114 IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 6, …mmlab.snu.ac.kr/~mchen/min_paper/2018/2018-IEEE-TCC-4-WSN-Zz.pdf · Digital Object Identiﬁer no. 10.1109/TCC.2015.2458272 1

Energy Efficient Cooperative Computingin Mobile Wireless Sensor Networks

Zhengguo Sheng ,Member, IEEE, Chinmaya Mahapatra, Student Member, IEEE,

Victor C. M. Leung, Fellow, IEEE, Min Chen , Senior Member, IEEE, and

Pratap Kumar Sahu,Member, IEEE

Abstract—Advances in future computing to support emerging sensor applications are becoming more important as the need to better

utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices

and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this

paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain

completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be

optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint

optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose

energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance

analysis is supplemented by simulation results to show the significant energy saving of the proposed solution.

Index Terms—Edge and cloud computing, mobile wireless sensor networks, cooperation

Ç

1 INTRODUCTION

CLOUD computing [1], [2], [3] has been proposed as anefficient and cost effective way of providing highly

scalable and reliable infrastructures and services. The keyidea of cloud computing is to create a pool of shared, visual-ized, dynamically configurable and manageable resourcesacross computing devices, networks, servers and data cen-ters, which can deliver on demand services to users over theInternet [4]. However, existing cloud computing models aredesigned for traditional web applications, rather than futureInternet applications running on various mobile and sensornodes. Particularly as we go to the era of Internet of Things(IoT) with one trillion endpoints worldwide, that createsnot only a real scalability problem but the challenge of deal-ing with complex clusters of endpoints, rather than dealingwith individual endpoints. Moreover, public clouds, as theyexist in practice today, are far from the idealized utility com-puting model, since it makes their network distance too farfrom many users to support highly latency-sensitive

applications. This is particularly true for applications thatare developed for a particular provider’s platform and run-ning in data centers that exist at singular points in space.

In contrast to the cloud, edge computing [5], which runsgeneric application logic on resources throughout networks,including routers and dedicated computing nodes, hasattracted a lot of attention and been considered as a comple-mentary of cloud computing to distribute intelligence innetworks, and allows its resources to perform low-latencyprocessing near the edge while latency-tolerant, large-scopeaggregation can still be efficiently performed on powerfulresources in the core of the cloud.

In a simple, topological sense, edge computing worksin conjunction with cloud computing, optimizing the use oftheir resource. Users can subscribe services via the cloudcomputing platform which can offer diverse storage andcomputation capabilities from both central server and dis-tributed nodes, respectively. Today, with the developmentof wireless technologies and embedded processor, the edgecomputing capability can be largely extended to a broadrange of wireless devices, such as smartphone and wirelesssensor nodes, to support flexible services.

Particularly, wireless sensor nodes, which are commonlywith a radio transceiver and a microcontroller powered by abattery, as well as diverse novel sensors, are in the creationof imaginative pervasive computing applications. We havealready witnessed that smartphones, such as iPhone andAndroid, can replace a normal desktop or a server runninga dual core processor for computation [6], [7]. Moreover, therecent advancement of small size and low cost sensor plat-forms such as WRTnode1 and Arduino [8], which can offer

� Z. Sheng is with the School of Engineering and Informatics, University ofSussex, Brighton BN1 9RH, United Kingdom.E-mail: [email protected].

� C. Mahapatra and V.C.M. Leung are with the Department of Electricaland Computer Engineering, University of British Columbia, Vancouver,BC V6T 1Z4, Canada. E-mail: {chinmaya, vleung}@ece.ubc.ca.

� M. Chen is with the School of Computer Science and Technology,Huazhong University of Science and Technology, Wuhan 430074, China.E-mail: [email protected].

� P.K. Sahu is with the Department of Computer Science and OperationResearch, University of Montreal, Montreal, QC H3T 1J4, Canada.E-mail: [email protected].

Manuscript received 5 Jan. 2015; revised 18 May 2015; accepted 6 July 2015.Date of publication 17 July 2015; date of current version 7 Mar. 2018.Recommended for acceptance by P. Corcoran.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference the Digital Object Identifier below.Digital Object Identifier no. 10.1109/TCC.2015.2458272

1. WRTnode is an open source development board with a wirelesscontroller based on OpenWrt. It is suitable for speech/video recogni-tion, Open CV and Machine Learning, etc. http://wrtnode.com/.

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CPU clock speeds of up to 600 MHz and low power IEEE802.11/15.4 radios, are capable of connecting external sen-sors (e.g., camera sensor, thermal sensor, heartbeat sensor,air pollution sensor, etc.) to support attractive lightweightsensing applications in various domains, such as environ-mental monitoring [9], social networking [10], healthcare [11]and transportation [12], etc. In addition to the developmentof efficient software [13] and communication protocols, e.g.,Constrained Application Protocol (CoAP) [14], [15], theformed wireless sensor networks can truly enable the newlyemerging sensor-as-a-service (SaaS) paradigm. Anothermotivation to leverage the sensor-based computing infra-structure is that IoT applications are rapidly developed in anumber of areas, ranging from personal devices to industrialautomations. The existing cloud infrastructure can benefitfrom significant energy savings by offloading tasks to power-ful sensor nodes. We believe that such an emerging dissemi-nation of wireless sensor networks and cloud computing canbring new opportunities of sensor and cloud integration,which will facilitate clients to not only monitor and collectdata from the environment, but also execute and output sen-sor applications using their own processing capabilities.Fig. 1 gives our prediction of cloud computing evolution to alarge scale and distributed sensor-as-a-service infrastructure.

There have been a number of technical challenges to buildsuch a sensor-based computing infrastructure. In particular,the biggest hurdle to harness sensor nodes for computing isthe battery life. In this paper, we investigate fundamentalcharacteristics of mobile wireless sensor networks (MWSN)computing in terms of energy efficiency and propose a novelapproach to optimize total energy consumption of pro-cessing an application, while satisfying a certain completiondeadline requirement. Specifically, we firstly introducethe concept of cooperative computing which encourages sin-gle nodes to share their resources cooperatively such that avirtual resource pool can be constructed. Fig. 1 also shows anexample of the cooperative computing serving client servicerequests from outside world. Moreover, by assuming theapplication profile with a limited size of input data and acompletion deadline, the proposed solution can jointly con-sider computation and communication costs as a whole, and

optimally partition, offload and execute workload betweensensor nodes to boost energy efficiency of the edge comput-ing. Based on these analytical results, we further proposeenergy efficient cooperation strategies for resource allocationin networks withmultiple sensor nodes.

The following summarizes our contributions and keyresults:

� We introduce mathematical models to characterizeapplication profile, computation and communicationenergy. Especially, the derived closed-form solutionof energy consumption is highly related to the inputdata size and completion deadline. Moreover, byconsidering the mobility nature of MWSN, the pro-posed solution can ensure the energy performancewith minimum transmission time.

� Wepropose an optimal partition tominimize the totalenergy consumption required by local and remotesensor nodes in cooperative computing under staticchannel model to satisfy a given deadline require-ment. Furthermore, an offloading decision rule isdefined to indicate the best computing strategy.More-over, under the optimal partition, our analysis showsthat the required energy consumption of a remotenode (helper) is always smaller than that of a localnode, a result which lays a foundation to encouragethe cooperative behaviors which means that the help-ing part only needs to spend relatively small amountof energy than the one seeking help from others.

� By utilizing the optimal results, we propose energyefficient cooperation node selection strategies toachieve fairness and maximal energy saving ina multi-node environment, and analyze node’s“willingness” to cooperate when selfish and unself-ish natures are imposed to individuals. Simulationresults are supplemented to illustrate the significantenergy savings of the proposed strategies in provid-ing reliable services.

This paper is organized as follows. The literature reviewof related work is given in Section 2. The system model andproblem formulation are introduced and derived in

Fig. 1. Evolution from cloud computing to edge computing.

SHENG ET AL.: ENERGY EFFICIENT COOPERATIVE COMPUTING IN MOBILE WIRELESS SENSOR NETWORKS 115

Section 3. The optimal cooperative computing scheme ispresented and analyzed in Section 4. The energy efficientcooperation node selection strategies are proposed inSection 5. Simulation results are provided in Section 6.Finally, concluding remarks are given in Section 7.

2 RELATED WORK

Cloud computing has been intensively investigated basedon off-the-shelf cloud infrastructures, such as resources/traffic optimization of backhaul networks [16], servicesadmission control [17] and scheduling [18], and pricingstrategies of using commerce cloud services [19], [20], etc.However, existing cloud computing models are designedfor traditional web applications, rather than future Internetapplications running on various mobile and sensor nodes.

Due to the emerging development of mobile Internet,more recent works show great interests in dealing withmobile applications in the context of cloud computing. Somestate-of-art literature [3], [21] in mobile cloud computing(MCC) reveal that the energy issue is one of the major chal-lenges. The issue of energy efficiency in future computinghas also been extended to the sensor cloud computing.Alamri et al. in [22] provide a comprehensive survey of sen-sor cloud architecture, approaches and applications. Pereraet al. in [23] propose a middleware design for IoT and bal-ance the computation and communication energy betweensensor nodes and cloud servers. Yuriyama and Kushida in[24] propose the concept of virtual sensors by collecting dif-ferent vertical application data into a single horizontal plat-form which can behave as a sensor node to reduce extracommunication between networks. There are also sensorcloud applications in body sensor networks [25] and truckmonitoring [26], etc. Although various sensor cloud schemeshave been developed to increase bandwidth efficiency, thesensor nodes are usually assumed as data collecting pointsand there is lack of understanding of their processing capa-bility and the potential benefits of being a computing node.

As a contrary, edge computing [27], [28] has been con-sidered to provide computing, storage, and networkingservices between end devices and traditional Cloud Com-puting data centers, typically, but not exclusively locatedat the edge of network. A comparable concept has alsobeen proposed by Cisco with the name of fog computing[29]. Since mobile sensor nodes now rival many PCs interms of computational power [6], they have the opportu-nity to talk directly to one another when possible andhandle much of their own computational tasks. Moreover,an emerging wave of sensor applications, requires mobilitysupport and geo-distribution in addition to location aware-ness and low latency. In our preliminary study [30], wehave already developed a prototype to connect an on-board diagnostics (OBD) sensor device to the cloud viasmart phone, where the data analysis engine can bedeployed in either smart phone or cloud, depending onthe size of the processing data and service requirements. Asimilar concept of crowd computing [31] has also been pro-posed to bring together the strengths of crowdsourcing,automation and machine learning.

In this paper, our contribution is to further leverage thecomputation capability of sensor nodes into computing and

consider a joint optimization of both computation and com-munication energy across mobile wireless sensor nodes totransfer and process sensor data and disseminate results toappropriate parties. To the best of our knowledge, this isthe first work that considers sensor node as a service andrealizes cooperative computing in MWSN.

3 SYSTEM MODEL AND ENERGY FORMULATION

In this section, we introduce the application model, itsexecution on sensor node and transmission over wirelesschannel. Specifically, the energy consumption of both com-putation and communication are formulated.

3.1 Application Model

We consider the cooperative computing in which each sen-sor node can execute lightweight applications with the helpof peer sensor nodes. In order to characterize an application,a canonical model [32] that captures the essentials of a typi-cal application is considered and can be abstracted into thefollowing two parameters:

� Input data size L. The total number of data bits asthe input of an application. Such input data can bepartitioned and offloaded to a peer sensor node forremote processing and execution [33].

� Application completion deadline T . The maximumnumber of time slots that an application must becompleted. t is the discrete time index ranging fromt ¼ 1 to t ¼ T .

It is worth noting that an application is a program thatperforms a computation on an input file, such as calculatingthe number of violate data from a period of history record.Similar to themodel applied inMapReduce [34], we considerthat an application can be breakable into tasks which do notexhibit dependencies across partitions of its input. Weassume that all sensor nodes are capable of executing a sameapplicationwithout need to transfer executable files for oper-ation, thus only the input partitions are transmitted to othersensor nodes for parallel executions. Although there arecases that some tasks cannot be broken into smaller piecesand can only be executed on a single node due to the depen-dencies in its input, there are still concurrency benefits whenmany such tasks are executed in batches.

The energy consumption of an application is highlyrelated to these two parameters. For example, with a largesize of input data and stringent completion deadline, a sen-sor node may consume extensive energy. In the following,we denote such an application as AðL; T Þ and use it to char-acterize the energy consumption of computation and com-munication, respectively.

3.2 Computation Energy Consumption

The energy consumption of computation is directly deter-mined by the CPU workload of an application. Accordingto [35], the workload can be measured by the number ofrequired CPU cycles, which is related to the input data sizeand computation complexity, and is defined as

W ¼ LX ; (1)

where W is the total number of required CPU cycles, L isthe input data size and X is the computation algorithm. It is


noted that with a same size of input data, the required cycledemands often vary greatly, which depend on the nature ofapplications [36], e.g., applying an input data to calculatethe average and factorial show distinct computation com-plexities. In the existing literature [36], [37], [38], X has beenshown as a random variable and can be modeled by aGamma distribution which is commonly used to model ser-vice times [39], and has been shown to work well in charac-terizing the distribution of CPU cycle demands [32], [37].

Although a number of factors consume CPU power, suchas short circuit power and dynamic power, etc., the energyconsumption is dominated by dynamic power which can beminimized by configuring the clock frequency of the chipvia the dynamic voltage scaling (DVS) technology2 [40].As a result, the total energy consumption of computation isgiven by

Ec ¼XWw¼1

�cðwÞ ¼XWw¼1

kf2w ; (2)

where �c is the computation energy per operation cycle, k isthe effective switched capacity determined by the chiparchitecture and fw is the clock-frequency which is sched-uled in the next CPU cycle given the number of w CPUcycles have been completed.

A careful reader may notice that the CPU can reduce itsenergy consumption by scheduling low clock frequency.However, as a practical implementation, the application hasto meet a completion deadline. Without deadlines, there isno particular reason to complete any given task by a certaintime. We use the soft deadline to characterize probabilisticperformance, that is, the statistical CPU scheduling model[32] which assumes the application completion needs tomeet its deadline with the probability p by allocating Wp

CPU cycles. The parameter p is the application completingprobability (ACP). In other words, the probability of anapplication requires no more than the allocated Wp shouldsatisfy FW ðWpÞ ¼ Pr½W � Wp� � p. This soft real-timescheduling integrated with DVS has been shown its effec-tiveness in saving energy without substantially affectingapplication performance [36].

According to (1), sinceW is a linear function ofX, we can

obtain Wp ¼ LF�1X ðpÞ, where F�1

X ðpÞ is the inverse cumula-tive distribution function of X. Therefore, the total energyconsumption can be derived as

Ec ¼ kXWp

w¼1

FcW ðwÞf2w ; (3)

where FcW ðwÞ is the complementary cumulative distribution

function (CCDF) that the application has not completedafter w CPU cycles. Since the Gamma distribution is expo-nentially tailed, the CCDF can be assumed as Fc

W ðwÞ �me�nw for some constants m > 0 and n > 0. It is noted that

with w ! 1, the probability goes to 0, which means it isunlikely that an application cannot be completed with alarge number of CPU cycles.

Theorem 3.1 [32]. For the optimal clock-frequency schedulingin each CPU cycle (fw) and the deadline requirement

(PWp

w¼1 1=fw � T ), the minimum computation energy can bederived as

E�c ¼ k

T 2

XWp

w¼1

½FcW ðwÞ�1=3

( )3

; and also has E�c � L3:

Simplifying the above result, we have the optimal com-putation energy as

Ec ¼ KL3

T 2: (4)

where K is a constant factor determined by k and p. Theresult tells that allocating a smaller size of input data or relax-ing the deadline can achieve better computational energyefficiency, which means that a sensor node can prefer to exe-cute delay tolerant applications or offloadmore tasks to peernode, in order to save its own computation energy.

3.3 Communication Energy Consumption

When a sensor node considers to offload its tasks to a peernode, the energy consumption is determined by the numberof bits being transmitted over wireless channel.

The energy consumption of communication is deter-mined by the current draw of the electrical circuits thatimplements the physical communication layer. In practice,it includes idle, transmit and receive modes. According tothe specifications of IEEE 802.11n [41] or IEEE 802.15.4 [42],[43], the energy consumption of a wireless sensor node isdominated by the transmit or receive modes, and has theircosts are approximately the same. So we consider the com-munication energy including both transmission and recep-tion of processing tasks, and do not consider the smalloutput results3 from the node.

The communication cost is characterized by the empiricaltransmission energy model [45], and the required energy Et

to transmitL bits is governed by a convexmonomial function4

Et ¼ rLn

g; (5)

where r denotes the energy coefficient, g denotes channelstate and n denotes the order of monomial with value1 � n � 5. According to [45], the choice of n depends on thebit scheduler policy.With an increasing value of n, the sched-uler more prefers to transmit equal number of bits at everytime slot regardless of the channel state. However, in thispaper, we consider the optimal case of n ¼ 1 which is calledone-shot policy [47], in which the transmission only depends

2. DVS is commonly used to save CPU energy by adjusting thespeed based on required cycle demands. It exploits an important char-acteristic of complementary metal oxide semiconductor (CMOS)-basedprocessors: When operation is at low voltage, the clock frequency scalesas a linear function of the voltage supply. The energy consumed percycle is proportional to the square of the voltage.

3. This is a reasonable assumption for sensor computing where mostof sensor based applications come with simple results of warning orimage detection indication [44], etc.

4. Although the monomial cost does not hold for operation at capac-ity in AWGN channel, there is a practical modulation scheme to wellapproximate by a monomial [46].


on the channel state and is completed in one time slot. Thereare several reasons for applying this scenario: First, for energyconstrained sensor node, it may not be desirable to split a sin-gle data across multiple time slots because of extra energyconsumed by a large overhead associated with each slot. Sec-ond, since we impose a deadline to complete an application,the transmission time should be relatively small compared toT , such that the time offset between local and remote execu-tions can be negligible. Third, in MWSN, the transmissiontime over the air should be minimized to avoid channel fluc-tuation caused by node mobility. In order to ensure the opti-mal performance of the policy, the scheduler should beopportunistic, that is, to offload tasks to a peer node withgood channel quality. The scheduling policy has been provedits effectiveness in combating channel fading [6].

4 ENERGY OPTIMIZATION FOR COOPERATIVE

COMPUTING

Our interest is to find an optimal partition to minimize thetotal energy consumption of processing an applicationgiven that a target completion deadline T is satisfied byusing cooperative computing, and can be formulated as

minll;lr

Elcðll; tÞ þ Etðlr; gl;rÞ|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

local energy cost

þErðlr; gr;lÞ þ Ercðlr; tÞ|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

remote energy cost

s:t: ll þ lr ¼ L

t � T :

(6)

� Elc and Er

c denote the local and remote computationenergy consumption, and Et and Er denote transmis-sion and reception energy consumption, respectively.

� ll and lr are partitioned input data size for local andremote processing. A symmetric channel is assumedbetween local and remote sensor nodes and haschannel gain gl;r ¼ gr;l. A completion deadline T isconsidered to ensure quality-as-service.

Theorem 4.1. The optimal input data partition to minimize thetotal energy consumption of processing an application AðL; T Þin MWSN, is given by

l�l ¼L

2þ b

6aL; l�r ¼

L

2� b

6aL: (7)

The correspondingminimum total energy consumption is

Etotalðl�l ; l�rÞ ¼aL3

4þ bL

2� b2

12aL: (8)

where a ¼ KT2, b ¼ 2r

gl;r, K denotes the computation coeffi-

cient, r denotes communication coefficient of wireless chan-nel and gl;r is the channel gain.

Proof. See Appendix A. tuIn general, we find that the minimum total energy con-

sumption can be achieved by optimally partitioning, offload-ing and executing the input data via cooperative computing,which can be determined by the application profile, hard-ware configurations of sensor nodes and wireless channelconditions. In the following, we further investigate the

individual behaviors of the optimal partition and analyzehow the system parameters affect the overall performance.

Proposition 4.2. The size difference of the optimal input databetween local and remote executions is

diffðL; T;K; r; gl;rÞ ¼ 2rT 2

3gl;rKL: (9)

Proof. The result follows (7) and has diff ¼ b3aL. Using

a ¼ KT2 and b ¼ 2r

gl;rinto the result leads to (9). tu

In essence, we can observe that the optimal partitionhighly depends on system parameters. Specifically, the localexecution is preferable when (9) tends to increase (i.e., smalldata size L, long completion deadline T , high transmissioncost r, low computation cost K or high channel loss gl;r).Otherwise, the remote execution is preferable.

Result 4.3. By defining the application processing speedas y ¼ L

T , we have the equivalent energy optimal com-puting rules

Local computing; if 0 < y �ffiffiffiffiffiffiffiffiffiffi2r

3Kgl;r

q;

Cooperative computing; ifffiffiffiffiffiffiffiffiffiffi2r

3Kgl;r

q< y �

ffiffiffiffiffiffiffiffiffiffiffiffiffi2rffiffi3

pKgl;r

q;

Cloud computing; ifffiffiffiffiffiffiffiffiffiffiffiffiffi

2rffiffi3

pKgl;r

q< y:

8>>>><>>>>:

(10)

Proof. According to (7), since L2 � l�l � L, we should have

0 � b6aL < L

2 for data offload. Thus, the lower bound of

application processing speed can be achieved as

L

T>

ffiffiffiffiffiffiffiffiffiffiffiffiffi2r

3Kgl;r

s; (11)

when b6aL � L

2, only the local computing is applied. Fur-thermore, considering the cloud computing case wheresensor nodes only collect and transmit input data to anenterprise cloud server via gateway for execution, weobtain the upper bound condition of using cooperativecomputing from (5) and (8) as

Etotalðl�l ; l�rÞ � Et ) aL3

4� b2

12aL) L

T�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2rffiffiffi3

pKgl;r

s: (12)

tuWe observe that when the application prefers cooperative

computing, it can always achieve better energy efficiencythan the local computing case. Moreover, the cloud comput-ing is only appliedwhen high processing speed is required.

Proposition 4.4. For cooperative computing, the bound perfor-mance of computation to communication energy ratio of localand remote nodes are

Elc

Et� Kgl;r

4ry2 � Er

c

Er: (13)

Proof. See Appendix B. tu


The result tells that the proposed optimal solution can bestachieve the local computation energy and have the minimalenergy ratio. Assuming the application processing speed canbe closed to its lower bound in (11), the local computationenergy can be achieved as close as 1=6 of transmissionenergy, which is promising to show the advantage of usingcooperative computing in MWSN. Such ratio can be higher,depends on the preference of application processing strategyand system parameters. Moreover, the cooperative comput-ing can also help reduce the remote computation energy.Especially, with fewer offloading bits, the remote computa-tion energy decreases at a faster pace than the communica-tion energy. The upper bounded is govern by the systemparameters and maximum number of bits can be offloaded.The result is usefulwhen selfish nature is imposed to individ-uals, because reducing the energy consumption for helpingnodes can largely improve their willingness of cooperation.

In addition to the individual performance, we also pro-vide an analytical result to characterize the average perfor-mance of the optimal solution. We assume that sensorcandidates are randomly located in space according to aPoisson point process with density �. An application initialnode (IN) will choose the best cooperation node (CN) toachieve the minimum total energy among all available can-didates. A network with a higher density of sensor nodescan have better choices to select CN.

We consider a simple channel model where gi;j betweenthe nodes i and j is modelled as gi;j ¼ 1=dai;j, where di;j is the

distance between the nodes i and j, a is the path-loss expo-nent and usually characterized as an integer value a � 2.Given the identical system parameters (i.e., K and r), theselected CN distance to achieve the minimum Etotal will beas close as possible to the IN. We let r� be a random variableof the selected CN distance to the IN, and r denote the dis-tance between the closest CN and the IN. The probabilitydistribution function of r is given by

Pr½r� < r� ¼ 1� Pr½r� � r�¼ 1� Pr½Nr ¼ 0� ¼ 1� e��pr2 ;

(14)

where Nr is the number of CN within distance r from theIN. The probability density function (pdf) of the selectedCN distance is

fðrÞ ¼ 2�pre��pr2 ; r � 0: (15)

According to (8) and (15), the expected value of the opti-mal energy consumption is

E Etotal½ � ¼ 2�p

Z 1

0

aL3

4þ rLra � r2r2a

3aL

� �re��pr2dr

¼ 2�p

� Z 1

0

aL3

4re��pr2drþ

Z 1

0

rLraþ1e��pr2dr

�Z 1

0

r2r2aþ1

3aLe��pr2dr

�

¼aL3

4 þ rLa!!

2aþ12 ð�pÞa�1

2

ffiffi1�

q� r2a!

3aLð�pÞa ; if a is odd;

aL3

4 þ rLða2Þ!ð�pÞ

a2� r2a!

3aLð�pÞa ; if a is even;

8>><>>:

(16)

where !! is the double factorial and have a!! ¼ Qðaþ1Þ=2i¼1

ð2i� 1Þ. Fig. 2 illustrates the optimal energy comparison ofthe simulation result and numerical analysis (16) underdifferent path-loss exponents. We can learn from the resultis that with an increasing value of �, there is a better chanceto select a well positioned sensor node to improve theenergy efficiency of the proposed solution. Moreover, witha higher loss of the channel, the optimal solution tends tolocal computing, which increases the energy consumption.In the following, we focus on a multi-node scenario anddesign energy efficient CN selection strategies.

5 ENERGY EFFICIENT COOPERATION NODE

SELECTION STRATEGIES IN MWSN

In this section, we consider a more general network settingwhere multiple nodes co-exist and cooperate with eachother by acting as CN in cooperative computing.

We are interested in finding a strategy that each applica-tion IN determines which node to select as the CN for themaximal energy efficiency in the multi-node environment.It is noted that CN selections affect the overall energy con-sumption, since the optimal energy cost depends upon theapplication requirement, sensor hardware configurationand channel condition of the selected CN. In the following,we summarize the system parameters and assumptions forthe multi-node environment.

� The network consists of a set of nodes N ¼ f1; :::; ng,where each node i 2 N processes a number of appli-cation tasks over time. For simplicity, we assume alltasks have the same application profile AðL; T Þ,though it is straight-forward to derive CN selectionrules in a more general setup. We also assume thattime is divided into discrete time slots.

� One sensor node can only execute one applicationtask at a given time slot and we do not considermulti-tasking scenarios.

� We denote by EINi;j ðtÞ the energy cost of a node i given

that the node j is chosen as the CN at time t for coop-erative computing. Meanwhile, the energy cost of

the CN j is denoted as ECNi;j ðtÞ. We assume that the

Fig. 2. Comparison of optimal energy consumption between simulationand numerical analysis: L ¼ 1; 024 bits, T ¼ 20ms, r ¼ 0:006 andK ¼ 10�10.


node i and j follow the optimal partition given by (7)for each executions, and both computation and com-munication energy can be estimated by (4) and (5),respectively.

� When node i uses local computing at time t, we

denote its energy cost as ELi ðtÞ.

� Energy consumption of a node Eiðt1 : t2Þ during atime interval ½t1 : t2� is the sum of node i’s executionenergy either as IN or CN, and communicationenergy either transmission or reception over allt 2 ½t1; t2� (we assume a node consumes zero-energyat t if it is neither an IN or a CN at t).

� We denote RiðtÞ as the set of the nodes (except nodei) which can achieve energy saving comparedto the local computing, for processing node i’s appli-

cation at time t, i.e., RiðtÞ ¼ fj 2 N � figjEINi;j ðtÞ þ

ECNi;j ðtÞ < EL

i ðtÞg.

5.1 Minimum Total Energy CN Selection

Our first CN-selection strategy makes use of the result inprevious section in a straightforward manner:

Min-Total-Energy-Strategy. A CN is selected for node i attime t such that

CNiðtÞ ¼ arg minj2RiðtÞ

fEINi;j ðtÞ þ ECN

i;j ðtÞg:

In other words, for each processing task from node i, a

CN j is selected, which minimizes EINi;j ðtÞ þ ECN

i;j ðtÞ among

those inRiðtÞ. IfRiðtÞ ¼ ;, CNiðtÞ ¼ null.

Result 5.1. For a given time interval of ½t1; t2�, the totalenergy consumption of MWSN

Pi2N Ei½t1; t2� is mini-

mized if each i is assigned a CN at each time by the Min-

Total-Energy-Strategy, i.e., CNiðtÞ ¼ argminj2RiðtÞEINi;j

ðtÞ þ ECNi;j ðtÞ.

Proof. Since we only consider the total energy consump-tion, the whole cooperative computing process can bescheduled into several rounds and each node can onlyprocess no more than one application task in eachround. Since any assignment CNi is injective in eachround, for any two nodes i and k, Si \ Sk ¼ ;, and[i2NSi N , where Si is a set of IN whose CN is i.Therefore, the total energy consumption in each roundP

i2N Ei ¼P

i2NðEINi;jþ

Pj2Si E

CNj;i Þ can be re-written asP

i2N EINi;j þ

Pi2N

Pj2Si E

CNj;i ¼ P

i2N EINi;CNi

þPj2N ECN

j;CNj¼P

i2NðEINi;CNi

þ ECNi;CNi

Þ, which is minimized if each indi-

vidual term isminimum. tuIt is worth noting that this result is straightforward and

obtainable from the optimal partition in (7), since the CNselection is based only on the energy consumption of itselfand other potential CN nodes for the upcoming task at eacht, but not on the past energy consumptions of itself or othernodes. However, it is clear that, though simple, the Min-Total-Energy-Strategy is optimal in the sense that it mini-mizes the total energy consumption of MWSN.

However, from the individual’s perspective, we mayargue that the CN selection can lead to the situation thatsome nodes end up with higher energy consumption than

the case when all nodes employ local computing. This isespecially true if some unfortunate nodes are heavilyselected as CN and hence consume more energy in coopera-tion than that saved from its own processing as an IN. Inthe following, we consider how to handle such unfairnessin cooperative computing.

5.2 Fairness CN Selection

Our strategy is to let each node act as a CN only when it hassaved more energy than that it has lost from cooperativecomputing in the past. Thus, we define the following func-tions to indicate the availability of each node.

5.2.1 Utility Function

To represent how much energy saving of cooperativecomputing can yield in comparison to local computing, weintroduce the concept of “utility” of nodes.

The utility function, uiðtÞ, of node i at time t is defined as

uiðtÞ ¼EL

i ðtÞ � EINi;j ðtÞ; if 9j s:t:; j ¼ CNiðtÞ;

�ECNj;i ðtÞ; if i ¼ CNjðtÞ for some IN j;

0; otherwise:

8<:

(17)

The above function represents how much energy a node ilocally saves (or loses) compared to local computing at time

t, where ELi ðtÞ � EIN

i;j ðtÞ denotes the energy saved from i’s

cooperative computing using a CN j at time t, and �ECNj;i ðtÞ

denotes the energy cost by node i as a CN for some othernode j at time t. In all other cases (if i is not active either asan IN or a CN, or if i uses local computing), the utility is 0.The initial uiðtÞ can be any arbitrary value, but for simplic-ity, we assume uiðtÞ ¼ 0 for all i 2 N . Then the cumulativeutility over a time interval ½t1 : t2� is defined as uiðt1 :t2Þ ¼

Pt2t¼t1

uiðtÞ, which is the overall energy savings of a

node during the time interval.

5.2.2 Cooperation Index

CiðtÞ ¼ 1; if uið0 : t� 1Þ � 0;0; if uið0 : t� 1Þ < 0:

�(18)

This value5 is used to decide whether node i can act as aCN for other nodes (when CiðtÞ ¼ 1, i.e., in “cooperation”mode) or i should not be selected as a CN for any othernode (when CiðtÞ ¼ 0). It is maintained for each node i andupdated at each time t.

Adaptive-Positive-Utility-Strategy. A CN is selected fornode i at time t such that

CNiðtÞ ¼ arg minj2RiðtÞ;CjðtÞ¼1

fEINi;j ðtÞ þECN

i;j ðtÞg : (19)

In other words, a CN j is selected for the i’s cooperative

computing at time t that minimizes EINi;j ðtÞ þ ECN

i;j ðtÞ among

the nodes whose cumulative utilities are positive or zero.

5. We set Cið0Þ ¼ 1 in order to enable the initial cooperation condi-tion when all nodes’s utilities are zero. If Cið0Þ ¼ 0 for all i, no nodewould cooperate to other nodes.


Thus, a node whose cumulative utility is negative will ceaseto act as a CN, and will be potentially available as a CNwhen its utility becomes positive. Note that the min-total-energy-strategy can be seen as a special case of the adaptive-positive-utility-strategy with CiðtÞ ¼ 1 for all i and for all t.

Giving that some nodes may benefit more from bettercooperation opportunities (i.e. larger utilities) than theothers due to difference in the amount of tasks and to poten-tially unfair channel conditions, we can generalize the strat-egy to bring the balance (or “fairness”) of the amount ofutilities that individual nodes collect.

5.2.3 Fairness Factor

How much importance will be given to the fairness term,reflecting the utility and how much to the energy consump-tion term, depends on how fast the function wðuÞ decays asthe utility value u increases. Motivated by [48] that the well-established power-law method can measure the fairnessand inequality of network performance, we employ apower-law function

wðuÞ ¼ u�k; (20)

where parameter k is a positive constant and can be used totradeoff fairness in energy consumption. In our simulation

study, we find that wðuÞ ¼ u�5 strikes a good balance. Theprincipal implication of this power-law relationship is thatthe CN selection is far from random, i.e., a node with a largerutility (smaller weight) will have a higher chance to beselected as a CN, so thewðuÞ obtained from the utility functioncan help us improve the energy performance of the system.

Considering all above strategies, we propose a Weighted-Fairness-Strategy to bring the fairness of the amount of utili-ties that individual nodes collect.

Weighted-Fairness-Strategy. A CN is selected for node i attime t such that

CNiðtÞ ¼ arg minj2RiðtÞ;CjðtÞ¼1

fwðujð0 : t� 1ÞÞðEINi;j ðtÞ þ ECN

i;j ðtÞÞg; (21)

where wðuÞ ¼ u�5 is a non-increasing function of the utilityvalue u. The constraint CjðtÞ ¼ 1 ensures that a node whosecumulative utility is negative will cease to act as a CN, andwill be potentially available as a CN when its payoff

becomes positive. Here, along with the energy consumption

factor (EINi;j ðtÞ þECN

i;j ðtÞÞ), the weight function wðujð0 : t� 1ÞÞis introduced in the CN selection strategy, such that thenodes with larger utilities (i.e., smaller weight) will have ahigher chance to be selected as a CN for each task execution.Additionally, among CNs which have the same total energyconsumption, preference will be given to the ones withhigher cumulative utilities.

6 SIMULATION RESULTS

In this section,we evaluate performance of the proposed opti-mal solution via numerical and simulation results obtainedusing MATLAB. To be consistent with the real energy meas-urements [35], we set the computation coefficient in theorder of 10�11, the communication coefficient in the order of

10�2, a time slot t ¼ 2ms and channel gain 0 < g < 1.

6.1 Performance of the Optimal CooperativeComputing

Fig. 3 shows the local data size partitioned by the proposedoptimal solution. The input data size is assumed asL ¼ 1024 bits and channel gain is gl;r ¼ 0:5. It is clear thatthe optimal partition is significantly affected by the systemcoefficients. With better computation efficiency (smaller K)and higher communication cost (larger r), the optimal parti-tion tends to allocate more processing task locally. More-over, with a relaxed completion deadline (large T ), the localexecution is more preferable to save energy by reducingprocessing speed.

Fig. 4 gives an illustration of the energy optimal comput-ing rules for L ¼ 1; 024 bits, T ¼ 30ms and gl;r ¼ 0:5. Withthe application profile and system coefficients, we canquickly decide the best strategy to process an application.

Fig. 5 shows the total energy consumption of the optimalsolution and compare it with that of local computing.By setting K ¼ 10�10, r ¼ 0:006 and T ¼ 20ms, we observethat under the same communication coefficient, the energyperformance improves with better channel quality. Evenwith severe channel quality and high communication cost(gl;r ¼ 0:1, r ¼ 0:01), the performance of the proposed solu-tion is closed to the local computing when the application

Fig. 3. The relations between the optimal data size of local execution andsystem coefficientsK and r.

Fig. 4. An illustration of energy optimal computing rules.


requirement is not stringent (small L, large T ). As the inputdata size increases, the cooperative computing can ensureoptimal with better energy efficiency than the local comput-ing. Given the worst channel scenario with gl;r ¼ 0:1, anaverage of 63 percent of energy can still be saved by usingthe proposed cooperative computing.

Fig. 6 shows the energy comparisons in terms of comple-tion deadline. We set K ¼ 10�10, r ¼ 0:01 and L ¼ 512 bits.With a longer completion time, the optimal solution canreduce energy consumption by adjusting the processspeed at a lower pace. Even for the worst channel sce-nario, the optimal solution can still achieve better perfor-mance and close to the local computing when the delaytolerance is high. Moreover, consider the cloud comput-ing where sensor nodes only collect and transmit data tothe enterprise cloud server, the proposed cooperativecomputing can still achieve energy efficiency on handlingnon-emergent applications.

6.2 Performance of the CN Selections forCooperative Computing

We consider the size of MWSN is N (varied between 10 and50 sensor nodes). Throughout the simulation, we set theinput data size L ¼ 1024 bits and completion deadline

T ¼ 40ms. The channel gain between two nodes is ran-domly selected between 0 < g < 1 and will be changedover the time, but kept constant during one offloading pro-cess. A total of 1,000 application tasks are executed withinthe network, and at each time t, an application is executedby a random IN and a selected CN. The initial utility valueof every node uið0Þ is set 0.

Fig. 7 shows the average energy consumption per node.The proposed CN selection strategies outperform the localcomputing and cloud computing. However, since the min-total-energy strategy is the optimal solution in this case, theweighted-farness strategy performs a bit worse (this is com-pensated by fairness results). Furthermore, as the numberof nodes increases, the average energy consumption of theproposed CN strategies decreases, this is because it is easierto find a CN with better channel quality and thus save moreenergy. As a comparison, Fig. 8 shows the average perfor-mance for L ¼ 512 bits with the same deadline T ¼ 40ms. Itis clear that with less stringent application requirement, it ispreferable to employ the proposed solutions. The cloudcomputing strategy which is commonly used in today’s sen-sor cloud solution is not always optimal in dealing withnon-emergent applications.

Fig. 9 shows the fairness in terms of how much energy issaved for individual nodes using the proposed CN selectionstrategies, where the y-axis represents Jain’s fairness indexof nodes’ cumulative utilities.6 It is clear that the weighted-fairness strategy achieves the best fairness compared withother strategies. Moreover, the index curve shows a non-decreasing tendency toward increased total number ofnodes, which is contradictory to the min-total-energy strat-egywhere an increasing number of good quality nodes couldbe extensively used for cooperation. As another example tohighlight the fairness, we show in Fig. 10 the energy con-sumption saving of individual nodes at the end of simulationin five-node network. It is clear that the weighted-fairnessstrategy achieves the best fairness, whereas the Min-Total-Energy results in negative utility for a node.

Fig. 5. Total energy consumption versus input data size.

Fig. 6. Total energy consumption versus completion deadline.

Fig. 7. Average total energy consumption per node for L ¼ 1; 024bits.

6. Jain’s fairness index is defined by ðPUiÞ2=ðNP

U2i Þ. The result

ranges from 1N (worst case) to 1 (best case). The larger the index is, the

better fairness that we can achieve.


We provide additional measurement of the impact ofeach node’s “willingness” to cooperate when its utility iszero when the weighted-fairness strategy is used. We there-fore change the rule (18) for a subset of nodes, and dividethe nodes into two groups: U ¼ fi j CiðtÞ ¼ 1 if uiðtÞ ¼ 0g(‘Unselfish node’), and S ¼ fi j CiðtÞ ¼ 0 if uiðtÞ ¼ 0g(‘Selfish node’); the rule remains the same as (5.2) for bothgroup when uiðtÞ 6¼ 0. In Fig. 11, we show the proportion ofthe nodes with positive utility in the y-axis as the time pro-gresses in x-axis. The unselfish cooperation represents atotal number of 100 unselfish nodes, whereas the selfishcooperation represents only one node cooperates initially toothers out of 99. The result tells that the proposed fairnessstrategy can ensure the proportion of nodes with positiveutility converges to 1, even for the extreme selfish scenario.Moreover, convergence speed is faster with a larger process-ing task, which indicates the proposed method is robust tocope with different application requirements and thus canensure energy efficiency on individual nodes.

7 CONCLUSION AND FUTURE WORK

We have shown that it is advantageous to employ coopera-tive computing to process application tasks, which can

significantly reduce the total energy consumption whilemaintaining a given level of completion requirement.Specifically, we proposed a joint optimization problem ofcomputation and communication costs as a whole, to opti-mally partition, offload and execute tasks between sensornodes to boost the energy efficiency of edge computing. Byimplementing the proposed optimal solution into a networkwith multiple mobile wireless sensor nodes, the proposedcooperation node selection strategies can serve as an effec-tive tool to achieve a desirable tradeoff between fairnessand energy consumption at each node. The resulting ideashave the potential to have a broad impact across a range ofareas, including Internet-of-Things, Machine-to-Machineand mobile cloud computing, etc.

In the future work, the following research issues will beconsidered: 1) Cross-layer optimization of sensor cloud net-works: we will further incorporate characteristic of sensornetworks into considerations, such as multi-hop transmis-sion and resource constrains. Specifically, we will considera practical application scenario where the routing protocolfor low power and lossy network (RPL) [44] is used forsmart grid application, and obtain an optimal cooperativesolution to maximize the network lifetime. 2) Multi-nodecooperation: So far we have focused on the single IN-CN pair

Fig. 8. Average total energy consumption per node for L ¼ 512 bits.

Fig. 9. Jain’s fairness on utility function.

Fig. 10. Normalized utility function per node.

Fig. 11. Proportion of nodes with positive utility.


case. It would be also interesting to consider the multi-CNcase where more than one cooperation nodes can beselected for sensor cloud computing. The challenges will bethe new characteristic of communication energy model,since multiple subtasks need to be distributed indepen-dently to a number of CN along multiple time slots. More-over, the trade-off between the overall energy performanceand number of CN should be justified.

APPENDIX APROOF OF THEOREM 4.1

We use the Lagrange multiplier method to solve the optimi-zation problem. According to (4) and (5), the optimizationproblem in (6) can be written as

minll;lr

Kl3lt2

þ rlnrgl;r

þ rlnrgr;l

þKl3rt2

; s:t: ll þ lr ¼ L; t � T :

(22)

In order to simplify the notation, we use gl;r to denote gr;lbecause of the symmetric channel assumption, and n ¼ 1.According to the Kuhn-Tucker condition (p. 244: KKT con-ditions for convex problems [49]), the inequality constraintin (22) can be converted to the equality constraint and havethe convex function

‘ðll; lr; �Þ ¼ K

T 2l3l þ r

lrgl;r

þ rlrgl;r

þ K

T 2l3r þ �ðll þ lr � LÞ: (23)

Let a ¼ KT2 and b ¼ 2r

gl;r, we can derive the optimal partition

which must satisfy the following conditions:

@‘ðll; lr; �Þ@ll

¼ 3al2l þ �; (24)

@‘ðll; lr; �Þ@lr

¼ 3al2r þ bþ � ; (25)

Then we obtain

l2l ¼��

3a; l2r ¼

�� b

3a: (26)

Since ll þ lr ¼ L, we have

� ¼ � 3aL2

4� b2

12aL2� b

2: (27)

Substituting (27) into (26), we obtain the unique optimalsolution.

The optimal result (8) can thus be directly obtained bysumming (4) and (5) for both local and remote executionswith optimal partition (26).

APPENDIX BPROOF OF PROPOSITION 4.5

1) For local execution. According to (4) and (5), we obtain

Elc

Et¼ Kl3

lT2 gl;r

rlr. Since lr � ll, we have

Elc

Et� Kl3l

T 2 gl;rrll

) Elc

Et� Kgl;r

r ll

T

� �2

: (28)

Because L2 � l�l � L, we can obtain the lower bound perfor-

mance of local execution as

Elc

Et� Kgl;r

r L

2T

� �2

: (29)

2) For remote execution. Similarly we haveErc

Er¼ Kgl;r

r ðlrTÞ2 .

Since 0 � l�r � L2, we can obtain the upper bound perfor-

mance of remote execution

Erc

Er� Kgl;r

r L

2T

� �2

: (30)

ACKNOWLEDGMENTS

This work was supported by the Start-Up Fund from theUniversity of Sussex, UK, and in part by the Canadian Nat-ural Sciences and Engineering Research (NSERC), theNSERC DIVA Strategic Research Network.

REFERENCES

[1] M. Dikaiakos, D. Katsaros, P. Mehra, G. Pallis, and A. Vakali,“Cloud computing: Distributed internet computing for IT and sci-entific research,” IEEE Internet Comput., vol. 13, no. 5, pp. 10–13,Sep. 2009.

[2] M. Chen, H. Jin, Y. Wen, and V. Leung, “Enabling technologies forfuture data center networking: A primer,” IEEE Netw., vol. 27,no. 4, pp. 8–15, Jul. 2013.

[3] Y. Xu and S. Mao, “A survey of mobile cloud computing for richmedia applications,” IEEE Wireless Commun., vol. 20, no. 3,pp. 46–53, Jun. 2013.

[4] B. Furht and A. Escalante, Handbook of Cloud Computing. NewYork, NY, USA: Springer, 2010.

[5] Edge computing. [Online]. Available: http://en.wikipedia.org/wiki/Edge_computing, 2015.

[6] M. Y. Arslan, I. Singh, S. Singh, H. V. Madhyastha, K. Sundaresan,and S. V. Krishnamurthy, “Computing while charging: Building adistributed computing infrastructure using smartphones,” inProc. 8th Int. Conf. Emerging Netw. Exp. Technol., 2012, pp. 193–204.

[7] NVIDIA says Tegra 3 is a PC-class CPU. [Online]. Available:http://engt.co/srvibU

[8] A. Chandra, Y. Lee, B. M. Kim, S. Y. Maeng, S. H. Park, and S. R.Lee, “Review on sensor cloud and its integration with Arduinobased sensor network,” in Proc. Int. Conf. IT Convergence Security,Dec. 2013, pp. 1–4.

[9] S. Gu, Y. Yue, C. Maple, C. Wu, and B. Liu, “Challenges in mobilelocalisation in wireless sensor networks for disaster scenarios,” inProc. 19th Int. Conf. Autom. Comput., Sep. 2013, pp. 1–6.

[10] T. Yamanoue, K. Oda, and K. Shimozono, “A M2M system usingArduino, Android and Wiki software,” in Proc. IIAI Int. Conf. Adv.Appl. Informat., Sep. 2012, pp. 123–128.

[11] Y. Zhang, L. Sun, H. Song, and X. Cao, “Ubiquitous WSN forhealthcare: Recent advances and future prospects,” IEEE Internetof Things Journal, vol. 1, no. 4, pp. 311–318, Aug. 2014.

[12] E. Hossain, G. Chow, V. C. Leung, R. D. McLeod, J. Mis�i�c, V. W.Wong, and O. Yang, “Vehicular telematics over heterogeneouswireless networks: A survey,” Comput. Commun., vol. 33, no. 7,pp. 775–793, 2010.

[13] G. Strazdins, A. Elsts, K. Nesenbergs, and L. Selavo, “Wirelesssensor network operating system design rules based on real-world deployment survey,” J. Sens. Actuator Netw., vol. 2, no. 3,pp. 509–556, 2013.

[14] Z. Sheng, H. Wang, C. Yin, X. Hu, S. Yang, and V. Leung,“Lightweight management of resource constrained sensor devicesin internet-of-things,” IEEE Internet Things J., vol. PP, no. 99, p.1,Apr. 2015.


[15] Z. Sheng, C. Mahapatra, C. Zhu, and V. Leung, “Recent advancesin industrial wireless sensor networks toward efficient manage-ment in IoT,” IEEE Access, vol. 3, pp. 622–637, 2015.

[16] C. Papagianni, A. Leivadeas, S. Papavassiliou, V. Maglaris, C.Cervello-Pastor, and A. Monje, “On the optimal allocation of vir-tual resources in cloud computing networks,” IEEE Trans. Com-put., vol. 62, no. 6, pp. 1060–1071, Jun. 2013.

[17] H. Xu and B. Li, “A general and practical datacenter selectionframework for cloud services,” in Proc. IEEE 5th Int. Conf. CloudComput., 2012.

[18] T. He, S. Chen, H. Kim, L. Tong, and K.-W. Lee, “Scheduling par-allel tasks onto opportunistically available cloud resources,” inProc. IEEE Int. Conf. Cloud Comput., Jun. 2012, pp. 180–187.

[19] H. Xu and B. Li, “Dynamic cloud pricing for revenue maxi-mization,” IEEE Trans. Cloud Comput., vol. 1, no. 2, pp. 158–171,Jul. 2013.

[20] V. Di Valerio, V. Cardellini, and F. Lo Presti, “Optimal pricing andservice provisioning strategies in cloud systems: A Stackelberggame approach,” in Proc. IEEE Int. Conf. Cloud Comput., Jun. 2013,pp. 115–122.

[21] C.-A. Chen, M. Won, R. Stoleru, and G. Xie, “Energy-efficientfault-tolerant data storage amp; processing in mobile cloud,”IEEE Trans. on Cloud Computing, vol. 3, no. 1, pp. 28–41, Jan.–Mar.2015.

[22] A. Alamri, W. S. Ansari, M. M. Hassan, M. S. Hossain, A. Alelaiwi,and M. A. Hossain, “A survey on sensor-cloud: Architecture,applications, and approaches,” Int. J. Distrib. Sensor Netw.,vol. 2013, p. 917923, 2013.

[23] C. Perera, P. P. Jayaraman, A. B. Zaslavsky, P. Christen, and D.Georgakopoulos, “MOSDEN: An internet of things middlewarefor resource constrained mobile devices,” in Proc. Hawaii Int. Conf.Syst. Sci., 2013, pp. 1053–1062.

[24] M. Yuriyama and T. Kushida, “Sensor-cloud infrastructure—Physical sensor management with virtualized sensors on cloudcomputing,” in Proc. Int. Conf. Netw.-Based Inf. Syst., Sep. 2010,pp. 1–8.

[25] G. Fortino, M. Pathan, and G. Di Fatta, “BodyCloud: Integration ofcloud computing and body sensor networks,” in Proc. IEEE 4thInt. Conf. Cloud Comput. Technol. Sci., Dec. 2012, pp. 851–856.

[26] N. Zingirian and C. Valenti, “Sensor clouds for intelligent truckmonitoring,” in Proc. IEEE Intell. Veh. Symp., Jun. 2012, pp. 999–1004.

[27] G. Lewis, S. Echeverria, S. Simanta, B. Bradshaw, and J. Root,“Tactical cloudlets: Moving cloud computing to the edge,” inProc. IEEE Military Commun. Conf., Oct. 2014, pp. 1440–1446.

[28] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computingand its role in the internet of things,” in Proc. 1st edition MCCWorkshop Mobile Cloud Comput., 2012, pp. 13–16.

[29] K. Hong, D. Lillethun, U. Ramachandran, B. Ottenw€alder, and B.Koldehofe, “Mobile fog: A programming model for large-scaleapplications on the internet of things,” in Proc. 2nd ACMSIGCOMMWorkshop Mobile Cloud Comput., 2013, pp. 15–20.

[30] X. Hu, L. Wang, Z. Sheng, P. TalebiFard, L. Zhou, J. Liu, and V. C.Leung, “Towards a service centric contextualized vehicularcloud,” in Proc. 4th ACM Int. Symp. Develop. Anal. Intell. Veh. Netw.Appl., 2014, pp. 73–80.

[31] T. Ali, M. Gheith, and E. Nasr, “Socially intelligent computing—Asurvey of an emerging field for empowering crowd,” in Proc. 9thInt. Conf. Informat. Syst., Dec. 2014, pp. PDC-102–PDC-108.

[32] W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. Wu,“Energy-optimal mobile cloud computing under stochastic wire-less channel,” IEEE Trans. Wireless Commun., vol. 12, no. 9,pp. 4569–4581, Sep. 2013.

[33] C. Mei, D. Taylor, C. Wang, A. Chandra, and J. Weissman,“Sharing-aware cloud-based mobile outsourcing,” in Proc. IEEEInt. Conf. Cloud Comput., Jun. 2012, pp. 408–415.

[34] B. Heintz, A. Chandra, R. Sitaraman, and J. Weissman, “End-to-end optimization for geo-distributed MapReduce,” IEEE Trans.Cloud Comput., vol. PP, no. 99, p. 1, Sep. 2014.

[35] A. P. Miettinen and J. K. Nurminen, “Energy efficiency of mobileclients in cloud computing,” in Proc. 2nd USENIX Conf. Hot TopicsCloud Comput., 2010, pp. 4–4.

[36] W. Yuan and K. Nahrstedt, “Energy-efficient CPU scheduling formultimedia applications,” ACM Trans. Comput. Syst., vol. 24,no. 3, pp. 292–331, 2006.

[37] J. R. Lorch and A. J. Smith, “Improving dynamic voltage scalingalgorithms with pace,” in Proc. ACM SIGMETRICS Int. Conf. Meas.Model. Comput. Syst., 2001, pp. 50–61.

[38] W. Yuan and K. Nahrstedt, “Energy-efficient soft real-time CPUscheduling for mobile multimedia systems,” in Proc. 19th ACMSymp. Oper. Syst. Principles, 2003, pp. 149–163.

[39] R. Jain, The Art of Computer Systems Performance Analysis: Techni-ques for Experimental Design, Measurement, Simulation, and Model-ing. New York, NY, USA: Wiley, 1991.

[40] J. M. Rabaey, A. Chandrakasan, and B. Nikolic, Digital IntegratedCircuits, 2nd ed. Englewood Cliffs, NJ, USA: Prentice-Hall, 2002.

[41] D. Halperin, B. Greenstein, A. Sheth, and D. Wetherall,“Demystifying 802.11n power consumption,” in Proc. Int. Conf.Power Aware Comput. Syst., 2010, p. 1.

[42] J.-P. Vasseur and A. Dunkels, Interconnecting Smart Objects withIP: The Next Internet Interconnecting Smart Objects with IP: TheNext Internet. San Mateo, CA, USA: Morgan Kaufmann, 2010.

[43] M. A. Razzaque and S. Dobson, “Energy-efficient sensing in wire-less sensor networks using compressed sensing,” Sensors, vol. 14,no. 2, pp. 2822–2859, 2014.

[44] Z. Sheng, S. Yang, Y. Yu, A. Vasilakos, J. Mccann, and K. Leung,“A survey on the IETF protocol suite for the Internet of Things:standards, challenges, and opportunities,” IEEE Wireless Commun.Mag., vol. 20, no. 6, pp. 91–98, Dec. 2013.

[45] J. Lee and N. Jindal, “Delay constrained scheduling over fadingchannels: Optimal policies for monomial energy-cost functions,”in Proc. IEEE Int. Conf. Commun., Jun. 2009, pp. 1–5.

[46] M. Zafer and E. Modiano, “Delay-constrained energy efficientdata transmission over a wireless fading channel,” in Proc. Inf.Theory Appl. Workshop, Jan. 2007, pp. 289–298.

[47] J. Lee and N. Jindal, “Asymptotically optimal policies for hard-deadline scheduling over fading channels,” IEEE Trans. Inf. The-ory, vol. 59, no. 4, pp. 2482–2500, Apr. 2013.

[48] A. Mahanti, N. Carlsson, A. Mahanti, M. Arlitt, and C.Williamson,“A tale of the tails: Power-laws in internet measurements,” IEEENetw., vol. 27, no. 1, pp. 59–64, Jan. 2013.

[49] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge,U.K.: Cambridge Univ. Press, 2003.

Zhengguo Sheng received the BSc degree fromthe University of Electronic Science and Technol-ogy of China in 2006, and the MS (with distinc-tion) and PhD degrees from Imperial CollegeLondon in 2007 and 2011, respectively. He thenjoined Orange Labs. He is currently a lecturer atthe School of Engineering and Informatics, TheUniversity of Sussex, United Kingdom. He isalso the visiting faculty of University of BritishColumbia (UBC). Previously, he was with UBCas a research associate, and with France Tele-

com Orange Labs as the senior researcher and project manager inM2M/IoT. He also worked as a research intern with IBM T. J. WatsonResearch Center, and U.S. Army Research Labs. His current researchinterests cover IoT/M2M, cloud/edge computing, vehicular communica-tions, and power line communication. He has published more than 40International conference and journal papers. He is also the recipients ofAuto21 TestDRIVE Competition Award 2014 and Orange OutstandingResearcher Award 2012. He is a member of the IEEE.

Chinmaya Mahapatra received the BTechdegree in electronics and communication engi-neering from the National Institute of Technology,Rourkela, India, in 2009, and the MASc degree inelectrical and computer engineering from TheUniversity of British Columbia (UBC), in 2013,where he is currently working toward the PhDdegree with the Department of Electrical andComputer Engineering. Prior to joining UBC, hewas a scientist with the Indian Defense ResearchLaboratory, and a systems engineer with the

Ciena Research and Development Center, Ottawa, Canada. His currentinterests include the Internet of Things, embedded systems, sensorcloud, smartphone energy optimization, E-health, LTE, and Green com-munications. He is a student member of the IEEE.


Victor C. M. Leung is currently a professor ofelectrical and computer engineering and holderof the TELUS Mobility Research Chair at the Uni-versity of British Columbia (UBC). His researchinterests include the areas of wireless networksand mobile systems, where he has coauthoredmore than 700 technical papers in archival jour-nals and refereed conference proceedings,several of which had won best-paper awards.He is a fellow of the IEEE, the Royal Society ofCanada, the Canadian Academy of Engineer-

ing, and the Engineering Institute of Canada. He is serving/hasserved on the editorial boards of the IEEE Journal on Selected Areasin Communications, IEEE Transactions on Computers, IEEE Trans-actions on Wireless Communications, and IEEE Transactions onVehicular Technology, IEEE Wireless Communications Letters, andseveral other journals. He has provided leadership to the technicalcommittees and organizing committees of numerous internationalconferences. He was the recipient of an APEBC Gold Medal, NSERCPostgraduate Scholarships, a 2012 UBC Killam Research Prize, andan IEEE Vancouver Section Centennial Award.

Min Chen is currently a professor at the Schoolof Computer Science and Technology, HuazhongUniversity of Science and Technology. He isChair of IEEE Computer Society Special Tech-nical Communities on Big Data. He was an assis-tant professor at the School of ComputerScience and Engineering, Seoul National Univer-sity (SNU), from September 2009 to February2012. He was the R&D director at ConfederalNetwork Inc. from 2008 to 2009. He worked as apostdoctoral fellow at the Department of Electri-

cal and Computer Engineering, University of British Columbia (UBC), forthree years. Before joining UBC, he was a postdoctoral fellow at SNU forone and half years. His research interests include Internet of Things,machine to machine communications, body area networks, body sensornetworks, e-healthcare, mobile cloud computing, cloud-assisted mobilecomputing, ubiquitous network and services, mobile agent, and multime-dia transmission over wireless network, etc. He received best paperaward from IEEE ICC 2012, and best paper runner-up award fromQShine 2008. He has more than 180 paper publications. He has been asenior member of the IEEE since 2009.

Pratap Kumar Sahu received the PhD degreefrom National Central University, Taiwan. Heis currently a postdoctoral researcher at theNetwork Research Lab in the Department ofComputer Science and Operation Research,University of Montreal. His research interestsinclude network coding, sensor networks, andvehicular ad hoc networks. Mostly, he works onvarious aspects of Intelligent TransportationSystems. He has many publications in variousjournals such as IEEE Transactions on Intelligent

Transportation Systems, IEEE Sensors, Springer TelecommunicationSystems and conferences such as IEEE Globecom, IEEE ICC, andIEEE ICCCN. He is a member of the IEEE.

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