Communication Challenges in High-Density Deployments

of Wearable Wireless Devices

Alexander Pyattaev, Kerstin Johnsson, Sergey Andreev†, and Yevgeni Koucheryavy1234

January 13, 2015

1A. Pyattaev, S. Andreev, and Y. Koucheryavy are with the Department of Electronics and Com-munications Engineering, Tampere University of Technology, FI-33720 Tampere, Finland.

2K. Johnsson is with Intel Corporation, Santa Clara, CA, USA.3†S. Andreev is the contact author: Room TG417, Korkeakoulunkatu 1, 33720, Tampere, Finland

(+358 44 329 4200); e-mail: sergey.andreev@tut.fi4February 2015; Mobile Wearable Communications; Editor: Dr. Hassnaa Moustafa

Abstract

Wearable wireless devices are very likely to soon move into the mainstream of our society,

led by the rapidly expanding multibillion dollar health and fitness markets. Should wearable

technology sales follow the same pattern as those of smartphones and tablets, these new devices

(a.k.a. wearables) will see explosive growth and high adoption rates over the following five

years. It also means that wearables will need to become more sophisticated, capturing what

the user sees, hears, or even feels. However, with an avalanche of new wearables, we will need

to find ways to supply them with low-latency, high-speed data connections, so as to enable

the truly demanding use-cases such as augmented reality. This is particularly true for high-

density wearable computing scenarios, such as public transportation, where existing wireless

technology may have difficulty to support stringent application requirements. In this article, we

summarize our recent progress in this area with a comprehensive review of current and emerging

connectivity solutions for high-density wearable deployments, their relative performance, and

open communication challenges. Keywords: Mobile wearable applications, communication

challenges, high-density deployments, short-range radio protocols, ray tracing.

1

Mobile wearables today

Mobile wearable devices are the pinnacle of miniature wireless technology, allowing one to carry

inside a wrist-watch what is typically found in a smartphone. Today, all signs indicate that

wearable computing (or, simply, wearables) will emerge as the ’next big thing’ within the mobile

ecosystem across smartphone manufacturers, application developers, advertisers, and content

creators [1].

Even though particular samples of wearable technology have been seen for already 20 years

(primarily, in military applications, with soldiers wearing sensors and radios concealed in their

uniforms, night-vision and helmet mounted cameras, etc.), they largely remained cumbersome,

bulky, and unaesthetic for consumer applications [2]. Generally, only headphones have been

truly successful in being adopted in the dress code so far. However, recent progress in mobile

communications and battery technology, advanced miniature electronics, materials, and soft-

ware is enabling increasingly capable, energy efficient, lightweight, and fashionable wearable

computing products.

Already today, wearable fitness trackers are revolutionizing the ways people do sports, fol-

lowed by other categories of wearables, such as smartglasses and smartwatches, often positioned

as accessories to modern smartphones. Not limited to individual consumer applications, emerg-

ing wearable solutions include rings, healthcare monitors, and even some examples of smart

textiles, which all have the potential to benefit multiple market segments.

Analysts predict that wearable technologies will soon create unrivaled market opportunities

for a wide variety of players, from apparel manufacturers, IT and telecom companies, to brands,

content providers, advertisers, and OEMs [3]. According to the recent predictions, the global

wearable device market is expected to grow almost 4000% between 2012 and 2017 with the

lion’s share of volumes initially given by fitness bands and then followed by smartwatches and

smartglasses [4].

While currently the wearable technology market is dominated by the products released by

two industry giants, Apple and Google, we soon expect an avalanche of new wearable devices,

such as smartphone compatible watches, innovative healthcare solutions, and many variations

of smartglasses [5]. As the result, by year 2018 the wearable industry foresees revenues on the

order of 5B to 30B, making for an unprecedentedly large market.

Interestingly enough, the biggest barrier towards mass adoption of wearables does not lie in

the field of technology. It appears that such factors as fashion and style are actually forming the

customer’s desire to purchase a new device. Therefore, future wearables will need to become

more stylish and fashionable, before people start considering them seriously. However, the

moment this barrier is lifted, we will experience a huge influx of new devices flooding our daily

lives. One curious example in this category is iWatch announcement from Apple, which might

just do for watches what the iPhone did for mobile phones.

As more functional and fashionable devices hit the market [6], the main focus will shift from

fashion to usability. At that point, it will be critical to deliver wire-equivalent connectivity

for the wearable devices, sometimes at very high data rates [7]. For instance, smart glasses

have huge potential for any workforce that could benefit from access to hands-free information

flows, but those flows need to be streamed to the glasses continuously, and with reasonably low

2

latency.

Here, Google is not the only player, followed by Vuzix, GlassUp, Recon Instruments, and

Telepathy. Finally, real, working smartclothing might still be somewhat of a science fiction, but

many companies, like OMSignal, Hexoskin, and Athos, are planning to make it happen soon.

Again, wireless interaction would be the key for its seamless integration into our society.

In summary, several important changes are expected to occur in the smart wearable device

market, partly as a result of developments in the application model, and partly due to the

increasing density of wireless links. As new wearables get introduced, we will need to find

ways to supply them with low-latency, high-speed data connections [8], so as to enable the

truly demanding use-cases such as augmented reality [9]. This article summarizes our progress

along these lines with a comprehensive review of current and emerging connectivity solutions

for high-density wearable deployments, their relative performance, and open communication

challenges.

Setting stage for high-density deployments

As current technology allows for increasingly smaller functional devices, we witness tighter in-

tegration of those devices into our daily lives. With modern sensing capabilities, we expect to

receive more significant information about the world around us, while making it more intelli-

gent and flexible [10]. Similarly, through wearable computing, we strive to enhance our own

capabilities by allowing us to see and hear better, and interact with surrounding technologies

easier [11]. However, similar to large-scale sensor networks, the vision of wearable computing

requires serious engineering effort before it could be really used by everyone. One of the key

issues is the density of the resulting network: if each person is supposed to have several wearable

devices, it would be necessary to multiplex hundreds of connections in crowded areas, resulting

in connection densities never seen before.

Already today, in any public transportation, one can observe a significant proportion of pas-

sengers to be reading an e-book, listening to the music, playing electronic games, or performing

some other operations with their mobile devices. Not surprisingly, due to limited capacity of

today’s mobile Internet access, most of those activities are happening offline. Furthermore, the

vast majority of the audio-visual information is carried by wires, without involving wireless

medium at all. Hence, hundreds of passengers can easily enjoy services without interfering with

each other, but only as long as those are in no need of wireless links.

If we are to assume that some 25 out of 50 passengers in a conventional bus desire to use

Bluetooth 2.0 communications technology for their Hi-Fi stereo headsets at the same time, we

would face the simple fact that there is barely enough bandwidth for all of them. And that

is just for the average of 0.5 wearable device per passenger, while we could soon be looking at

around 5 devices per passenger. In the very near future, we will apparently have to consider

the situation that what we can support today in terms of network density is simply not enough

– we will need to efficiently multiplex many more wireless links.

Wireless engineers have already been facing similar scalability challenges in sensor networks.

While sensor network densities are much lower, the coverage areas typically exceed those for

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wearable computing applications. As a result, future wearables may just do to the short-

range technology what massive machine-type communication scenarios have already done to

the cellular networks. Indeed, in the past technology, the density of service has seldom been

the main design target. By contrast, coverage and capacity have traditionally been driving

wireless protocol development, and for the majority of use cases it seemed to be sufficient (even

though frequent users of WiFi hot spots may argue otherwise).

In this paper, we take a closer look at the emerging challenging scenarios that wearable

computing brings along, and investigate if a paradigm shift is needed in the design priorities

of the wireless technologies aiming at wearable markets. We also consider the current state-of-

the-art in wearable device and IoT-oriented protocols, as well as several more recent solutions,

as potential candidates for the future “wire replacement” in the realm of wearable computing.

Finally, we evaluate attainable levels of spatial reuse as well as achievable peak bitrates for the

most promising candidate technologies when used in high-density wearable computing scenarios.

We conclude by detailing the appropriate design targets that might result in more advanced

protocols for future wearable computing applications.

Candidate technologies for mass wearables

Every existing wireless protocol may be viewed as a compromise between simplicity, efficiency,

and flexibility. Below we review some of the existing protocols from the point of view of high-

density wearable applications and investigate their relative position with respect to this balance

point.

State-of-the-art wireless protocols

Today, WiFi is probably the dominant short-range connectivity solution. We find conven-

tional WiFi interface, based on IEEE 802.11 technology, in close to any mobile device, with

emerging IEEE 802.11ac and IEEE 802.11ah extensions targeting the important special cases

of high-throughput and low-power applications. The WiFi MAC (medium access control) can

usually multiplex around 5-10 users reasonably well, typically achieving spectral efficiency of

over 90% [12]. However, when we consider the high-density application scenarios characteristic

of future wearable computing, this protocol may have difficulty to support stringent application

requirements.

For instance, as follows from our above survey, smartglasses could be one of the major

wearable applications, but running a high-definition video signal without wires, albeit making

a very attractive selling point, cannot be easily done without several tens of megabits of bitrate.

Hence, in emerging protocols aiming to replace Bluetooth and WiFi at some point, this limita-

tion has been addressed quite radically, by scaling up to several orders of magnitude in terms

of throughput, from tens to thousands of megabits per second (such as WiGig products based

on IEEE 802.11ad). In what follows, we review these novel mmWave solutions in more detail.

Meanwhile, a summary of existing short-range radio protocols in wide use today is provided in

Table 1.

4

Table 1: Comparison of current short-range radio technologiesRate, Mbps Range, m Users per cluster

Bluetooth 0.5-20 10-15 8IEEE 802.11g/n 50-120 10-50 5-10IEEE 802.11ac up to 400 10-30 10-15IEEE 802.11ah 0.1 10-100 5-10

ZigBee 0.5 10-100 5-10

Indeed, our daily experience with WLAN (Wireless Local Area Network) technology con-

firms that it has difficulty to support some 20 devices attempting to access the channel simul-

taneously, and has near-zero efficiency with 50 active users. The reason behind this observation

is that WiFi has been designed around a certain “typical” number of users that would end

up contending for the channel. Any more than that provisioned number – and the network

efficiency will degrade dramatically. Conventionally, for short-range radios, the underlying as-

sumption is that there will be at most 10-20 devices within a single collision domain. What

happens beyond that “limit” is typically left out of the discussion in respective standards and

remains up to the researchers/developers to tackle. As we argue below, if we take a look at

the future of short-range radios (namely, the 60 GHz band protocols [13]), we will see a similar

situation.

Emerging short-range radio solutions

Today’s short-range radio developments focus mostly on the ultra high frequency (UHF) bands,

primarily, the 60 GHz ISM (industrial, scientific and medical) spectrum. Currently, there are

several emerging solutions on the market, but all of them are built around similar principles.

The first UHF solution, released in 2008, is commonly known as the Wireless HD standard.

It implements a controller-based MAC with both random-access and scheduled operation within

the same network. Each Wireless HD network has exactly one controller, that governs every

single aspect of its operation, much like a cellular base station would, but only with time-

domain multiplexing. However, unlike cellular systems, Wireless HD does not have dedicated

procedures for resource negotiation between neighboring networks. It means that whenever

multiple networks overlap in space, time, and frequency, they do not have any reasonable

means to coordinate their effective schedules. Therefore, all scheduled transmissions that were

supposed to occur at the same time would be very likely to fail due to disruptive interference.

Unfortunately, with the primary application of Wireless HD being in audio-visual equipment

(e.g., in media centers and home theaters), this issue is not likely to be addressed any time soon,

as normally one would not have conflicting media systems at home. This does, however, mean

that Wireless HD technology cannot be immediately employed for most wearable applications,

as it would only operate when there is no chance of overlapping coverage areas. As it has

exactly four frequency channels, one can safely assume that there cannot be more than four

networks running wireless HD at any given point of space.

Following Wireless HD, the WiGig standard (implementing IEEE 802.11ad technology) has

been released in 2009. It offers a much greater flexibility to short-range wireless systems. In

5

particular, WiGig allows for arbitrary data traffic to be efficiently exchanged between devices,

and has much better capabilities to support low-cost devices that may opt out of supporting

high-bitrate transmissions. However, when it comes to architecture, WiGig is very similar to

Wireless HD: it is also designed around the concept of a central controller, which would assist

in scheduling of all transmissions within the network. Just like in Wireless HD, WiGig assumes

that there will never be a situation when more than four networks need to coexist, as it does

not provide any means to resolve the resulting conflicts between controllers. Similarly, WiGig

does not allow for a reliable coexistence of more than four networks at a time.

ECMA 387, released in 2010, is probably the best-suited technology for wearable applica-

tions. All the same with the previously discussed options, it utilizes a controller-based MAC.

However, on top of that, ECMA 387 is the only short-range standard explicitly covering network

mobility by providing mechanisms to remedy possible conflicts of interest that may happen as

a result (see Section 16.5.3.11 of the standard for details). The individual subnetworks in the

ECMA 387 network have two mechanisms to resolve conflicts of coverage areas: soft channel

switch and coordination.

Whenever a conflict is detected, one of the controllers would have to look for alternative

frequency channel to use, and if one is found, it will switch to the new channel together with all of

the associated devices. However, if that is not possible, ECMA controllers employ a mechanism

to keep their reference clocks aligned by adjusting their beacon transmission times. This allows

for the beacons from all the overlapping networks to be transmitted one after another. Hence,

the controllers have capability to keep track of each other’s scheduled transmissions and avoid

unwanted conflicts.

All of that additional signaling, however, adds to protocol overheads, and consequently to

the complexity and cost of the devices. Sadly, it also takes a fair amount of time for such a

system of networks to converge, and thus an abrupt change in the network structure may be

detrimental to all existing connections. In summary, ECMA does allow an arbitrary number of

networks to coexist, but not without a cost.

This incurred overhead is at least 21.3 microseconds per superframe of 16.384 ms (about

0.1% of its duration) for each network within the collision domain, plus the fact that no spatial

reuse can happen between any two pairs of devices which belong to thus coordinating controllers.

In practice, even the raw overhead of extra beacons could not be neglected, as for, say, 200

networks it is already 26% of the superframe resource, and that is assuming that there are

no other problems. That said, we can safely assume that ECMA 387 protocol could work

reasonably well in public transportation scenarios, as one would not anticipate more than

several hundreds of people in a single collision domain. Unfortunately, there are no vendors

supporting ECMA 387 technology at the moment of writing this article.

As a result of our technology review, we conclude that emerging radio protocols to be used

for short-range communication generally do not adequately address the problem of contention

and resource allocation on the scale one would anticipate in the wearable computing scenarios.

The original underlying assumption has been, that due to a very fast decay of wireless signal

at higher frequencies, it would not matter how well the protocol reacts to conflicting coverage

areas, as there would never be any meaningful number of users, not mentioning networks, in one

6

Figure 1: Motivating high-density scenario: a commuter train

collision domain. In what follows, we demonstrate that this assumption does not hold anymore

even in a very conventional wearable scenario.

Spatial reuse in short-range communications

In this section, we consider the achievable spatial reuse factors in a typical public transportation

environment. Today, a significant fraction of the population takes daily commutes in public

transportation on a regular basis. In our motivating public transportation scenario, we consider

hundreds of people crowded into regular train cars, airplanes, or buses. There could easily be

as many as 200 people in a subway train car, adding up to at least 300 mobile devices (as there

are actually more mobile phones than people). To make matters worse, a commuter train could

act as a decent waveguide at 2.4 or 5 GHz ISM frequencies (see Figure 1), when traveling inside

a tunnel. Below we estimate the anticipated contention levels in modern public transportation

by applying advanced radio coverage prediction methods.

Noteworthy, in public transportation scenarios the radio signal is reflected by the walls of

the vehicle, reducing the effect of occlusions that would normally be created by people and

other objects. To evaluate the effective coverage area of a given device in the vehicle, we utilize

a variation of a ray tracing model, which has been specifically designed for the purposes of

this research. In our model, we assume free-space propagation if no people occlude the path

between the TX and the RX positions. In addition to conventional path loss, at 60 GHz there

is also a noticeable contribution of atmospheric absorption, which is modeled conventionally as

15 dB/km in reasonable conditions according to ITU specifications [14].

If there are people occluding the line-of-sight (LOS) path between the TX and the RX, then

we assume that the incident radio wave is scattered. This is due to the fact that humans have

the reflection coefficient of roughly 0.4 (linear) at 5 and 60 GHz based on [15], and the respective

transmission can thus only reach receiver via some other (indirect) paths. One of the effects to

account for here is diffraction, which for an obstacle sized as a human being costs about 10-15

dB at both 5 and 60 GHz frequencies, based on the Universal Theory of Diffraction (interested

reader is referred here to ITU recommendations on the subject of diffraction). Unfortunately,

as no strict knowledge of passenger geometry can be obtained, we pessimistically add a 15 dB

penalty in all of the cases as an upper bound on path loss, when the only path is that via

7

diffraction.

Similarly, wireless energy can be reflected from walls, floor, and ceiling of the vehicle, which

are typically made of metal, thus resulting in specular reflections with minimal scattering or

attenuation. One can readily estimate the path loss in such cases based on the principles

of geometric optics. Naturally, reflected signals take a longer route to propagate, and hence

typically carry less energy. Additionally, if the path to a reflection surface is occluded, no

reflected energy is received. However, in practice it rarely happens that all of the reflected

paths are occluded simultaneously, as the passengers do not usually occlude the ceiling of the

vehicle. In summary, the ray tracer employed in this study does not claim absolute accuracy,

but serves to obtain the first-order understanding by establishing the lower bounds on how far

would electromagnetic energy propagate.

As our deployment model for the target commuter scenario, we adopt the existing MOVIA

subway train car, which is typically used in large metropolitan areas of San Francisco, London,

Shanghai, and many others. We also assume that several cars compose a longer train, as they

would normally do in real-world subways. For more technical data on the trains in question,

the reader is referred to the manufacturer’s webpage1. In addition, we make an assumption of

exactly five wearable devices per passenger, which may even become an underestimation in the

future, but for now should provide us with a clear baseline scenario. Finally, we assume that

all of the devices are always able to reach their theoretical maximum of throughput, which we

set as 60 Mbps per 20 MHz at 5 GHz and 4.620 Gbps per channel at 60 GHz. As our study

shows, these are feasible numbers for low-cost devices, whereas higher rates generally require

complex beamforming procedures and thus do not fit into the low-cost paradigm.

With our ray tracing tool, we first investigate the signal propagation characteristics in the

motivating commuter scenario. The heat map in Figure 2 clearly illustrates how far the radio

signal propagates along the train on different frequencies. There are some non-linear effects

observed due to occlusions, but we conclude that a general regression would adequately capture

the overall dynamics of the signal propagation.

Further, studying in more detail path loss vs. distance, we clearly observe a distinct pattern

of dependence. In Figure 3, we illustrate the two particular propagation mechanisms, reflection

and diffraction, at longer ranges. What is also important to note here, is that if we assume a

reasonable WLAN protocol in operation, it would require an isolation of around 80 dB between

the receivers sharing the same frequency channel. The vertical lines in the plot confirm that in

a half-empty train those path losses are achieved at distances of 11 and 110 meters for 60 and

5 GHz carriers, respectively. Therefore, our results suggest that the 5 GHz signal easily covers

the train car completely and carries sufficient energy even beyond it. On the contrary, the 60

GHz signal is notably attenuated by people and dissipation in free space.

From our results, we also learn that interference with noticeable levels of well above the

noise floor travels reliably over sufficient distances, such that multiple passengers with all of

their devices become affected. One can estimate how much impact there would be, and the

corresponding results are detailed in Figure 4. Clearly, we cannot simply disregard the fact

that there could be as many as several hundreds of wearable devices trying to share the channel

1http://www.bombardier.com/en/transportation/products-services/rail-vehicles/metros.html

8

Figure 2: Signal propagation inside the train, 100 people

at the same time. Even with several orthogonal frequency channels (e.g., 4 at 60 GHz and 24

at 5 GHz), it is still barely sufficient to even approach the required numbers. In particular,

during peak commuting hours (with up to 200 passengers per a train car), radio protocols at 60

GHz would need to deal with up to 40 devices trying to share the channel. Most importantly,

those devices would all belong to different networks, complicating any feasible coordination. At

5 GHz, the situation is even worse, with up to 100 devices in a collision domain on the same

channel, which is way beyond workable ranges for both WiFi and Bluetooth protocols.

Interestingly, we also learn how larger numbers of users inside the train car have a highly

non-linear effect on achievable capacity. Even though we may anticipate that the neighboring

people would absorb some radio energy, in fact it barely happens, and the increase in user

densities has truly detrimental effects on available service quality. For instance, by analyzing

Figure 4, we conclude that at already 20 people per train car the use of 5 GHz band for

multimedia applications becomes somewhat complicated. Similarly, the anticipated hundreds

of megabits rapidly degrade to tens for 60 GHz systems. It is also important to note that

the bitrate calculations reported here are theoretical maximums, so in practice we expect even

lower numbers due to MAC protocol overheads, as discussed above.

Summarizing, we discover that low-frequency radios face enormous challenges in high-density

wearable scenarios. It may even be so that there are simply no means to make them serve the

needed numbers of users at any reasonable cost in terms of frequency resource. In contrast,

9

Figure 3: Path loss inside the train

Figure 4: Network scaling in public transportation scenarios

60 GHz technologies could potentially deliver the desired rates of several tens of megabits per

device in the target scenario. On the other hand, the actual protocols that exist today cannot

efficiently handle tens of collocated networks operating simultaneously.

10

Towards efficient wearable connectivity

As a conclusion to our evaluation of high-density wearable connectivity, we summarize the key

envisioned challenges. First, the existing low-frequency communication protocols (such as WiFi

and Bluetooth) simply do not scale well enough to support massive wearable computing de-

ployments. There is a possibility that a new look at the radiated power levels could remedy this

to some extent, but then the range and coexistence with other wireless systems would become

a problem. Second, the existing 60 GHz protocols, while theoretically having the capacity to

serve the desired user densities and enable massive wearable applications, lack efficient mecha-

nisms to handle scenarios where there are tens of neighboring networks overlapping with each

other.

One could, however, envision a future wireless system that would be specifically designed

to serve high-density wearable deployments. For it to operate, all the necessary functionality

would need to be in place to arbitrate competition for resources from hundreds of diverse systems

(perhaps even without explicit negotiation due to security/privacy concerns). In addition, a

pure random access based protocol may be required due to the fact that any sort of structure

imposed by a person-specific coordinator is likely to be disrupted by some other people around.

In summary, wireless engineers currently face a serious challenge of designing an efficient

wearable communication protocol that would operate in the very difficult high-density scenarios.

To this end, the following requirements to such protocol may be distilled:

• Densities of ˜4-5, up to 10 nodes per square meter, with some 50-100 devices in a single

collision domain.

• Average link lengths of 1 meter, expected interference range of 10 meters.

• Highly repetitive, QoS-demanding access patterns typical for sensor networks.

• Relatively low per-device bandwidth requirements.

• Very high cluster densities (e.g., each 5 devices may form their own network), with po-

tential cluster mobility.

Today, none of the existing state-of-the-art wireless protocols readily address the demanding

high-density wearable scenarios. Whereas certain solutions have been designed to remain scal-

able under arbitrarily high loads, they still suffer from overheads proportional to the number

of users. Unfortunately, when one deals with hundreds of devices in a collision domain, such

overheads may turn out to be prohibitive. And that is on top of the fact that nearly all actually

scalable protocols are random access based, and therefore cannot guarantee consistent access

delays. To bridge this gap, additional efforts are required to engineer efficient radio protocols,

based on more scalable approaches, that have known complexity irrespective of the number of

active devices in the network. We expect this direction to become and important research trend

in the very near future.

11

Acknowledgment

This work is supported by Intel Corporation, TISE, and the IoT SRA program of Digile, funded

by Tekes. The work of the third author is supported with a Postdoctoral Researcher grant by

the Academy of Finland.

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Authors’ Biographies

Alexander Pyattaev (alexander.pyattaev@tut.fi) is a Ph.D. Candidate in the Department of

Electronics and Communications Engineering at Tampere University of Technology, Finland.

He received his B.Sc. degree from St. Petersburg State University of Telecommunications, Rus-

sia, and his M.Sc. degree from Tampere University of Technology. Alexander has publications

on a variety of networking-related topics in internationally recognized venues, as well as several

technology patents. His primary research interest lies in the area of future wireless networks:

shared spectrum access, smart RAT selection and flexible, adaptive topologies.

Kerstin Johnsson (kerstin.johnsson@intel.com) is a Senior Research Scientist in the Wire-

less Communications Laboratory at Intel, where she conducts research on MAC, network, and

application layer optimizations that improve the mobile client experience while reducing wire-

less operator costs. She graduated from Stanford with a Ph.D. in Electrical Engineering and

has more than 10 years’ experience in the wireless industry. She is the author of numerous

publications and patents in the field of wireless communications.

Sergey Andreev (sergey.andreev@tut.fi) is a Senior Research Scientist in the Department

of Electronics and Communications Engineering at Tampere University of Technology, Fin-

land. He received the Specialist degree (2006) and the Cand.Sc. degree (2009) both from St.

Petersburg State University of Aerospace Instrumentation, St. Petersburg, Russia, as well as

the Ph.D. degree (2012) from Tampere University of Technology. Sergey (co-)authored more

than 80 published research works on wireless communications, energy efficiency, heterogeneous

networking, cooperative communications, and machine-to-machine applications.

Yevgeni Koucheryavy (yk@cs.tut.fi) is a Full Professor and Lab Director at the Depart-

ment of Electronics and Communications Engineering of Tampere University of Technology

(TUT), Finland. He received his Ph.D. degree (2004) from TUT. He is the author of numer-

ous publications in the field of advanced wired and wireless networking and communications.

His current research interests include various aspects in heterogeneous wireless communication

networks and systems, the Internet of Things and its standardization, as well as nanocommu-

nications. He is Associate Technical Editor of IEEE Communications Magazine and Editor of

IEEE Communications Surveys and Tutorials.