Computer Networks 55 (2011) 3224–3245

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Computer Networks

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Wireless sensor deployment for collaborativesensing with mobile phones

Zheng Ruan a, Edith C.-H. Ngai a,⇑, Jiangchuan Liu b

a Department of Information Technology, Uppsala University, Swedenb Department of Computing Science, Simon Fraser University, Canada

a r t i c l e i n f o a b s t r a c t

Article history:Received 8 January 2011Received in revised form 31 May 2011Accepted 13 June 2011Available online 26 June 2011

Keywords:Sensor networksMobile phonesCollaborative sensingDeployment

1389-1286/$ - see front matter � 2011 Elsevier B.Vdoi:10.1016/j.comnet.2011.06.017

⇑ Corresponding author.E-mail address: edith.ngai@it.uu.se (E.C.-H. Ngai

Wireless sensor networks have been widely deployed to perform sensing constantly at spe-cific locations, but their energy consumption and deployment cost are of great concern.With the popularity and advanced technologies of mobile phones, participatory urbansensing is a rising and promising field which utilizes mobile phones as mobile sensors tocollect data, though it is hard to guarantee the sensing quality and availability under thedynamic behaviors and mobility of human beings. Based on the above observations, wesuggest that wireless sensors and mobile phones can complement each other to performcollaborative sensing efficiently with satisfactory quality and availability.

In this paper, a novel collaborative sensing paradigm which integrates and supportswireless sensors and mobile phones with different communication standards is designed.We propose a seamless integrated framework which minimizes the number of wirelesssensors deployed, while providing high sensing quality and availability to satisfy the appli-cation requirements. The dynamic sensing behaviors and mobility of mobile phone partic-ipants make it extremely challenging to estimate their sensing quality and availability, soas to deploy the wireless sensors at the optimal locations to guarantee the sensing perfor-mance at a minimum cost. We introduce two mathematical models, a sensing quality eval-uation model and a mobility prediction model, to predict the sensing quality and mobilityof the mobile phone participants. We further propose a cost-effective sensor deploymentalgorithm to guarantee the required coverage probability and sensing quality for the sys-tem. Extensive simulations with real mobile traces demonstrate that the proposed para-digm can integrate wireless sensors and mobile phones seamlessly for satisfactorysensing quality and availability with minimized number of sensors.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

The advancement of recent technologies in embeddedsystems and low power wireless communications turnedwireless sensor networks into reality. A wireless sensornetwork (WSN) consists of spatially distributed autono-mous sensing devices which cooperatively monitor physi-cal or environmental conditions, such as temperature,sound, vibration, pressure, motion or pollutants at differ-

. All rights reserved.

).

ent locations. Traditional sensor networks involve a num-ber of stationary sensors being deployed carefully atchosen locations for a particular technological purpose.For example, the structural health of buildings in anearthquake-prone area can be monitored by deploying anetwork of dedicated wireless sensor nodes equipped withaccelerometers on the buildings. Applications of WSNs in-clude habitat monitoring, structure monitoring, healthmonitoring, object tracking and fire detection [1]. Althoughindividual sensor node is not very expensive, large deploy-ment of sensor nodes in the network could make the totalcost considerably high.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3225

Apart from sensors, mobile phones are becomingincreasingly popular and more powerful. Some mobilephones are even equipped with various sensing capabilities,such as detecting sound, motion, location, etc. Recently, analternative paradigm has emerged for accomplishing large-scale sensing, known as participatory sensing or urbansensing [2,3]. The key idea of participatory sensing is toleverage the existing sensing and communication infra-structure to achieve the goal of sensing by having networkusers providing and sharing the necessary sensing informa-tion. Mobile phone users could collect data at different timeand locations when they move around. Similarly, Metro-Sense [3,4] envisioned a people-centric paradigm for urbansensing at the edge of the Internet, at very large scale basedon an opportunistic sensor networking approach. A numberof participatory sensing applications have emerged in re-cent years. CarTel [5] is a system that uses mobile sensorsmounted on vehicles to collect information about traffic,quality of en-route WiFi access points, and potholes onthe road. Micro-Blog [6] is an architecture which allowsusers to share multimedia blogs enhanced with inputs fromother physical sensors of the mobile phone. However, therandomness of user movements and behaviors may bringdifficulty in guaranteeing satisfactory coverage and sensingquality in the network. The quality of sensing data resultedby human may differ from one to another, which may notalways satisfy the requirement of the applications. Com-pared with the dynamic nature of participatory sensingcampaigns, wireless sensor networks are relatively stable.In most applications, after the WSNs are deployed, thetopologies remain almost the same and their behaviorsare more predictable. Although there are some random orunpredictable factors, such as damage of sensors, runningout of energy, and data inaccuracy during transmission,their performance can be analyzed. It is obvious that the dif-ferent natures and characteristics of stationary sensors andmobile phones could complement each other to performcollaborative sensing to reduce the deployment cost andprovide satisfactory quality of sensing data.

In this paper, we consider a novel collaborative sensingparadigm which includes stationary sensors and mobilephones. Inspired by the mobile sensing architectures in[2,3], we investigate how stationary sensors could bedeployed to complement the sensing performance of themobile phones. In particular, we aim at providing collabo-rative sensing by both mobile phone participants andstationary sensors at satisfactory sensing quality and avail-ability with a minimized deployment cost. We face someunique challenges when designing cost-effective and effi-cient sensing for this innovative collaboration paradigm.First, the existing wireless communication standard of sen-sors and mobile phones are different. Most of the existingsensors only support IEEE 802.15.4 standard and Zigbee forcommunication. On the other hand, mobile phones supportmainly IEEE 802.11b/g standard (WiFi) and bluetooth, butnot IEEE 802.15.4 and Zigbee. These limitations should betaken into account when building an integrated network.Second, the efficiency of the collaborative sensing para-digm depends on the sensing quality and the availabilityof mobile phone participants and sensors. Unfortunately,human behaviors and mobility may vary from time to time

and they are not always predictable. Third, we would liketo reduce the cost of the collaborative sensing system byminimizing the number of sensors required in the field,while guaranteeing the sensing quality and availability inlong period of time. Moreover, one-time deployment ispreferred to avoid extra costs and inconvenience causedby re-deployments.

To address these challenges, we propose a novel inte-gration framework that incorporates mobile phones andwireless sensors seamlessly to provide cost-efficient col-laborative sensing with high quality and availability at aminimized deployment cost. First, we present a new net-work architecture that support mobile phones and wirelesssensors with different wireless communication standards.Considering the most common technologies on existingphones and sensors, we suggest a network with WiFi asbackbone and overlayed with a IEEE 802.15.4 network forconnecting to the sensors. Second, we introduce two math-ematical models to estimate the sensing quality and mobil-ity of mobile phone participants based on reputationstatistics and probability model for mobility respectively.Third, we propose a cost-effective sensor deployment algo-rithm which minimizes the number of stationary sensors,while guaranteeing the sensing field are covered with therequired sensing quality and probability in most of thetime. Despite the dynamic behaviors of mobile phone par-ticipants, we aim at one-time deployment of wireless sen-sors to avoid unnecessary re-deployments for a practicaland cost-effective solution. Forth, we evaluate our collabo-rative sensing paradigm comprehensively with real mobiletraces from the mobile phone participants in Disney World(Orlando).

The remainder of this paper is organized as follows:Section 2 presents related work. In Section 3, we describethe system architecture for collaborative sensing withwireless sensors and mobile phone. In Section 4, we pres-ent our sensing and terrain models followed by the sensordeployment problem in the proposed paradigm. The sensordeployment framework for collaborative sensing in mobilephone assisted environment is presented in Section 5, to-gether with detailed descriptions of the three modules. InSections 6 and 7, we conduct extensive simulations to eval-uate our framework and provide a case study based on realmobile traces. Finally, we conclude the paper in Section 8.

2. Related work

Participatory sensing has been studied recently to pro-vide mobile phone-based data gathering [2]. It is coordi-nated across a potentially large number of participantsover wide spans of space and time. Research topics onparticipatory sensing spread over privacy mechanisms,context-annotated mobility profiles for recruitment, per-formance evaluation for feedback, incentives and recruit-ment, etc. Applications of participatory sensing include,collecting and sharing information about urban air pollu-tion [7], noise pollution [8], and consumer pricing informa-tion [9]. BikeNet [10] has successfully demonstrated aprototype that integrates a mobile personal sensor withmultiple static sensors embedded in a homogenous envi-

3226 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

ronment. It utilizes an opportunistic networking paradigm,whereby mobile sensing platforms are tasked and data ismuled or uploaded according to the opportunities thatarise as a result of the uncontrolled mobility of the cyclists.Different from BikeNet, we consider stationary sensors andmobile phones that can connect to the Internet and sharethe same sensing duties, i.e. noise sensors and camera thatare equipped on both type of devices. We work on thedeployment problem of the stationary sensors to reducethe deployment cost and improve the sensing perfor-mance. Reddy et al. proposed a model for evaluating par-ticipation and performance in participatory sensing basedon beta distribution [11]. They also proposed a recruitmentengine that uses campaign specifications provided by anorganizer to select a limited set of potential volunteersbased on participants’ previously gathered mobility pro-files [12]. Their work focuses on the recruitment of mobilephone participants considering their geographic and tem-poral availability, while our framework works on thedeployment problem of stationary sensors for collabora-tive sensing with mobile phone participants.

Opportunistic networks [13] and delay tolerant net-works (DTNs) [14] have been proposed, which are drivenby the popularity of mobile phones and personal electronicdevices. Messages are routed through any possible nodeopportunistically as next hop, provided that it is likely tobring the message closer to the final destination. A numberof routing and forwarding protocols have been proposedfor opportunistic networks and delay tolerant networks(DTN) [15,16]. Burns et al. [17] proposed the MV routingprotocol which learns the movement pattern of networkparticipants and uses it to enable informed message pass-ing. A similar approach is followed in the PROPHET proto-col [18] to improve the delivery rate. Zhao et al. [19]proposed a message ferrying approach to address the net-work partition problem in sparse ad hoc networks. Thesework focus on the communication and information sharingbetween intermediate peers opportunistically. Differentfrom them, our work provides an infrastructure of WSNs,but using mobile phones as complementary tools to collectsensing data collaboratively. We aim at providing optimaldeployment of wireless sensors to minimize the systemcost, while guaranteeing enough sensing quality and avail-ability by considering the mobility and sensing quality ofmobile phone users.

Techniques for improving the performance of uncon-trolled mobility opportunistic sensor networking (OSN)has been studied in [20,21]. While this novel OSN approachcan allow large scale sensing at a lower cost compared toan ubiquitous static infrastructure of sensing devices, theopportunistic nature of sensing and communication pre-sents challenges to the fundamental sensor networkingoperations. Eisenman et al. [20] proposed sensor sharingand sensor substitution as two techniques for mobile sen-sors to improve the probability of successfully and moreexpediently completing the sensing tasks. They further im-proved the sensing quality of participants in the context ofsensor sharing in [21]. They investigated on the Metro-Sense architecture for large scale sensing based on hu-man-carried (mobile) sensors, leveraging opportunisticinteractions between sensors, to get large scale coverage

of human-centric activity and environment. They studiedcomprehensively on opportunistic sensing and data collec-tion for human-centric applications. Different from anopportunistic approach, we consider a system architecturewith infrastructure for reporting data from both the sta-tionary sensors and mobile phones. We focus on thedeployment problem of the stationary sensors to providesatisfactory sensing performance of the system consideringthe uncontrolled mobility of the mobile phones.

Deployment problems in traditional wireless sensornetworks have been widely studied. Tian et al. proposeda node-scheduling scheme to reduce system overall energyconsumption and increase system lifetime [22]. Theirscheme turns off some redundant nodes and guaranteesthat the original sensing coverage is maintained. Dhillonand Charkrabarty proposed two greedy algorithms fordeployment of wireless sensor network [23]. They built aprobability model for wireless sensors based on a gridsensing field. Chakrabarty et al. proposed a deploymentstrategy to reduce cost for wireless sensor network whichhas different kind of wireless sensors [24]. They formu-lated the problem with integer linear programming. Poduriand Sukhatme proposed an algorithm based on artificialpotential fields for the self-deployment of a mobile sensornetwork [25]. Their deployment strategy is researched in anetwork with the constraint that each of the nodes has atleast K neighbors. Our work is different from the aboveas we consider sensor deployment in mobile phone as-sisted environment. We investigate how sensor deploy-ment can be optimized cost-effectively considering themobility and human behaviors in mobile phone sensing.Our framework enables stationary sensors and mobilephone participants complement each other to provide sat-isfactory sensing services with minimized cost. Althoughsensor coverage with mobile sensors have been investi-gated, but most of them focus on deploying or controllingthe mobility of mobile sensors. For instance, Gupta et al.[26] proposed a stochastic sensor movement strategy thatuses a small number of mobile sensors to monitor variousthreats in a geographical area. Similarly, Wang et al. [27]designed two bidding protocols to guide the movementof mobile sensors to cover the coverage holes of the staticsensors. Different from their work, we consider mobilephones with uncontrolled mobility as the mobile sensors.Under this uncontrolled mobility scenario, our work aimsat improving the sensing performance by deploying sta-tionary sensors at optimal locations.

3. Collaboration sensing paradigm with wireless sensorsand mobile phones

We aim at designing a novel collaborative sensing par-adigm that connects wireless sensors and mobile phones ina network, such that users can collect and process datafrom both of them.

3.1. System architecture

The network in our paradigm includes both wirelesssensors and mobile phones. Unfortunately, the different

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3227

wireless communication standards on mobile phones andwireless sensors hinder direct communications betweenthem. Most of the existing wireless sensors communicatewith IEEE 802.15.4/ZigBee standard [28]. Although wecan find sensors that support WiFi [29] and bluetooth[30], they are not very common. On the other hand, mostof the mobile phones are equipped with GPRS, WiFi, blue-tooth and infra-red nowadays [6], but they are rarely Zig-bee enabled [31]. We need a new network architecturethat support devices with different communication stan-dards to collect and integrate data from them. We exploreddifferent implementations which enable mobile phonescommunicating with wireless sensors. For example, wecan install a wireless access point like Asus WL-500GP[32] which supports IEEE 802.11b/g and is equipped withUSB ports for connecting to the sensors. Alternatively, wecan connect a mobile phone such as Nokia N810 [33]through its USB port or a IEEE 802.15.4/ZigBee USB adapter[34] to the sensors.

Although USB ports and adapters could be used to con-nect mobile phones and sensors, they are far from a conve-nient and practical solution to the general mobile phoneparticipants. Based on the most popular existing technol-ogy, we suggest a hybrid network architecture as shownin Fig. 1 for our collaborative sensing paradigm. Theproposed architecture supports sensors and mobile phonesequipped with either IEEE 802.15.4/ZigBee or IEEE802.11b/g standards. Given the popularity and wide cover-age of WiFi, we consider WiFi as the backbone of our

Fig. 1. System ar

network which allows mobile phone users to report theirdata easily and freely. In the meantime, we also deploy asensor network that enables multi-hop communicationwith IEEE 802.15.4/ZigBee among the wireless sensors.The sensor network includes one or multiple sink nodesthat support IEEE 802.11b/g to overlay the sensor networkwith the backbone network. We may also include a gate-way server to process and store the data collected by thesensors and mobile phones. We do not require necessaryinteractions between sensors and mobile phones in thisstage to keep our design simple and practical.

3.2. Collaborative sensing with sensors and mobile phones

The network can be deployed in different places tomonitor the environment and human activities. Potentialsensing environments include amusement parks, universi-ties, tourist attractions, etc. Fig. 2 shows the map of anamusement park where a number of games, theaters andaquariums are located at different zones. The smiley facesin the figure indicate the spots that many people like to go.The map also shows some green areas with trees and pos-sibly mountains that attract less people. We intend to builda network for the administrator to monitor and collect datafrom the environment considering the unique behaviorsand mobility of the mobile phone participants collaborat-ing with wireless sensors.

We observe that the wireless sensors and mobilephones have different properties, such as mobility,

chitecture.

Fig. 2. Map of amusement park where smiley faces indicate the crowds.

Table 1Comparisons between wireless sensors and mobile phones.

Properties Wireless sensors Smart phones

Wireless interfaces IEEE 802.15.4, bluetooth Bluetooth, GPRS, WiFiMobility Stationary MobileBatteries 2 AA batteries, rarely recharged Proprietary, regularly recharged by usersProcessor 7.37 MHz (MICA2) [35] Around 1000 MHz (ARM Cortex) [36]Memory 512 kB Flash (MICA2) [35] Around 2 GB SD cardSensing capabilities Wide variety (e.g. temperature, humidity, noise, pressure, gas) Mainly GPS, sound, images, motions

3228 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

computation power and sensing capabilities (see Table 1).These are also the reasons making them complementary toeach other in a sensing system. We consider some primarysensing data like the noise level and images to be collectedby the microphone and the camera of the sensors or mobilephones. Based on the capability of the sensors and phones,other secondary data like temperature, pressure and mo-tion could also be detected with different types of sensingcomponents in the sensors or the mobile phones. We focuson the primary data which could be collected by sensors ormobile phones interchangeably in this work. However, theapproach could be extended easily for different kinds ofsensing data and application requirements. Sensors couldbe deployed cost-effectively with the assistance of mobilephones for monitoring the environment.

4. The sensor placement problem in sensor-mobilephone collaboration paradigm

4.1. Sensing and terrain models

In our collaborative sensing environment, there are twotypes of devices: mobile phones and wireless sensors.Since participants may have different kinds of mobilephones, their sensing capabilities differ from one to an-other. We extend the model proposed by Dhillon and

Charkrabarty for the detection probability of a target by asensor in a terrain of sensing area [23]. We assume thatthe detection probability varies exponentially with the dis-tance between the target and the sensor. A target at dis-tance h from a sensor is detected by that sensor withprobability

zðhÞ ¼ rte��h;

where � can be set to different values to model the sensingquality of heterogeneous sensors and the rates at whichtheir detection probability diminishes with distance. rt

can model the degradation on sensing quality of individualsensors at different time t in a changing environment. Thechoice of a sensor detection model could be changedaccording to different sensing environments withoutaffecting our algorithm. Terrain is an important factor inwireless sensor networks, which heavily affects the sens-ing capabilities of the sensing devices. For example, obsta-cles such as buildings can block the vision of some sensors.Fig. 3 shows an example of sensing field with obstacles.

In our paper, the sensing field is represented as a grid oftwo- or three-dimensional points gi. The distance betweenadjacent grid points is d. For simplicity, we assume thatsensors are deployed only at these grid points. The partic-ipants’ sensing actions are also considered to be performedwithin these grids. The number of grid points in the

Grid points

Obstacles

Sensor

r

dd

Fig. 3. Sensing field with obstacles.

Fig. 4. Overview of our framework.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3229

sensing field is denoted by N. We define the detectionprobability matrix, D, which describes the detection prob-ability from the sensors or mobile phones to the targets as

D ¼

d1;1 d1;2 � � � d1;n

d2;1 d2;2 � � � d2;n

..

. ... . .

. ...

dn;1 dn;2 � � � dn;n

0BBBB@

1CCCCA

in which di,j indicates the sensing probability of a target ingrid point j by a sensor or mobile phone in grid point i. Theprobability matrix can be calculated according to ourknowledge of the sensing and terrain models. We let dis(i, j)denote the distance from grid point i to grid point j. Then,entries of D are calculated as follows

di;j ¼zðdisði; jÞÞ if vision from i to j is not blocked;0 otherwise:

�

The detection probability matrix depends on the sens-ing capability of the sensors and mobile phones, so itmay vary from one type of devices to another even thoughthey are monitoring the same terrain. Given that ourframework is module-based, it is easy to extend to includemore advanced sensing models. For instance, the sensingcontext of changing environment could be considered interms of the data quality at different location and time[37]. Alternative sensing models could be applied in ourframework depending on the application requirements.Since we aim at one-time deployment for the stationarysensors, Qreq and Preq could be set more strictly and act asupper bound requirements of the designed application.

4.2. Problem description

Many factors have to be considered when a wirelesssensor network is deployed, such as energy consumption,connectivity and deployment cost. In a sensing environ-ment with sensors and mobile phones, the deploymentcost of sensors could be relatively expensive in comparisonwith the recruitment of mobile phone participants. Mini-mizing the number of sensors in deployment could defi-

nitely reduce the cost for the sensing applications. Ouraim is to deploy minimum number of sensors and provideenough coverage and sensing quality for every grid point inthe sensing field.

The grid points in a sensing area may have differentimportance according to the application requirements.For example, some grids are critical to the sensing cam-paign where data need to be sensed with higher priority.Such importance can also be changed during the progressof participatory sensing from period to period. Thus everygrid point gi is associated with a pair hQi,Pii, where Qi indi-cates the lowest quality of data required by the campaignexpressed as a real number in the range of [0,1]. The qual-ity of sensing result could be judged by the organizers orexperts. The parameter Pi indicates the lowest requiredcoverage probability for that grid point. Regarding to thecoverage probability, we mean the probability that a gridpoint gi is sensed by any mobile phone participants orwireless sensors. At the beginning of each period, the qual-ity and probability vectors Qreq = (Q1Q2� � �QN) andPreq = (P1P2� � �PN) are given as input parameters.

Wireless sensor network should complement mobilephone participants in sensing to make sure that enoughsensing quality and availability could be achieved. Our sen-sor deployment algorithm should be adaptive to humanactions. It is not wise to deploy the network once and thenremain it the same during the whole campaign. The partic-ipatory sensing campaign can be divided into several peri-ods. Before each period, the wireless sensor network couldbe reconfigured slightly according to the information fromthe participatory sensing campaign and the behaviors of itsparticipants. However, re-deployments are unfavored dueto the extra time, effort and cost. In case that re-deploy-ment is not possible, our scheme could predict the behav-ior of the mobile phone participants and figure out anoptimal sensor deployment that guarantees the best sens-ing quality at most of the time in the future.

5. Wireless sensor deployment in mobile phone assistedenvironment

We propose a seamless integrated framework for thedeployment of wireless sensors in mobile phone assistedenvironment. Our framework consists of three modules,which communicate with each other by passing parame-ters (see Fig. 4). The implementation of every module can

3230 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

be replaced by another provided that the interfaces be-tween the modules remain the same. This gives ourdeployment framework great flexibility and generality,which is important to support a diverse variety of partici-patory campaigns.

This section is organized as follows: firstly, we describethe sensing quality evaluation model. Then, we explain themobility prediction model for the participants. Finally, weuse the above two models to figure out the locations thatrequire the deployment of extra wireless sensors.

5.1. Evaluation of sensing quality of participants

The sensing quality of participants are affected by manyhuman factors, like community expertise [38], trustworthi-ness of the participants, data quality of their mobilephones, etc. Some participants will report their data reli-ably and honestly which can provide the system high sens-ing quality that satisfy the application requirements, whilesome bad participants might not always provide usefuldata. Participatory sensing reputation metrics can incorpo-rate expertise, data quality, credibility and certaintyamong the participants [11].

Evaluating sensing quality of participants has an inher-ent relation with reputation evaluation of transaction par-ties in e-commerce [39]. Online markets require a greatdeal of trust among trading partners to mitigate the risksinvolved in anonymous transactions. In reputation systemsfor e-commerce, the reputation of merchants are calcu-lated according to the feedbacks and remarks from cus-tomers [40]. Similarly, in participatory sensing, theparticipants may act as merchants who sell goods andthe organizers or experts may act as the customers. Unlikepeer-reviews in on-line market, the organizers in partici-patory sensing can evaluate performance of the partici-pants by comparing the sensing data among theparticipants and checking whether their collected datameet the application requirements.

Moreover, the sensing quality of participants dependheavily on the time and locations that their actions are per-formed. For instance, a mobile phone participant may bemore willing to report data when he is traveling, ratherthan hurrying to work. A participant may gain more expe-riences gradually and report data with higher quality alongtime. One may even change to a new mobile phone withstronger sensing capabilities. The dynamic natures of hu-man activities at different time and place bring uniquechallenges in sensing quality evaluation. We hence pro-pose a mathematical model to estimate the sensing qualityof mobile phone participants considering the order of theirprevious actions over time.

Beta distribution can be applied to model the perfor-mance of a participant, which is based on the statisticson probability distribution of some binary events [11,41].The beta probability distribution function y(qja,b) is ex-pressed by

yðqja;bÞ ¼ Cðaþ bÞCðaÞCðbÞ q

a�1ð1� qÞb�1;

where C is the Gamma function, 0 6 q 6 1, a and b areintegers greater than 0. The function is indexed by two

parameters, a and b. Consider a process has two possibleoutcomes fx; �xg, r denotes the number of outcome x ands denotes the number of outcome �x. Then the probabilitydensity function of outcome x in the future is a beta distri-bution by setting a = r + 1 and b = s + 1. At the beginning, aand b are initialized to be 1, which result in a uniformdistribution.

The results of participants’ performance can be repre-sented as a stochastic process which has two possible out-comes ðx; �xÞ. x means a successful action and �x means anunsuccessful action. For the ith outcome, we define ran-dom variables ri and si as follows

ri ¼1 if the ith action is successful;0 otherwise:

�

si ¼1 if the ith action is unsuccessful;0 otherwise:

�

Beta distribution can be applied to model participantsperformance by setting r as number of successful actionsand s as number of unsuccessful actions, such that

r ¼X

ri; s ¼X

si:

As the campaign progresses, the sensing performance ofthe participant is changing. The more recent performancesis more representative than the old ones, so that old per-formances should have less weights than recent ones. Weintroduce the following aging factor that emphasizes onthe order of action results as

ki ¼ kðt�tiÞ;

where t is the current day and ti is the day when the actionis performed. Meanwhile, it has the advantage of being cal-culated recursively by

r ¼ r0kðti�ti�1Þ þ ri; s ¼ s0kðti�ti�1Þ þ si;

where r0 and s0 are the r and s in the previous time stampat ti�1.

In practical campaign, the feedbacks from organizersabout the participants are not simply binary because theresult of an action cannot be only judged as successful orunsuccessful. In this case, the organizers may give thefeedback in form of a pair of real numbers hri,sii, where ri

indicates the satisfaction degree and si indicates the dissat-isfaction degree. In addition, it is also possible for the orga-nizers to give the feedback by only one real number vi.Then, ri and si can be calculated by

ri ¼1þ v i

2; si ¼

1� v i

2:

Since different tasks may have special difficulties, it isstraightforward that a positive weight wi can be appliedto show the levels of difficulty. More important the taskis, the larger its weight is. Then, ri and si can be calculatedby

ri ¼wið1þ v iÞ

2; si ¼

wið1� v iÞ2

:

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3231

Together with the aging factor, the parameters a and bcan be calculated as follows

a ¼ 1þX

ri ¼ 1þXwið1þ v iÞ

2kðt�tiÞ;

b ¼ 1þX

si ¼ 1þXwið1� v iÞ

2kðt�tiÞ:

After that, we can obtain the probability that the nextsensing action of a participant whose result is better thanQ by calculating

R 1Q yðqjða; bÞÞdq. Note that our framework

is extendable to include more advanced sensing qualityestimation models that consider the sensing context and dy-namic application requirements in changing environment.

5.2. Prediction of participant mobility

Another dynamical aspects of participants are their mo-tions because nobody knows what exactly they will dotomorrow. However, their motions are not completely ran-dom as most people have their schedule everyday or placesthey used to go. For example, students usually go to theuniversity canteen for lunch after their morning lectures.Similarly, tourists who just played with the roller coasterin the amusement park are likely to play with the ferriswheel nearby. The motion patterns of human beings couldbe learned and predicted by some mathematical models.We formulate and predict participants’ behaviors by theMarkov model here which requires only the sensing datauploaded by the participants.

Since most people care about their privacy, a reliableway to collect information of users’ motions is using theiruploaded geo-tagged data from which the locations can beobtained. Their motions in a day can be described by a se-quence L. Fig. 5 shows an example of motion sequence rep-resented as L = [A,D,C,E,B]. Every element in the sequencedescribes the location where the task is performed. The se-quence of participants’ motions can be modeled by a Mar-kov chain {c1,c2, . . .,cn}. Each state ci corresponds to the gridpoint gi in the sensing field.

According to the property of Markov transition matrix

Y, pðnÞi;j gives the probability that the Markov chain, startingin state ci, will be in state cj after n steps. We calculate the

probability for the starting states fð0Þ ¼ f ð0Þ1 f ð0Þ2 � � � fð0Þn

� �of

the participants in a period T, which can be obtained bysome statistics from the previous participants’ actions.

Y ¼

p1;1 p1;2 � � � p1;n

p2;1 p2;2 � � � p2;n

..

. . .. ..

.

pn;1 pn;2 � � � pn;n

0BBBBB@

1CCCCCA:

Fig. 5. Example of participant motions.

Let f ðmÞi denote the probability that a participant is instate ci at time m. These state probability at time m areconveniently arranged in a row-vector

fðmÞ ¼ f ðmÞ1 f ðmÞ2 � � � f ðmÞn

� �

known as the state probability vector at time m. It can becalculated by

fðmÞ ¼ fðm�1ÞY:

Repeated application of this recursive equation yields

fðmÞ ¼ fð0ÞYm:

From the Markov transition matrix Y, we can calculatethe probability of grid point gi being visited by a partici-pant during one day as

1�YNd

t¼0

1� f ðmÞi

� �;

where Nd is the number of time units in one day.

5.3. Cost-effective deployment of wireless sensors

Given the corresponding sensing requirement hQi,Pii ofeach grid point gi, the probability that its data can be sensedby any mobile phone participants with the required qualityis obtained by Algorithm 1. For each grid point gi, we calcu-late the probability Pri that gi cannot be sensed by any par-ticipants x in a time period T. Then the probability CovT

i thatgi cannot be covered by any participants is

Q8xPri. Finally,

CovTi ¼ 1�

Q8xPri denotes the probability that gi can be

covered by the mobile phone participants, where i = 1. . .Nwith N is the number of grids in the monitored area.

Algorithm 1. Calculation of coverage for each gridpoint

for all gi is a grid point doPri = 1;for all x is a participant do

px ¼R 1

Qiyxðqja; bÞdq;

for all gj is a grid point doif dj,ipx > Qreq then

Pri ¼ PriQT

t¼0 1� f ðtÞj

� �;

end ifend for

end for

CovTi ¼ 1� Pri;

end for

T1

We can obtain the coverage probability Cov ;

CovT1 ; . . . ;CovTn at each grid in different periods of time

T1,T2, . . .,Tn, where CovTj ¼ CovTj1 CovTj

2 � � � CovTjN

� �. Since

one-time deployment is often required for sensor net-works, we summarize the average coverage probability,Cov, by taking an average of CovTj over different periods as

Cov ¼Xn

j¼1

CovTj=n:

3232 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

Given the required coverage probability Preq for thegrids, we can calculate the missing coverage probabilityvector M ¼ Preq � Cov, where M = (M1M2� � �Mn). The gridcells with Mi > 0 need to be covered by extra wireless sen-sors with the required sensing quality Qreq. We representthese set of grid cells and model their wireless sensordeployment as a set-covering problem. The required sens-ing quality Qreq is quantified as the number of elements tobe covered for each of these grid cells. The elements of allthese grid cells are denoted as X, where X = [{QijMi > 0, "i}.In other words, X contains all elements that need to be cov-ered in the grids with missing coverage, i.e. Mi > 0. The set-covering problem has been proven as a NP-hard problemand heuristic algorithms have been suggested for solvingrelated problems in sensor networks [23,42].

An instance (X,F) of the set-covering problem consistsof a finite set X and a set F of subsets of X. We assume thateach element in X appears in at least one of the subsets in F,

X ¼ [Si2FSi;

where each Si represents the elements that can be coveredby a sensor deployed at grid cell gi, Si = ["jdi,j and di,j are theelements referring to the quantified sensing quality fromgrid cell gi to gj by a stationary sensor.

The set-covering problem here requires us to determinea minimum sized subset of F that covers all the elements ofX. In other words, given an instance (X,F), we are requiredto find a set C # F such that

X ¼ [Si2CSi and C is minimal:

A greedy algorithm for sensor deployment is presentedin Algorithm 2. The algorithm maintains a set of elementsof X that are yet uncovered in U. In each iteration of thewhile loop, the algorithm calculates the cost effectiveness,c ¼ cðSiÞ

jSi\Cj for each Si. It greedily chooses the set Si⁄ that hasthe minimum c, until all of the elements of X are coveredor there is no available sensors. The set C contains all thesets that have been chosen as part of the set cover at anypoint during the operation of the algorithm.

Algorithm 2. Deployment of wireless sensors

Input: (X,F)Sensors_num = Sensors_max;U X;C /;while (U – /) and (Sensors_num > 0) do

c ¼ cðSiÞjSi\Cj;

select the Si⁄ with the smallest c;For each e 2 Si � C, set price(e) = c;U U � Si⁄;C C [ Si⁄;Sensors_num = Sensors_num-1;

end whilereturn C;

Theorem. The above greedy algorithm is an Hu factorapproximation algorithm for the minimum set cover problem,where Hu ¼ 1þ 1

2þ � � � þ 1u � log u, where u is the total

number of elements in X. [42]

Proof. We knowP

e2UpriceðeÞ = cost of the greedy algo-rithm = c(S1) + c(S2) + � � � + c(Sm) because of the nature inwhich we distribute costs of elements.

Consider the optimal sets are O1,O2, . . .,Op, such that

OPT ¼ cðO1Þ þ cðO2Þ þ � � � þ cðOpÞ: ð1Þ

Now, assume that the greedy algorithm has covered theelements in C so far. Then, we know that uncovered ele-ments, or jU � Cj, are at most the intersection of all of theoptimal sets intersected with the uncovered elements:

jU � Cj 6 jO1 \ ðU � CÞj þ jO2 \ ðU � CÞj þ � � � þ jOp

\ ðU � CÞj: ð2Þ

In the greedy algorithm, we select a set with cost effec-tiveness c, where

c 6cðOiÞ

jOi \ ðU � CÞj ;

such that,

cðOiÞP cjOi \ ðU � CÞj; ð3Þ

where i = 1. . .p. We know this because the greedy algo-rithm will always choose the set with the smallest costeffectiveness, which will either be smaller than or equalto a set that the optimal algorithm chooses. Given Eqs.(1) and (3), we have

OPT ¼X

i

cðOiÞP cX

i

jOi \ ðU � CÞj:

Applying Eq. (2), we obtain

OPT P cjU � Cj:

Then,

c 6OPTjU � Cj :

Therefore, the price of the kth element is

c 6OPT

u� ðk� 1Þ ¼OPT

u� kþ 1:

Finally, we get the total cost of the set cover is bounded by

Xu

k¼1

priceðekÞ 6Xu

k¼1

OPTu� kþ 1

¼ OPT 1þ 12þ � � � þ 1

u

� �

¼ OPT � Hu: �

6. Performance evaluations

We evaluate the performance of our framework by sim-ulating a sensing field of 10 � 10 grid points with ran-domly generated obstacles in the environment. Thedistance between adjacent grid points is 1 unit. Each gridpoint has a required coverage probability. Their requiredlowest sensing quality lies uniformly random in the rangeof [0.6,1]. In our simulations, the mobile phones and wire-less sensors share the same detection probability functionas

zðhÞ ¼ e�0:4h:

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3233

6.1. Deployment allows reconfigurations

In the first experiment, we consider a sensing campaignwith 3 mobile phone participants. A sensing campaign is asensing activity or sensing application designed to collectsensing data for a specific purpose. The term is also usedin participatory sensing, which represent a sensing appli-cation or a sensing activity that is supported by a numberof mobile phone users [2,43]. The whole campaign lasts for200 days which are divided into 10 periods. Re-deploy-ment is allowed at the beginning of each period simplyto study the necessary deployment changes in optimalsolution. We set all grid points with the same required cov-erage probability of 0.9 in this experiment. We imposesome motion patterns to the participants. The sensing fieldis divided into 4 small areas and each of them contains5 � 5 grid points. Each participant only performs sensingin their own small area out of the four. Three out of thesefour areas have participants moving around with randommotion. The remaining one is simulated as a lake, wherethe participants cannot go there.

Fig. 6 compares the coverage for the network with andwithout sensors deployed. We show the coverage satisfac-tion percentage in our figure, which means the percentageof grids that can satisfy the required coverage probabilitywhich is 0.9 here. Coverage satisfaction percentage is anindex showing the satisfaction level on coverage in sens-ing. In our algorithm, the sensors are deployed after thefirst period, such that our framework can get enough infor-mation about the participants. From the figure, we see thatonly around 30% of the grids can achieve the required cov-

Fig. 6. Comparison on coverage satisfaction pe

erage probability on average. On the other hand, about 80%of the grids on average can achieve the required coverageprobability in the network with sensors deployed. Fig. 7shows the number of sensors deployed in the campaign.The number of sensors required is around 18–20 in thiscase. It is surprising that re-deployments are not requiredso often under random motion of participants in dividedareas.

This experiment is repeated by setting a lower requiredcoverage of 0.6. Again, Fig. 8 compares the coverage satis-faction percentage of a network with and without de-ployed sensors. It shows that the coverage satisfactionpercentage becomes higher when the required coverageprobability decreases. Fig. 9 shows that the correspondingnumber of sensors deployed decreases in compare withFig. 7.

6.2. One-time deployment

In this experiment, we evaluate the performance of ourframework considering one-time sensor deployment with-out any reconfiguration. The experimental setting here isthe same as the first experiment with the required cover-age probability at 0.9. The wireless sensor network is de-ployed only once after our framework has learnedenough information in the first period. We show the cover-age satisfaction percentage after 18 sensors are deployedin Fig. 10. The figure shows that our proposed sensordeployment algorithm can achieve much better coveragesatisfaction percentage than the random deployment algo-rithm. The coverage satisfaction probability of random

rcentage with required coverage of 0.9.

Fig. 7. Number of sensors deployed with required coverage of 0.9.

Fig. 8. Comparison on coverage satisfaction percentage with required coverage of 0.6.

3234 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

deployment with equal number of sensors is also plottedfor comparison. Both our algorithm and the randomdeployment algorithm can achieve better coverage thansensing with only mobile phone participants. The resultsdemonstrate that wireless sensors can complement themobile phone participants to improve the coverage.

7. A case study with mobile traces

We further evaluate our sensor deployment with realmobile traces collected by the mobile phone participantsin Disney World (Orlando) [44,45]. The human mobilitytraces are collected with GPS receivers carried by 41

Fig. 9. Number of sensors deployed with required coverage of 0.6.

Fig. 10. Comparison on coverage satisfaction percentage with one-time deployment.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3235

participants at every 10 s. These traces are mapped into atwo dimensional area and recomputed to a position atevery 30 s by averaging three samples over that 30 s periodto account for GPS errors [44].

We monitor a 1 km � 1 km area at the center of thetheme park with our framework considering the mobiletraces of 10 h. The sensing area is divided into 10 � 10 gridcells with the grid points located at the center of each of

3236 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

them. We assume that the mobile phones and wirelesssensors share the same sensing quality. A mobile phoneor a sensor located in a grid can provide full sensing quality

Fig. 11. Coverage satisfaction probability with one

Fig. 12. Average sensing quality with one-tim

within that grid. The sensing quality degrades to only 50%in the neighboring grids and only 15% two grids away. Thesensing quality drops to 0% for grids further away. The

-time deployment at Preq = 0.5 and Qreq = 0.5.

e deployment at Preq = 0.5 and Qreq = 0.5.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3237

sensing data from mobile phones and sensors to the samegrid could complement each other to achieve higher sens-ing quality. We set the expected coverage probability Preq

Fig. 13. Coverage satisfaction probability with one

Fig. 14. Average sensing quality with one-tim

and the expected sensing quality Qreq for all grids in thisexperiment. We consider one hour as a time unit for a gridto be monitored by mobile phones and/or sensors with Preq

-time deployment at Preq = 0.7 and Qreq = 0.7.

e deployment at Preq = 0.7 and Qreq = 0.7.

3238 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

and Qreq at least once. Again, we target at one-time deploy-ment in this experiment.

7.1. Transient behaviors

We run our sensor deployment algorithm to obtain theminimum number of wireless sensors required and theirplacements. From the traces, we found that at least 9 sen-sors are required in our algorithm to satisfy the expectedPreq = 0.5 and Qreq = 0.5 over the sensing field. Fig. 11shows the coverage satisfaction probability of the gridcells over time after deploying the sensors. The resultsshow that our sensor deployment can always guaranteea satisfaction coverage probability greater than 0.5, whileuniform and random deployments with same number ofsensors can satisfy this requirement only in certain hours.The coverage probability of the field without sensors isalso plotted for comparison. Similarly, the average sens-ing quality of the grids is shown in Fig. 12. It demon-strates that our deployment can always provide the bestaverage sensing quality among the three differentdeployments.

We then repeat the experiment by increasing both theexpected Preq and Qreq to 0.7. We found that at least 13 sen-sors are required to satisfy the expected Preq = 0.7 andQreq = 0.7 over the sensing field. Fig. 13 shows the coveragesatisfaction probability of the grid cells over time afterdeploying the sensors. Again, the results show that oursensor deployment can always guarantee a satisfactioncoverage probability greater than 0.7, while not alwaysthe case for uniform and random deployments. The cover-age probability of the field without sensors is also plotted

Fig. 15. Coverage satisfaction probability with one

for comparison. Fig. 14 demonstrates that our deploymentcan always provide the best average sensing quality amongthe three different deployments. The results of our algo-rithm here can achieve higher satisfaction probabilityand coverage compared with Figs. 13 and 14.

We set the expected Preq = 0.9 and Qreq = 0.9 for a higherlevel of requirements. It turns out that 18 sensors are re-quired for our algorithm to achieve these requirements.Fig. 15 shows that our sensor deployment can always guar-antee a satisfaction coverage probability greater than 0.9.Although 18 deployed sensors can greatly increase the sat-isfaction coverage of uniform and random deployments,they are not able to satisfy the expected Preq = 0.9 andQreq = 0.9 requirement in certain all hours. Similarly, theaverage sensing quality of the grids is shown in Fig. 16.Again, it demonstrates that our deployment can achievethe best average sensing quality among the three differentdeployments. In summary, the above experiments showthat our algorithm out perform both uniform deploymentand random deployments, which are unable to providethe required sensing performance even with the samenumber of stationary sensors.

7.2. Varying percentage of training data

We show the performance of our sensing system by uti-lizing only a subset of the mobility traces as training datain this experiment. All of the available mobility traces re-mains as testing data to evaluate the satisfaction coverageprobability and average sensing quality after sensordeployment. Only 10%, 25% and 50% of the availablemobility traces are randomly selected as the training data.

-time deployment at Preq = 0.9 and Qreq = 0.9.

Fig. 16. Average sensing quality with one-time deployment at Preq = 0.9 and Qreq = 0.9.

Fig. 17. Coverage satisfaction probability at Preq = 0.9 and Qreq = 0.9 with different percentage of training data.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3239

Figs. 17 and 18 show that our system can achieve compa-rable satisfaction coverage probability and average sensingquality even with reduced percentage of training data. It

implies that a small number of training data is representa-tive to a greater testing dataset for sensor deployment inour system.

Fig. 18. Average sensing quality at Preq = 0.9 and Qreq = 0.9 with different percentage of training data.

Fig. 19. Coverage satisfaction probability at Preq = 0.5 and Qreq = 0.5 with reduced grid size.

3240 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

7.3. Reduced grid size and sensing capability

We repeat our experiments with more fine-grained gridcells of size 50 m � 50 m. We set Preq = 0.5 and Qreq = 0.5.

The sensing coverage of both mobile phones and sensorsare reduced to half of the original. We found that at least15 sensors are needed to achieve the required sensingquality and coverage with our deployment algorithm. Figs.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3241

19 and 20 show the coverage satisfaction probability andaverage sensing quality of different sensor deploymentalgorithms with 15 sensors. The results indicate that ouralgorithm works better than both uniform and random

Fig. 20. Average sensing quality at Preq = 0.5

Fig. 21. Coverage satisfaction probability at Preq = 0.5 and Qreq =

sensor deployments. Compared with Figs. 11 and 12, thisexperiment achieves lower satisfaction coverage probabil-ity and average sensing quality even with more deployedsensors due to the reduced sensing capability of nodes.

and Qreq = 0.5 with reduced grid size.

0.5 with heterogeneous nodes in changing environment.

3242 Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245

7.4. Heterogeneous sensors in changing environment

We repeat our experiments to evaluate the sensing per-formance of heterogeneous nodes in a changing environ-

Fig. 22. Average sensing quality at Preq = 0.5 and Qreq = 0.5

Fig. 23. Coverage satisfaction probabi

ment. Fifty percentage of the mobile phones havereduced sensing quality to only half of the original due totheir weaker sensing capability. Moreover, both the mobilephones and stationary sensors may have sensing quality

with heterogeneous nodes in changing environment.

lity varying number of sensors.

Fig. 24. Average sensing quality varying number of sensors.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3243

degradation at different moments due to the changingenvironment. Twenty percentage of them may have a qual-ity degradation of 20%. Another 30% of them may have aquality degradation of 30% during the experiment. Figs.21 and 22 show the coverage satisfaction percentage andaverage sensing quality at Preq = 0.5 and Qreq = 0.5. Fromthe results, 9 sensors are no longer enough to achieve therequired Preq when there is quality degradation due tothe heterogeneous nodes in changing environment. Aftertaken the potential degradation into account for thedeployment, we find that 10 sensors are needed to achievethe required sensing coverage and quality.

7.5. Varying number of deployed sensors

Next, we examine the satisfaction coverage probabilityand the average sensing quality varying the number of sen-sors (see Figs. 23 and 24). Our deployment can alwaysachieve higher satisfaction probability and average sensingquality than both uniform and random sensor deploy-ments. The results confirm that our sensor deploymentalgorithm can reduce the number of sensors effectively,while guaranteeing satisfactory sensing coverage and sens-ing quality.

8. Conclusions and future works

We propose a framework for wireless sensor networkdeployment in mobile phone assisted environment. Wesuggest that wireless sensors and mobile phone

participants can perform sensing collaboratively and com-plement each other. Our framework predicts the sensingquality of the mobile phone participants considering theirmobility and sensing behaviors. Then, it provides wirelesssensor deployment minimizing the number of sensors,while guaranteeing satisfactory sensing quality and cover-age. Our framework includes several sub-models which of-fers high level of flexibility. It can adapt to different kinds ofsensing campaigns by replacing any of the sub-modelsaccordingly. Extensive evaluations with real mobile traceshave shown that our framework can provide good coverageand sensing quality in most of the grid points with smallnumber of additional wireless sensors. We believe thatthe performance of our framework will improve further ifwe understand the behavior and motion patterns of the par-ticipants thoroughly in real campaigns.

Acknowledgments

This work was supported by the VINNMER Program andUppsala VINN Excellence Center WISENET funded by VIN-NOVA, Sweden. J. Liu’s work was supported by a CanadaNSERC Discovery Grant and an NSERC Strategic ProjectGrant.

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Zheng Ruan is currently a Master student inthe Department of Information Technology,Uppsala University, Sweden. He received hisBachelor degree from the Department ofComputer Science and Technology, HefeiUniversity of Technology, China in 2006. Hisresearch interests include wireless sensornetworks and mobile computing.

Z. Ruan et al. / Computer Networks 55 (2011) 3224–3245 3245

Edith C.-H. Ngai is currently an AssistantProfessor in Department of InformationTechnology, Uppsala University, Sweden. Shereceived her Ph.D. from The Chinese Univer-sity of Hong Kong in 2007. She did her post-doc in Department of Electrical and ElectronicEngineering, Imperial College London, UnitedKingdom. Her research interests includewireless sensor networking, mobile comput-ing, network security and content-basedrouting. Previously, she has conductedresearch in Simon Fraser University in Canada,

Tsinghua University in China, Intelligent Systems & Networks (ISN) Groupin Imperial College London, and Networked and Embedded SystemsLaboratory (NESL) in UCLA.

Jiangchuan Liu is an Associate Professor inthe School of Computing Science at SimonFraser University, British Columbia, Canada.From 2003 to 2004, he was an Assistant Pro-fessor in the Department of Computer Scienceand Engineering at The Chinese University ofHong Kong. He was also a Microsoft ResearchFellow, and worked at Microsoft ResearchAsia (MSRA) in the summers of 2000, 2001,2002, and 2007. His research interests are innetworking, in particular, multimedia com-munications, overlay/peer-to-peer network-

ing, and wireless sensor/mesh networking. He is an Associate Editor ofIEEE Transactions on Multimedia.