculated to produce an area in which the receiving device can be found. This process results in a much more accurate method of geo-location than the Nearest Sensor method. Several studies conducted on the accuracy of the Wi-Fi triangulation method revealed an average accuracy of approximately 50 meters (Eduardo, 2010). While a 50-meter accuracy can often be acceptable in an indoor environment, the accuracy can vary wildly depending on attenuation and environmental factors. It is not uncommon for Wi-Fi signals to vary by 50 dBm depending on the amount of interference present (Grossmann, 2007). As such, while accurate, in certain conditions, the Triangulation method is not reliable due to the unpredictable nature of Wi-Fi signal propagation.

1.2.3 Fingerprinting

A more recent and sophisticated method of using Wi-Fi triangulation is the concept of fingerprinting. At the base level, Wi-Fi signals are prone to interference (Figure 1), making it difficult to provide a consistent platform from which to perform procedures like Triangulation or Nearest Sensor. Wi-Fi signals can fluctuate wildly depending on humidity, positioning of the receiving device, environmental obtrusions, and wireless interference (Bryant, 2005). To combat this singularity, the fingerprinting method leverages probability and statistical analysis to build a radio map of a building,

where several “fingerprints” are obtained in two stages. A fingerprint is commonly defined as a collection of wireless beacons with an associated signal strength and location. A beacon is a single signal containing a wireless access point’s Basic Service Set Identification (BSSID) that is often sent every second by wireless access points, and can be used to uniquely identify the access point. The two stages of fingerprinting are an offline phase, followed by an online phase.

The offline phase allows for an analysis of the environment by collecting fingerprints at predetermined location intervals. Several concurrent sets of fingerprints are collected for each location interval at varying angles to ensure that an average is available for each location, to counter fluctuations in Wi-Fi signal strength. The online phase provides a method to compare a fingerprint against a database of known locations and correlated fingerprints, to obtain a probable location.

Figure 1: Varying signal strength for a single wireless access point at single location.

1.2.4 Fingerprinting analysis

There are many different forms of analysis that have been researched to provide mechanisms to increase the reliability of fingerprinting. A majority of the studies have been limited to the algorithms in the comparisons of fingerprints in the online phase against the database of fingerprints collected in the offline phase. While there are several studies available to analyze, the focus of this paper will be to discuss the backgrounds, advantages and disadvantages of the Nearest Neighbor approach.

1.2.4.1 Nearest Neighbours

The Nearest Neighbour procedure compares the signal strength of beacons from the same wireless access points at a particular location against all of the historical fingerprints collected for that location. The smallest numerical result is determined to be the closest neighbour. Considering the signal strengths as vectors, the Euclidean distance formula (Equation 1) is applied to current fingerprint against the database of historical fingerprints to find likelihood of closest N number of wireless access points. An example scenario involves the detection of a fingerprint at a real-time point p. The Euclidean distance between point p and all points collected in the offline phase are collected. The smaller the “distance”, the closer point p is to the compared points. A subset of the closest points are selected, and based on known positions as collected in the offline phase, triangulation is used to obtain a location area.

0  2  4  6  8  10  12  

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Count  

Signal  Strength  (dBm)  

The performance of the Nearest Neighbors method of Wi-Fi fingerprinting varies depending on the environment. The number of wireless access points and their distance apart, and the accuracy of the data collected during the offline phase all affect the accuracy of the results. While there are several bounding factors, the accuracy was anywhere between 1.3 meters up to 6.3 meters in a recent study conducted at the Hong Kong Polytechnic University (Qingjun, 2005).

Equation 1: Euclidean Distance where i is a unique wireless access point, si is the current fingerprint signal strength, and Si is the fingerprint signal strength of the same parent wireless access point stored in the collection database.

2 . “ R E L A T I V E ” F I N G E R P R I N T I N G RESEARCH

2 . 1 OUTLINE

As explored in the analysis of the fingerprinting model, while accuracy of such systems can be highly precise, there are inherent dependencies that can render the model unreliable or inaccurate. Namely, the accuracy of the results directly depends on the quality of the data collection in the offline phase, and the changes to the environment or number of Wi-Fi access points. A change in either requires a renewed calibration and a time consuming recollection of data.

After consulting hospital environment specialists, it was concluded that tracking mechanisms were required to notify relevant parties if something or someone had moved into areas they were not supposed to be in. An example scenario provided was the need to track patients with Alzheimer’s. Alzheimer’s patient often forgot that they were admitted, and in their confusion, attempt to leave the hospital ward. If informed in a timely fashion, hospital nurses would be able to know that the patient had attempted to leave the ward, and guide them back to their rooms. As a consequence, it was just as effective to allow hospital officials to know of their absence, as it was to know the patients absolute location. Put differently, the location of an object relative to an anchoring point offered the ability to track patients as required, while circumventing the dependencies present in the Nearest Neighbor algorithm.

The closest study to the research conducted into the zoning algorithm is one conducted at the California Polytechnical State University (Eduardo, 2010). The study explores the advantages and disadvantages to triangulation and fingerprinting. This study differs in that it extends on the concept of fingerprinting, and attempts to overcome the high maintenance needs of calibration by dynamically regulating changes in the environment, at the slight cost of accuracy.

2 .2 PROJECT DESIGN

To ensure the reliability and pricing of the hardware, a host device where all the calculations would occur was considered. This provided a decentralized method of geo-location, with little need for maintenance, and small costs in the initial set up. An Android handset was selected to be the host device on which the tracking would occur due to the ubiquity of the devices, and the integration of all the necessary hardware. Android devices are also relatively cheap, with a variety of sizing options, with devices as small as a watch.

The wireless access points employed were of the 802.11g standard, at a 2.4 GHz range. While the type of the router could cause some discrepancies, the algorithm was to be decentralized, and had to allow for integration of Wi-Fi systems other than the ones tested.

The specific nature of hardware employed was as follows: • Samsung Galaxy S II i9100 handset with Android 4.0.3 operating system • 4 Linksys WAP54G 802.11g 2.4GHz routers

The environment test bed was an office with a 100-meter by 80-meter floor, with 6 rooms, and

concrete walls (Figure 2).

Figure 2: Test Environment Overlay with Known Interferences

2 .3 IMPLEMENTATION/TESTING/RESULTS

To help narrow the different scenarios in which the concepts of relative positioning could be leveraged, two distinct possibilities were noted. The two possibilities were that the tracked device was definitely or possibly in a zone, or that the device was not in the same zone. To keep the concept as decentralized as possible; a zone was defined as an imprecise area no smaller than 15m2 and no larger than 40m2. This way, the same concept can be leveraged to the specific zoning needs of an environment. The boundaries of a zone were arbitrarily derived, on the assumption that on average, a room is no smaller than 15m2 and no larger than 40m2.

To provide an overview of the functionality, a summary scenario is as follows:

• Upon initializing the device, a user would be prompted to capture an anchor fingerprint, from which all future fingerprints would be compared against.

o The anchor fingerprint determines the fingerprint of the zone, and provides a singular reference point.

o The anchor fingerprint is a quick successive set of 5 scans, to provide an average RSSI value for each unique beacon. To improve the accuracy of the fingerprint, the user is prompted to turn around in a 360-degree turn as the device captures the fingerprints.

• Once the program captures an anchor fingerprint, the user is provided an option to start the tracking.

• Every 10 seconds, the device captures 7 successive fingerprints to compare to the anchoring fingerprint.

o The fingerprint is assessed against the anchor fingerprint, and a percentage likelihood of the device’s zone location is provided.

o If the percentage falls below 10%, the device must be recalibrated. • Every 24 hours, the device automatically captures a new anchoring fingerprint, to

account for any changes in the environment or number of access points o This occurs only if the device percentage has not fallen below 10%.

The algorithm was separated into two possible execution threads. The easier of the two aforementioned possibilities was an ability to deduce if a device was no longer in the anchor zone. The device compared all of the beacons of collected fingerprints against the beacons of the anchoring fingerprint. If there were no beacons that shared a common BSSID, then the device was determined to have a 0% likelihood of being in the same anchoring zone (Figure 3).

The second execution thread contained two exits, with each exit signifying a different scenario. As with the previous thread, the algorithm first compared all beacons in the collected and anchoring fingerprints against one another. If less than 50% of the beacons from both fingerprints share a common BSSID, then the algorithm exits determines that the device has a 50% likelihood of being in the same anchoring zone. If it is determined that more than 50% of the beacons share common BSSID’s, the algorithm calculates the Euclidean distance between each beacon with the same originating BSSID. The distances of each unique BSSID are then put through a standardizing

function, where they are mapped on a z-score scale. The mean value of all the distances of all the same BSSID is calculated, and mapped on the z-score scale. A mapping of a probability is then applied. 0 percent signifies a z-score of anything greater than 3 or less than -3, and 100% signifies a z-score between .5 and -.5. The percentage is an indication of the likelihood that the device is in the same anchoring zone.

An important aspect of any algorithm is the speed and resources required. This is especially true when an algorithm is to run on a handset. Handsets are engineered with portability in mind, generally at the expense of processing power and memory capacity. To speed up the algorithm, and to increase the accuracy of the results, all fingerprints are stored for future reference, alongside the calculated probability. This allows for a quick literal comparison of fingerprints, to assess if the fingerprint is one that was collected before. Furthermore, the algorithm was written to ensure in a manner that ensured the worse case performance did not exceed O (n2) in terms of execution time. (Figure 3). The numbers of fingerprints are directly dependant on the number of unique BSSID’s present in the environment, and will typically be less than 10.

To test the algorithm, a trial environment was employed (Figure 2). 4 wireless access points were placed in four corners of the test environment. The handset running the algorithm was initialized once every day at random times, to factor in the natural changes in humidity during the day. It should be noted that the changed in humidity were estimated to be almost negligible, but that it added a minimal overhead to test. The handset was also initialized in two different zones, to test for environmental differences between zones. The handset was then moved within a 40-meter radius (the maximum range of a zone), at 10-meter intervals. As the device was moved from one interval to the next, it was rotated such that it completed a full circle. This insured that interference from the experimenter’s own body was accounted for. The percentage the algorithm produced was recorded at each interval. The algorithm was then tested at 10-meter intervals outside the maximum confines of each zone, to test the algorithm’s efficiency when outside a zone. The results were averaged for each interval, and mapped out (Figure 4).

The results were indicative of a high success rate. As can be seen in Figure 4, the majority of the averaged interval percentages were accurate in predicting the likelihood of being in a zone. These tests results could be made more reliable, as each interval was only tested once. While each test collected 10 successive fingerprints to average (Figure 3), they were taken in the same timeframe, which could cause some skewed results. Furthermore, the test room was only available for an hour at a time, and as such, the two different zones were tested over a two-day period. While there were some discrepancies in the results, the general trends indicated that the system would be able to reliably predict the probability that a device was in the same zone as the zone in which the anchoring fingerprint was taken.

In order to establish the relationship between the distance away from an access point, and the signal strength of the received beacon, a measurement was taken at 3 meters intervals, and plotted on a scatter graph. As can be seen in Figure 5, the relationship between distance and beacon signal strength seems to be logarithmic. To compare, the ideal linear relationship was also plotted. A linear relationship implies that there is a directly proportional relationship between the distances away from an access point to the signal strength received. This relationship, while ideal, can be far away from the truth due to variances in interference. In applying the two different lines of best fit, the R-Square

value was also derived to calculate the percentage to which the line addressed variations. The R-Square for the linear line was 71%, whereas the logarithmic line produced an accuracy of 84%. While interesting, these figures were not used in the algorithm. These figures are presented to further validate that the strength of a beacon can have a semi-predictable relationship with the distance away from the originating source.

1. Anchoring fingerprint taken [O(1)]

2. Capture fingerprint to compare [O(1)]

3. Compare BSSID’s against each other from both fingerprints [O(n2)]

a. If no BSSID’s are common, exit with 0% likelihood

b. If less than 50% of BSSID’s are common, exit with % of common BSSID’s

c. If more than 50% of BSSID’s are common, continue

4. Calculate Euclidean distance between beacons with the same BSSID [O(n)]

5. Standardize all distances on z-score scale (-3 to 3) [O(n)]

6. The expected value is compared against the average of all beacons. [O(1)]

a. The score is mapped to a percentage

b. Exit with % where 0% is a z-score of anything greater than 3, or less than -3. 100% is anything between -0.5 and 0.5.

Figure 3: Algorithm

Figure 4: Average Interval Results Map

Figure 5: Distance Away From Access Point Vs Average Signal Strength

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Averaged  Signal  Strength  Points  

Ideal  [y  =  -­‐0.9117x  -­‐  25.363]  

Line  of  best  Git  [y  =  -­‐31.61ln(x)  +  50.437]  

2 .4 DISCUSSION/FUTURE IMPROVEMENTS

While the results indicated a high level of accuracy, there were many dependencies and shortcomings that the algorithm encompassed. Perhaps the largest dependency present in the zoning algorithm was the reliance on having more than 3 routers. Ordinarily, this shortcoming would not be too big a dependency, as a large indoor environment requires several routers to be able to provide uniform Wi-Fi access. Recently however, many wireless systems are now deployed using wireless extenders. The zoning algorithm relies on being able to identify one beacon from another by identifying the parent BSSID. Wireless extenders capture a wireless signal, and strengthen it before relaying it. The beacon is unchanged, and retains its parent BSSID. In this manner, the same beacon is propagated far beyond what the algorithm assumes is possible, making it impossible to deduce the probability of being in a zone. As there is no simple way to determine if a wireless extender has strengthened a beacon, this effectively renders the algorithm useless.

Another dependency is the frequency of re-calculating an anchoring point. As it stands, the algorithm currently relies on an external party to reset the anchoring point. While this allows for a timely and flexible manner to cater for changes to the number of access points, or environmental variations, it relies on the explicit interaction of a knowledgeable end-user. This problem is accentuated if there are temporary changes to an environment or number of access points, as it could create a large maintenance overhead. To combat this, a possible extension of the algorithm could be to incorporate alternate Android technologies. Most Android handsets are also equipped with accelerometers, which allow for movement detection. Using this, the algorithm could be re-written to incorporate an input from the device accelerometer to prompt a re-calculation of an anchoring point. The accelerometer can be further utilized to validate the zoning percentage calculation. If the algorithm calculates that it is likely that the device has moved zones, this could be correlated against the accelerometer to check if there has been any detected movement. To further increase the accuracy of the anchoring point, and subsequent scans, the algorithm can factor in the type of the access point before committing it to be compared. As it stands, there are two main types of access points- an ad-hoc and a “fixed” type. The ad-hoc network type is typically used by mobile phones to connect to other mobile phones directly, without an access point. The algorithm is based on the assumption that most access points don’t move, and that if they do, it is not an oft-recurring event. Ad-hoc wireless networks are inherently mobile, and make this assumption false. An extension to the algorithm is to deduce the type of wireless network the beacon is from, and exclude all beacons originating from an ad-hoc network.

As this study was conducted with practicality in mind, the constraints of deploying this system in a hospital environment had to be considered. While many hospitals have Wi-Fi systems in place, there is a lack of consensus on hospital regulations on the types of Wi-Fi to be used, and standards to help limit interference. This is important when considering safety and interference concerns. As an example, some medical devices operate on a 2.4GHz band, which is what most Wi-Fi access points also operate on. The legal system in Australia has failed to define any standards in hospital wireless systems, and as such, the Wi-Fi systems vary from hospital to hospital (Case, 2006). These variations could cause some problems for the algorithm, as the environmental interference could be substantially greater than most.

The collection of BSSID’s to correlate to a geo-coordinate is not a new concept. Large companies such as Google and Microsoft regularly fund programs to get a general location from a set of BSSID’s (Dziemborowicz, 2010). This is accomplished by having a set of vans, each equipped with a GPS unit and a Wi-Fi receiver, capture all BSSID’s as it drives around, and map them to the coordinates supplied by the GPS unit. Indeed, Google recently exposed an API to provide a coordinate and corresponding accuracy range when given a set of BSSID’s and their associated signal strength (Dziemborowicz, 2010). The availability of these services makes the collection of BSSID’s a security hazard. If given a historical set of BSSID’s and their associated signal strength, one would be able to track where a person has been with a high degree of accuracy. To explore this, a separate app was written to see how easy it was to obtain a location using a set of BSSID’s. Out of 10 test executed on a Samsung Galaxy S i9100 running Google Android version 4.0.3, 9 of the coordinates provided by the app were accurate within 35 meters. The possible security problem was further accentuated when it was realized that the Android system did not notify the user of the geo-location capabilities of the app. The Android operating system utilizes a permissions system, where users are informed of the permissions that an app requires before installing it. One of the permissions is the use of a handsets geo-location capability to locate a user. As an additional layer of security, a user is able to turn off the geo-location capabilities of the handset such that no apps could track a user, even if the app lists geo-location as one of the permissions required. As the test app written did not explicitly access the handset’s geo-location API’s, it bypassed the need to ask for a geo-location permission. This had the added consequence of being able to track a user, irrespective of whether the user had turned off the handset’s geo-location capabilities. As a result, not only was the app able to track a user using BSSID’s, it was able to do so without their knowledge and irrespective of their consent. In order to avoid the security issues present in collecting BSSID’s, a possible extension of the algorithm is to put all BSSID’s through a one-way hash function, whereby the hash is the same for the same BSSID, and the hash cannot be reversed.

The exploration of geo-location using Wi-Fi is a field that is bound to be explored in depth in the coming years. The advantages of a system that leverages existing architectures, and further offers reliable and accurate location services is one that has great appeal. As explored and validated, relative localization using Wi-Fi could be a pathway to provide such services.

3 . B I B L I O G R A P H Y 3 . 1 GLOSSARY

l BSSID

Ø Basic Service Set Identifier. A unique address that identifies the access point from which it originated.

l Beacon

Ø A single data transmission from the wireless access point, which carries the BSSID, the type of wireless network, the channel number and security protocols.

l RSSI

Ø Received Signal Strength Indicator. A measurement of the power present in a beacon.

l Fingerprint

Ø The mapping of a set of beacons to a singular object for later reference.

3 .2 REFERENCES

• Chris Dziemborowicz. "Google Is Watching." New Matilda, 18 May 2010. Web. <http://newmatilda.com/2010/05/18/google-watching>.

• David A. Case. "Deploying WLAN in a Hospital Setting: Understanding the Issues." Deploying WLAN in a Hospital Setting: Understanding the Issues. CE Magazine. June 2006. Web. <http://www.ce-mag.com/archive/06/ARG/case.htm>.

• E. Brewer, “Technology Insights for Rural Connectivity”, Oct. 2005.

• Joanie Wexler. "All about Wi-Fi Location Tracking." Techworld. Network World, 4 Apr. 2006. Web. <http://features.techworld.com/mobile-wireless/2374/all-about-wi-fi-location-tracking/>.

• Navarro Eduardo, Benjamin Peuker, and Michael Quan. "Wi-Fi Localization Using RSSI Fingerprinting." California Polytechnical State University (2010). Print.

• Razvan Musaloiu-E, Andreas Terzis. “Minimising the effect of WiFi interference in 802.15.4 wireless sensor networks.” Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA (2008). Web.

• Rod Bryant, “Assisted GPS – Using Cellular Telephone Networks for GPS Anywhere”, GPS World, May 2005.

• U. Grossmann, M. Schauch, S. Hakobyan, “RSSI based WLAN Indoor Positioning with Personal Digital Assistants”, IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Sept. 2007

• Xiao Qingjun. "Range-free and Range-based Localization of Wireless Sensor Networks." The Hong Kong Polytechnic University (2005). Web.