partner selection and incentive mechanism for physical layer...

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/TWC.2015.2418316, IEEE Transactions on Wireless Communications

Partner Selection and Incentive Mechanism forPhysical Layer Security

Ning Zhang, Student Member, IEEE, Nan Cheng, Student Member, IEEE, Ning Lu, Student Member, IEEE,Xiang Zhang, Jon W. Mark, Life Fellow, IEEE, Xuemin (Sherman) Shen, Fellow, IEEE

Abstract—In this paper, we study user cooperation to enhancethe physical layer security. Specifically, the source cooperates withfriendly intermediate nodes to transmit message securely in thepresence of multiple eavesdroppers. We propose a cooperativeframework, whereby the source selects multiple partners andstimulates them by granting an amount of reward. First, multiplecooperative relays and jammers are selected by the source usinggreedy or cross-entropy based approaches. Then, the source andthe partners negotiate for the payment and transmission power,which is modeled as a two-layer game. At the top layer, a buyer-seller game is utilized, where the source buys the service providedby the partners. At the bottom layer, all the partners share thereward by determining their transmission powers in a distributedway, which is formulated as a non-cooperative power selectiongame. By analyzing the game, the partners can determine thetransmission powers for cooperation, while the source can selectthe best payment. To further improve the utility of the source, aset of reward allocation coefficients are introduced and optimizedusing particle swarm optimization approach. Simulation resultsare provided to demonstrate the performance of the proposedschemes.

Index Terms –Physical layer security, information-theoreticsecurity, relay and jammer selection, incentive mechanism,particle swarm optimization.

I. INTRODUCTION

It is well known that security is of paramount importancefor wireless networks due to the inherent openness of thewireless medium, where anyone within the transmission rangeof the source can receive the message. To protect the trans-mitted message, encryption/decryption can be adopted, whichis usually implemented at upper layers of the protocol stack[1]. However, encryption algorithms could be compromised asthe capabilities of the adversaries expand, e.g., when quantumcomputing is available. Moreover, it is difficult to distributeand manage key materials in a network without infrastruc-ture [2]. As a complement, physical (PHY) layer security,or information-theoretic security, can provide perfect securityeven though the adversary has unlimited computational power,by means of exploiting the properties of the wireless channel toprotect the transmitted signal from being received or decoded

Manuscript received September 27, 2014; revised January 28, 2015.N. Zhang, N. Cheng, N. Lu, Jon W. Mark and X. Shen are with the De-

partment of Electrical and Computer Engineering, University of Waterloo, 200University Avenue West, Waterloo, ON N2L 3G1, Canada (e-mail:{n35zhang,n5cheng, n7lu, jwmark, sshen}@uwaterloo.ca).

X. Zhang is with School of Information and Software Engineering, Uni-versity of Electronic Science and Technology of China, China. Email:[email protected]

by eavesdroppers [3]–[6]. By doing so, the the sequenceof bits that are transmitted through physical layer can beguaranteed to be secure, i.e., the eavesdroppers cannot recoverthe correct bit sequence. Therefore, physical layer securitycan be leveraged to significantly strengthen the security ofthe communication system.

The theoretical basis for PHY layer security or information-theoretic security is the well known notion of the perfectsecrecy from Shannon [7]. In the pioneer work [3], it is shownthat the information with perfect secrecy can be exchangedat a nonzero rate between the source and the destination,while the eavesdropper cannot learn anything, if the channel ofeavesdropper is worse than that of the destination. This rate iscoined as the secrecy rate, and the maximal achievable secrecyrate is refereed to as the secrecy capacity. More specifically,suppose that X , Y , and Z are the input of the source, the out-puts at the destination and the eavesdropper, respectively. Thesecrecy capacity can be given by max[I(X;Y ) − I(X;Z)],where I(·, ·) is the mutual information. It holds that the secrecycapacity is positive when the channel of eavesdropper isworse than that of the destination. However, when the source-destination channel is worse than the source-eavesdropperchannel, the source and destination cannot exchange anysecure information since the secrecy capacity is equal to zerounder such a scenario.

To address the above issue, user cooperation has beenintroduced to enhance the secrecy of communications [8]–[14].In [8], cooperative relaying is leveraged to increase the secrecyrate, either in decode-and-forward (DF) mode or amplify-and-forward (AF) mode. In [9], friendly jammers are employedto jam the eavesdropper by broadcasting artificial noise. In[10], distributed beamforming is performed at relays, whereeach cooperative node forwards a weighted version of thesource’s message. By selecting suitable beamforming weights,the secrecy rate can be maximized. In [11], zero-forcingbeamforming is employed, whereby the cooperative nodes cancreate noise to confound the eavesdroppers while protectingthe destination from interference. In summary, to improvethe secrecy of communication, individual nodes can act asrelays or friendly jammers, or collaborate with each otherto perform beamforming. However, the existing work relieson the assumption that the users are voluntary to cooperate.Considering that the nodes consume energy during cooperationand have no gain, the assumption might not be suitable inpractice. The issue of how to stimulate users to cooperate forsecurity enhancement needs to be studied [15], [16]. Moreover,most of the existing works focus on the power allocation



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or weight selection to maximize the secrecy rate and onlya few works pay attention on partner selection [17], [18].However, the work in [17], [18] only considers a single relayor jammer, or multiple relays without jammers, which maynot fully exploit the benefits of cooperation. Although therehas been a flurry of research activities in this area, the abovefundamental issues still need to be further studied.

In this paper, we aim to facilitate cooperation for security,whereby a source selects multiple partners and stimulates themfor cooperation by paying an amount of reward to exchangeinformation securely with the destination in the presence ofmultiple eavesdroppers. To transmit the information securely,the source first selects a set of partners for cooperation, eachof which can act as a relay or a jammer1. To select suitablepartners, i.e., cooperative relays and jammers, two heuristicalgorithms are proposed: greedy partner selection and cross-entropy based partner selection. Then, the source stimulatesthe selected partners for cooperation by paying them for theirservice, i.e., relaying or jamming. Considering that both thesource and the participants are rational and selfish, they areonly interested in maximizing their own utilities. Thus, tonegotiate the parameters for cooperation, i.e., the payment ofthe source and the transmission power of the partners, thenegotiation process is modeled by a two-layer game. At thetop layer, based on the Stackelberg game framework, a buyer-seller game is utilized to model the process that the sourcepays an amount of reward to buy the service provided bythe partners. At the bottom layer, all the partners determinetheir transmission powers to share the reward of the source ina distributed way, which is formulated as a non-cooperativepower selection game. The utility functions of both playersare first defined and each player selects the best strategyto maximize its own utility. Then, the existence and theuniqueness of Nash equilibrium (NE) is proved. By analyzingthe game, the partners can determine their best transmissionpowers for cooperation, while the source can select the bestpayment. To further improve the utility of the source, a setof reward allocation coefficients are introduced. To find theoptimal allocation coefficients, particle swarm optimizationapproach is adopted, where the particles are constructed andmoves towards the best solution by iteratively adjusting themovement of particles. Simulation results are provided tovalidate and show the performance of the proposed algorithmsand the incentive mechanism.

In a nutshell, the contributions of this paper are summarizedas follows:

1) To the best of our knowledge, there is no existingwork to stimulate cooperation for physical layer securityenhancement. Based on game-theoretical approach, wepropose an incentive mechanism to bridge this gap.

2) Two partner selection algorithms are devised, wherebythe source selects multiple relays and jammers to max-imize the secrecy rate.

3) The cooperative relays and jammers autonomously se-lect the transmission power to maximize their utilities,

1We only consider cooperating relays and jammers for simplicity, sincecollaborative beamforming requires perfect synchronization and informationexchange among intermediate nodes

Figure 1. System model.

which is modeled by a non-cooperative power allocationgame. It is proved that there exists a unique pure NE inthis game.

4) The source can select the total payment and a suitableallocation coefficient vector to provide incentive for theintermediate nodes such that its utility can be maxi-mized.

The remainder of this paper is organized as follows. Thesystem model is presented in Section II. The partner selec-tion and incentive mechanism are given in Section III andSection IV, respectively. The weighted payment allocationapproach is presented in Section V. Simulation results areprovided in Section VI, followed by the conclusion in Sec-tion VII.

II. SYSTEM MODEL

As depicted in Fig. 1, the considered system consists ofa source (S), a destination (D), M intermediate nodes (i =1, 2, ...,M ), and one or multiple eavesdroppers (E) who aimto decode the source’s information. It is known that when thechannel between S and D is worse than that between S andE, the secrecy rate is zero. To transfer information securely, Srequests the intermediate nodes for cooperation, which are allconsidered friendly2. The intermediate node can act as a relayor a friendly jammer and cooperation can be performed in atwo-phase fashion. In the first phase, S transmits message tothe cooperating relays, which is also overheard by D and E.In the second phase, the cooperative relays employ Amplify-and Forward (AF) protocol to relay the source’s message toD to increase the transmission rate at D, while the jammerssimultaneously broadcast artificial noise to confound E3. Tomaximize the secrecy rate, the suitable cooperative relays andjammers should be carefully selected by the source.

A slow, flat, block Rayleigh fading environment is consid-ered, where the channel remains static in one time slot andchanges independently over different time slots. The channel

2The work in [19], [20] consider user cooperation with untrusted nodes.3Please note that certain techniques can also be used to protect the transition

in the first phase such as [21], [22]. Since the focus of this work is on partnerselection and incentive mechanism design, interested readers can refer to thoseworks.



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coefficients from S to D and S to a specific E are denotedby hsd and hse, respectively. The channel coefficient from Sto intermediate node i ∈ M is denoted by hi

s. Similarly, thechannel coefficients from intermediate node i ∈ M to D andE are hi

d and hie, respectively. The global CSI is assumed

available for the system, including D-related CSI (D-CSI) andE-related CSI (E-CSI), which is a common assumption in PHYlayer security literature [8], [10], [12], [13], [23]. E-relatedCSI (E-CSI) can be obtained in the scenarios where the eaves-droppers are active 4 in the network and their transmission canbe monitored [8]. In addition, additive white Gaussian noiseis assumed with zero mean and the one-side power spectraldensity is N0. Moreover, each node is equipped with a singleantenna and communicates with each other in a half-duplexmode.

III. PARTNER SELECTION

We use secrecy rate as a measure for secure communication,which is defined as the difference between the transmissionrate at D and that at E. In what follows, we will firstanalyze the secrecy rate through cooperation and then selectthe suitable partners.

At the destination D, the SNR γsd from the direct link (Sto D) is given by

γsd =Ps |hsd|2

N0, (1)

where Ps is the transmission power of the source.Suppose that node i is in the relay set R, then the SNR

from relay i using AF cooperative protocol can be given asfollows [24]:

γid =

1

N0

Ps

∣∣his

∣∣2 Pi

∣∣hid

∣∣2Ps |hi

s|2+ Pi

∣∣hid

∣∣2 +N0

, i ∈ R, (2)

where Pi is the transmission power of node i.Suppose that node j is in the jammer set J, the interference

γjd caused by jammer j can be given as follow:

γjd =

Pj

∣∣∣hjd

∣∣∣2N0

, j ∈ J. (3)

The achievable rate at D can be expressed as follows:

Rd =W

2log2(1 +

γsd +∑

i∈R γid

1 +∑

j∈J γjd

). (4)

At a generic eavesdropper, e.g., k-th E, the SNR γse fromthe source can be given as follows:

γse =Ps |hse|2

N0. (5)

The SNR γie from relay i, where i ∈ R, can be given as

follows:

γie =

1

N0

Ps

∣∣his

∣∣2 Pi

∣∣hie

∣∣2Ps |hi

s|2+ Pi |hi

e|2+N0

, i ∈ R. (6)

4When the eavesdroppers are passive, in order to avoid interfering with thedestination, the jammers can create artificial noise which is transmitted in thenull space of the channel from the source to destination [10], [11].

The interference γje caused by jammer j, where j ∈ J, can

be given as follow:

γje =

Pj

∣∣hje

∣∣2N0

, j ∈ J. (7)

Similarly, the achievable rate at the k-th E can be expressedas follows:

Rke =

W

2log2(1 +

γse +∑

i∈R γie

1 +∑

j∈J γje

). (8)

According to the definition of secrecy rate, the secrecy rateis given by

Rksec = Rd −Rk

e , (9)

where Rd and Rke are given in (4) and (8), respectively.

Considering the presence of multiple eavesdroppers, theoverall secrecy rate Rsec is given by

Rsec = max{0,mink

{Rd −Rke}}, (10)

where Rke is the achievable rate at the k-th eavesdropper.

In the first step, the source selects the cooperative relaysand jammers to maximize the secrecy rate, assuming that thetransmission power of the potential participants is fixed. Thisproblem can be formulated as follows:

maxXi,j ,∀i∈{1,2,...,M}

Rsec

s.t.∑

j∈{R,J,Nu}

Xi,j = 1, ∀i ∈ {1, 2, ...,M}

Xi,j ∈ {0, 1}, ∀i ∈ {1, 2, ...,M} and ∀j ∈ {R, J,Nu}

Specifically, the binary variable Xi,j indicates the role of nodei, where j can be {R, J,Nu}, which correspond to act as arelay (R), a jammer (J), or keep silent (Nu). For example,when Xi,R = 1, node i acts as a relay. The secrecy rate Rsec =W2 log2(1 +

γsd+∑

i∈R γid

1+∑

j∈J γjd

)− W2 log2(1 +

γse+∑

i∈R γie

1+∑

j∈J γje), where

the relay and jammer set can be determined by R = {i,Xi,R =1} and J = {i,Xi,J = 1}. Exclusive search can obtain theoptimal solution. However, the complexity is very high sincethe search space is exponential to the number of intermediatenodes. Instead, two heuristical algorithms are proposed in thefollowing.

A. Greedy Partner Selection Algorithm

Based on the above formula, a greedy partner selectionalgorithm is developed, as shown in Algorithm 1. The mainidea is to select the best relaying node at each round until theoverall secrecy rate cannot be improved.

B. Cross-Entropy based Partner Selection Algorithm

The partner selection problem can also be solved usingthe Cross-entropy (C-E) method, which is more efficient insearching the optimal solution [25]. In C-E method, ”deter-ministic” optimization problem should be translated into arelated ”stochastic” optimization problem, where the rare eventsimulation techniques similar to [26] can be utilized. In other



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Algorithm 1 Greedy Parter Selection AlgorithmInput: M, hi

s, hid, h

ie, ∀i ∈M.

Output: Partner selection results R and J1: (Initialization): Set Rsec = 0, ∀i ∈M.2: for i ← 1 to M do3: for j ∈ {R, J,Nu} do4: Xi,j = 1

5: Calculate R′sec

6: end for7: Find the maximum R′

sec

8: if R′sec > Rsec then

9: Rsec = R′sec

10: Xi,j = argmaxR′sec

11: end if12: end for13: return R = {i,Xi,R = 1} and J = {i,Xi,J = 1}

words, the main idea behind the C-E method is to definefor the original optimization problem an associated stochasticproblem (ASP) and then efficiently solve the ASP by anadaptive scheme. It sequentially generates random solutionswhich converge stochastically to the optimal or near-optimalone.

Typically, the C-E method involves an iterative procedurewhere each iteration comprises of the following two phases:i) Generate a random data sample according to a specifiedstochastic policy; ii) Update the stochastic policy based onthe outcome of the sample to produce a ”better” sample in thenext iteration.

C-E algorithm: Algorithm 2 represents the detailed proce-dure of channel selection, which consists of five main steps asfollows.

Define the strategy space S for all the intermediate nodesas follows:

S := {R, J,Nu}. (11)

The probability vector associated with the strategy space isgiven as follows:

Pit := {piR,t, p

iJ,t, p

iNu,t},

∑j∈{R,J,Nu}

pij,t = 1, (12)

where Pit denotes the stochastic policy of node i on the strategy

space S at t-th iteration, and pij,t denotes the probability thatnode i chooses strategy j at t-th iteration.

1) (Initialization). Set the iteration counter t := 1. Set theinitial stochastic policy Pi

0 of all SUs to be the uniformdistribution on the strategy space S. In other words, foreach intermediate node, it picks the strategy from thestrategy space uniformly, with equal probability 1/3.

2) (Generation samples). Based on the initial stochasticpolicy of all nodes, the Z samples of the strategy vectorare generated, which can be given as follows:

Si(z) := {IiR(z), IiJ(z), IiNu(z)}, (13)

where Si(z) is the z-th strategy vector of node i withonly one element to be ”1” and the rest are ”0”. Theprobability for Iij to be ”1” is pij,t.

3) (Performance evaluation). Substitute the samples into

Algorithm 2 C-E Partner Selection AlgorithmInput: M, T , Z, ρ, hi

s, hid, h

ie, ∀i ∈M.

Output: Partner selection results R and J1: (Initialization): Set Rsec = 0 and pij,t = 1/3, j ∈ {R, J,Nu},∀i ∈M.

2: for t← 1 to T do3: for z ← 1 to Z do4: for i← 1 to M do5: Generate samples of the strategy vector.6: end for7: end for8: for z ← 1 to Z do9: Calculate the utilities U(z) according to (9).

10: end for11: Order the utilities U(z) in a nonincreasing manner.12: for i← 1 to M do13: for j ← {R, J,Nu} do14: Update Pi

t using (14)15: end for16: end for17: end for18: return R = {i, piR,T = 1} and J = {i, piR,J = 1}

(11) to calculate the utilities U(z). Arrange the U(z)in a nonincreasing order according to the values, i.e.,U1 > U2 > ... > UZ . Let υ be the (1 − ρ) samplequantile of the performances: υ = U⌈(1−ρ)Z⌉, where ⌈·⌉is the ceiling function.

4) (Stochastic policy update). Based on the same sample,calculate Pi

t := {piR,t, piJ,t, p

iNu,t

}, using the followingequation:

pij,t =

∑Zz=1 XUn≥υI

ij(z) = 1∑Z

z=1 XUz≥υ

, (14)

where XUz≥υ is defined as follows:

XUz≥υ =

{1 Uz ≥ υ

0 otherwise(15)

5) If the stopping criterion is met (e.g., the maximumiteration number is reached), stop; otherwise increasethe iteration counter t by 1, and reiterate from step 3.

Theorem 1: For the given number of intermediate nodesM , the time complexity of the proposed greedy and C-Ealgorithm is polynomial in M .

IV. INCENTIVE MECHANISM FOR COOPERATIVE SECURECOMMUNICATIONS

To motivate the intermediate nodes to participate in coop-eration for security enhancement, the source announces anamount of reward to all the participants. Then, given theannounced reward, all the participants, which is competitivewith each other, maximize their utilities by determining thetransmission power for cooperation. This process is modeledas a two-layer game, which can be illustrated in Fig. 2.At the top layer, a buyer-seller game is utilized to modelthe payment selection process, based on the framework oftwo-stage Stackelberg game. At the bottom layer, all thepartners share the reward by determining their transmission



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Stage I: Payment determination

The source determines the payment and announces to all the users

Stage II: Power determination (non-cooperative game)

Each intermediate node determines the power for secure communication

Figure 2. Two-layer game for the incentive mechanism.

powers in a distributed way, which is formulated as a non-cooperative power selection game. By analyzing the game, thebest payment and transmission power can be determined. Inthe following, we first define the utilities of players and thenanalyze the game to find the best strategies for the players.

A. Utility Functions

The utility of the source node is given by

Us = λ1Rsec −Rm (16)

where λ1 is the profit per secrecy rate, while 0 ≤ Rm ≤ Rmax

is the payment it grants to the cooperative relays and jammers.The cooperative relays and jammers share the payment

according to their contribution to the secrecy rate. In otherwords, the payment the cooperative participant can obtain isproportional to the contribution it makes in the cooperation.Since the relay is leveraged to increase the perfect secrecyof the relaying link compared with that of the eavesdropperlink, the contribution for relay i can be approximately given

byPi|hi

d||hi

e|. While the jammer is leveraged to increase more

artificial noise at eavesdropper than at the destination node,the contribution for the jammer j can be approximately given

byPj|hj

e||hj

d|.

The utility of the selected node i is given by

Ui =Piri∑

j⊆C PjrjRm − λ2Pi. (17)

where C := R⊎J is the set of selected nodes with the size N ,λ2 is the cost rate for transmission power, and the contributionfactor ri is defined as follows:

ri =

|hi

d||hi

e|, i ∈ R

|hie|

|hid|, i ∈ J

(18)

As a two-stage game, the buyer-seller game can be analyzedby the backward induction method. First, the optimal strategies(i.e., the transmission powers) of the partners are analyzed,assuming the strategy of the source node (i.e., the payment) isfixed. Second, based on the results of the first step, the sourcenode decides the optimal strategy, being aware of the effectsof its decision on the strategies selected by the partners. Bydoing so, the best strategies of both the source node and thepartners are obtained such that the corresponding utilities canbe maximized.

B. No-cooperative Power Selection game

In order to stimulate cooperation of the intermediate nodes,the source pays rewards for their service. Each intermediatenode gets a certain amount of rewards according to its con-tribution in the service. For a given reward, each cooperativenode tries to maximize its own utility by selecting a suitabletransmission power, which is modeled as a non-cooperativepower selection game. In the following, we will analyzethis game to find the best power selection strategies, whichconstitute a Nash equilibrium (NE)

Definition 1: Non-cooperative power selection game is de-fined by G = {C, {Si}, {Ui}}, where C is the set of players,Si is the strategy set of node i, and Ui is the utility functionof node i.

Note that Si is the transmission power that node i canchoose and the utility function of node i is given by (17).

Theorem 2: There exists an NE in the non-cooperativepower selection game G = {C, Si, {Ui}}.

Proposition 1: An NE exists in the non-cooperative powerselection game G = {C, {Si}, {Ui}}, if for all node i ∈ C: i)Si is a nonempty, convex, and compact subset of someEuclidean space RN ; and ii) Ui is continuous in P andconcave in Pi, where P is the set of Pi, i ∈ C.

The strategy space Si is defined as the transmission power0 ≤ Pi ≤ Pmax. Therefore, the strategy space is a nonempty,convex, and compact subset of some Euclidean space Rn.

Since the utility Ui is given by

Ui =Piri∑

j⊆C PjrjRm − λ2Pi. (19)

which is continuous in P . Taking the first derivative of Ui

with respect to Pi yields

∂Ui

∂Pi=

riRm

∑j ̸=i,j⊆C Pjrj(∑

j⊆C Pjrj

)2 − λ2 (20)

Then, taking the second derivative of Ui with respect to Pi,we have

∂2Ui

∂2Pi= −2

ri2Rm


j⊆C Pjrj

)3 < 0 (21)

The second derivative of Ui with respect to Pi is alwaysnegative, which means Ui is concave in Pi. Therefore, thenon-cooperative power selection game G exists an NE.

Theorem 3: The non-cooperative power selection game Ghas a unique NE.

Definition 2: A weighted sum of Ui(P ) is given byσ(P, µ) =

∑Ni=1 µiUi(P ), where µ = {µ1, µ2, ..., µN} with

µi ≥ 0 and P = {P1, P2, ...Pi, ...PN}. The pseudogradient ofσ(P, µ) is defined by φ(P, µ), which is given by

φ(P, µ) =

µ1∇1U1(P )µ2∇2U2(P )

...µN∇NUN (P )

(22)

Define Ψ(P, µ) to be the Jacobian matrix of φ(P, µ) withrespect to P .



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Proposition 2: If σ(P, µ) is diagonally strict concave in Pfor some positive µ, the non-cooperative power selection gamehas a unique Nash equilibrium [27].

Proposition 3: σ(P, µ) is diagonally strict concave if thesymmetric matrix [Ψ(P, µ),Ψ′(P, µ)] is negative definite forP [27].

Proposition 4: The symmetric matrix [Ψ(P, µ),Ψ′(P, µ)]is negative definite for P if the following conditions aresatisfied: i) Ui(P ) is concave with respect to Pi; ii) Ui(P )is convex with respect to P−

i , where P−i is the transmission

power of other nodes rather than node i; iii) σ(P, µ) is concavewith respect to P for some positive µ [27].

According to above propositions 2, 3 and 4, we only need toprove the proposition 4 to have the theorem 2. For proposition4, we have already proved that Ui(P ) is concave with respectto Pi. Next, we will prove that Ui(P ) is convex with respectto P−

i . Taking the first derivative of Ui(P ) with respect to Pj ,j ̸= i, yields

∂Ui

∂Pj= − riRmrj(∑

j⊆C Pjrj

)2 (23)

The second derivative of Ui(P ) with respect to Pj (j ̸= i)is given by

∂2Ui

∂2Pj=

2riRmr2j(∑j⊆C Pjrj

)3 > 0 (24)

Therefore, Ui(P ) is convex with respect to P−i . According to

the rule that ∂∑

i f(x)

∂x =∑

i∂f(x)∂x , based on (21) and (33),

the second derivative of σ(P, µ) with respect to Pi is givenby

∂2σ(P, µ)

∂2Pi= µi

−2ri2Rm


j⊆C Pjrj

)3

+∑

j ̸=i,j⊆C

µj2rjRmr2i(∑j⊆C Pjrj

)3

(25)

It is obvious that for some positive µ, ∂2σ(P,µ)∂2Pi

> 0. Then, itapplies that σ(P, µ) is concave with respect o P for somepositive µ. Therefore, the non-cooperative power selectiongame has a unique NE.

Since Ui is concave with respect to Pi, the best responsecorrespondence can be obtained by setting the first derivativeof Ui with respect to Pi to 0, as follows:

∂Ui

∂Pi= −−riRmAi + λ2Ai

2 + 2λ2AiPiri + λ2Pi2ri

2(∑j⊆C Pjrj

)2 = 0 (26)

where Ai =∑

j ̸=i,j⊆C wjPjrj . By solving it, the solutionsare given by (27).

The detailed procedure can be found in the Appendix-A.By solving the equations set (26), we can find the unique

equilibrium as follows:

P ∗i = [min{ RmriBi

λ2(ri +Bi)2, Pmax}]+ (28)

where Bi = (N−1)ri∑Nj=1

rirj

−N+1. The detailed procedure can be

found in the Appendix-B.

C. Source Node Utility Maximization

Based on the analytical results of the power selection game,the source determines its strategy (the payment) to maximizeits utility, aware of the effects of its strategy on the resultsof the power selection game. It can be formulated as thefollowing problem:

maxRm

Us = λ1Rsec −Rm

s.t. 0 ≤ Rm ≤ Rmax.(29)

where Rsec is obtained when the partners adopt the trans-mission power given by (28), which is a function of Rm.Therefore, the utility function of the source becomes a functionof one single parameter Rm. To find the best Rm, the classicalapproach is to get the extremum by setting the first derivativeof Us with respect to Rm equal to 0 and then comparethe extremum with the boundary to find the best paymentR∗

m. Finally, we can obtain the best strategy of partners bysubstituting R∗

m into (28).

V. WEIGHTED PAYMENT ALLOCATION APPROACH

In the previous section, the source can only determine theamount of payment to the cooperative partners. To furtherimprove the utility of the source, it can actively affect the wayhow the partners share the payment by means of introducinga set of weights for the partners, which are relevant to the CSIof the partners. Specifically, the source introduces the weightsW := {w1, w2, ..., wi, ..., wN} as the allocation coefficients,associated with the selected nodes, where N is the totalnumber of selected nodes for cooperation, 0 ≤ wi ≤ 1 isthe allocation coefficient for node i and

∑i wi = 1. With

the allocation coefficient posed by the source, the interactionbetween the source and intermediate nodes are modeled usinga similar game as before.

A. Utility Functions

The utility function of the source node is the same as before,which is given as follows:

Us = λ1Rsec −Rm. (30)

Different from the previous case, the utility of the cooper-ative node i is given by

Ui =Piwiri∑

j⊆C PjwjrjRm − λ2Pi. (31)

where wi is the payment allocation coefficient for node i andri is the contribution factor defined in (18).

B. Non-cooperative Power Selection Game

Given the payment Rm and the allocation coefficientsW := {w1, w2, ..., wi, ..., wN}, the selected nodes determinetheir own strategies, i.e., the transmission power, to maximizetheir utilities, given by (31).



7

P ∗i (P

−i ) =

0 if

∑j ̸=i,j∈C Pjrj ≥ RmPiri

λ2

1ri(√

RmPiriAλ2

−A) if∑

j ̸=i,j∈C Pjrj <RmPiri

λ2and 1

ri(√

RmPiriAλ2

−A) < Pmax

Pmax otherwise

(27)

Taking the first derivative of Ui with respect to Pj , j ̸= i,yields

∂Ui

∂Pj= − wiriRmwjrj(∑

j⊆C wjPjrj

)2 (32)

The second derivative of Ui with respect to Pj (j ̸= i) isgiven by

∂2Ui

∂2Pj=

2wiriRm(wjrj)2(∑

j⊆C wjPjrj

)3 > 0 (33)

Similar to the proof for the existence of NE and theuniqueness in the previous case, the new power allocationgame can be proved to have a unique NE.

Since Ui is concave with respect to Pi, the best responsecorrespondence can be obtained by setting the first derivativeof Ui with respect to Pi equal to 0, i.e.,∂Ui

∂Pi=

−−wiriRmAi + λ2Ai2 + 2λ2AiwiPiri + λ2wi

2Pi2ri

2(∑j⊆C wjPjrj

)2 = 0

where Ai =∑

j ̸=i,j⊆C wjPjrj .By solving the equations set (34), we can find the unique

equilibrium as follows:

P ∗i = [min{ RmwiriBi

λ2(wiri +Bi)2, Pmax}]+ (34)

where Bi =(N−1)wiri∑N

j=1wiriwjrj

−N+1.

C. Source Node Utility Maximization

In the previous section, we present how the weight coeffi-cient W affects the power allocation of the cooperative part-ners, as shown in (34). Different transmission power selectionin turn changes the utility function of the source. Therefore,there exists an implicit relationship between the utility functionUs and the weight coefficient W . In this section, we aim tofind the optimal W such that Us can be maximized, whichcan be formulated as the following optimization problem:

maxw1,w2,...,wN

Us

s.t. 0 ≤ wi ≤ 1, i = 1, 2, ..., N∑iwi = 1

(35)

Since it is difficult to derive an explicit equation to expressthe relation between Us and W , regular optimization methodsmay not be applicable. Bio-inspired and swarm intelligenceoptimal method, as an important branch of optimization theory,provides an effective way to address such complex problems.Genetic algorithm (GA) is the most successful one in this area

w1 w2 w3 wN-2 wN-1 wN

1× N

Element n in the particle represents

the weight coefficient for partner n

Figure 3. The structure of particle.

and has been applied to solve many practical problems. How-ever, due to the inherent encoding structure and iteration rule,GA is not appropriate for continuous variable optimization.In this section, we adopt a relatively new swarm intelligencemethod, named Particle Swarm Optimization (PSO) to solvethe above problem [28], [29]. Compared with GA, PSO hasbetter global searching ability, especially in the continuousspace, and a local searching ability near the end of the run.

The standard PSO algorithm typically involves the followingsteps: 1) Construct particle structure to map the solution ofinterest problem; 2) Create initial topology for particle swarmand parameters; 3) Evaluate fitness value of each particle; 4)Update particle position; 5) Repeat step (2) to (4) until thesolution satisfies the terminating condition.

Following this framework, we first construct a root par-ticle particleroot, as shown in Fig. 3. The n-th element ofparticleroot indicates the allocation coefficient for n-th partner(i.e., wn). In other words, particleroot implies an initial alloca-tion, as well as a start point for the optima searching. In thispaper, an equal weight distribution strategy are adopoted, i.e.particleroot(n) = 1/N .

Based on the given particleroot, we initialized the particleswarm with the size of NPSO. The i-th particle can beexpressed as an N -dimensional vector particlei and denoteits n-th element by particlei(n), which is given as follows:

particlei(n) = particleroot(n) + ω, (36)

where ω follows the uniform distribution in[−particleroot(n), 1− particleroot(n)].

particlei(n) =particlei(n)∑n particlei(n)

,∀i ∈ [1, NPSO], n ∈ [1, N ]

(37)

The fitness value of the i-th particle is denoted as Fitnessi,which actually is the utility of the source. In other words,Fitnessi is the utility function Us of the source that canbe obtained by using (16). Fig. 4 illustrates the process forcalculating the fitness for a given particle. In addition, denoteby particleGopt the global best particle of the swarm, i.e.,



8

Figure 4. Illustration of calculating the fitness for a given particle.

the particle with the highest fitness value Gopt; denote byparticleGopt

i the best historical position of i-th particle with thecorresponding fitness value Popti. The position variation fori-th particle is denoted as velocityti. At the t-th iteration, theparticle position can be updated by the following equations:

velocityt+1i = λ(velocityti + cγ1(Pparticleti − particleti)

+cγ2(Gparticlet − particleti))(38)

particlet+1i = particleti + velocityt

i (39)

where λ is the inertia coefficient in PSO algorithm and therandom variables γ1 and γ2 are uniformly distributed within[0,1]. In this paper, these parameters are set as follows:

λ =1

|1− c−√c2 − 2c|

(40)

where c = 2.05 and λ = 0.729. Algorithm 3 represents thedetailed procedure of the PSO based weight selection.

Algorithm 3 PSO based weight selection algorithmInput: Number of partners, number of particle swarm NPSO

Output: Weight Coefficient W1: // Step1: Initialization2: Generate root particle particleroot with equal weight distribution,3: for i ← 1 to NPSO do4: Generate searching particle particlei5: end for6: // Step2: Find particleGopt, Gopt7: Calculate the Fitnessi of source node, i = 1, 2, ..., NPSO

8: Find the global best particleGopt and Gopt9: Find the local best particleGopt

i and Popti10: // Step3: Update11: for i ← 1 to NPSO do12: Update particlei using (38) and (39)13: Run Step 214: if particleGopt and particleGopt

i stay unchanged then15: Stop16: else17: Continue18: end if19: end for20: Return particleGopt

6 7 8 9 10 11 12 13 140

0.5

1

1.5

2

2.5

3

Number of Intermediate nodes

Sec

recy

rat

e of

the

sour

ce (

bps/

HZ

)

C−EGreedySingle R+JExhaustive search

Figure 5. Comparison among different partner selection algorithms.

0 1 2 3 4 5 6 7 8 9 10 11−8

−6

−4

−2

0

2

4

6

8

X

Y

S

D

E

Figure 6. The network scenario for simulation.

VI. SIMULATION RESULTS

In this section, simulation results are provided to evaluatethe performance of the proposed scheme. The simulation isset up as follows. In a 1 km× 1 km area, the source, thedestination, and two eavesdroppers are located at the origin,(1 km, 0.5 km), (1 km, -0.5 km), and (0.8 km, -0.4 km),respectively, while a set of nodes are located in between.The maximum transmission power of all nodes are set to10 W, while the noise power is set to -70 dB. The averagepower gains between nodes is calculated by the path loss withexponent µ = 3.5.

To evaluate the average performance of the proposed partnerselection algorithms with respect to the number of interme-diate nodes, Monte Carlo simulation is carried out, whichconsists of 500 trials. At each trial, a number of intermediatenodes are uniformly distributed in the area. Fig. 5 shows theaverage secrecy rate using the exhaustive search algorithm,the proposed greedy algorithm, C-E algorithm, and singlerelay and jammer selection algorithm in [17]. The exhaustivesearch algorithm has the best performance and it provides aperformance benchmark. It can be seen that the C-E algorithmcan achieve almost the same performance as the exhaustivesearch algorithm does. Moreover, it can be seen that theproposed algorithms can achieve higher secrecy rate, comparedwith the single relay and jammer selection algorithm. This isbecause they can fully exploit the benefits of cooperation byleveraging multiple relays and jammers.

In the following simulation, we validate the incentive mech-anism in the network scenario, as shown in Fig. 6. The source,destination, eavesdroppers are fixed at the same location asbefore, while 15 intermediate nodes are distributed at the



9

0 10 20 30 40 50 60 70 8020

40

60

80

100

120

140

160

180

200

The amount of reward

The

util

ity o

f the

sou

rce

λ1=100,λ

2=1

λ1=100,λ

2=4

λ1=60, λ

2=1

λ1=60,λ

2=4

maximum

Figure 7. Utility of the source versus the amount of rewards.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

Intermediate nodes

Util

ities

of i

nter

med

iate

nod

es

Figure 8. Utilities of intermediate nodes averaged over fading when λ1 = 60and λ2 = 1.

locations marked in the figure. The source can choose thereward from the range between 0 and 100. Fig. 7 shows theutility of the source, averaged over fading distribution, versusthe amount of reward, for different λ1 and λ2. It can be seenthat the overall utility first increases and then decreases as thereward increases. The reason is that, at the beginning, withincreasing reward, the partners are willing to devote moretransmission power during cooperation, which leads to anincrease in the secrecy rate. However, when the reward keepsrising, the cost also increases, which will lower the overallutility. It can also be seen that there exists an optimal valueof the reward, with which the utility can be maximized. Itcan also be seen that a larger λ1 leads to a greater utility andpayment because the source node cares more about the secrecyrate and is willing to pay more reward to increase the secrecyrate. Moreover, a larger λ2 leads to a lower utility, since theintermediate node cares more about their energy consumptionand it will devote less power to cooperate given the samepayment.

Fig. 8 shows the utilities of intermediate nodes, averagedover fading distribution. It can be seen that the partners,who contribute to increase the secrecy rate of the source,can receive a certain amount of reward through cooperation,which implies that all the partners have the incentive forcooperation. Moreover, the node located at (0.9 km, -0.4 km)act as a jammer (node 13), while other nodes receiving non-zero rewards act as relays.

Fig. 9 shows the average utility of the source using PSOwith respect to the number of intermediate nodes, using MonteCarlo simulation. It can be seen that with PSO algorithm,the source can achieve higher utility than that using only

6 7 8 9 10 11 12 13 14 1560

70

80

90

100

110

120

130

Number of intermediate nodes

The

util

ity o

f the

sou

rce

C−EPSO

Figure 9. Utilities of the source with PSO when λ1 = 100 and λ2 = 1.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

Intermediate nodes

Util

ities

of i

nter

med

iate

nod

es

Figure 10. Utilities of intermediate nodes averaged over fading using PSOwhen λ1 = 60 and λ2 = 1.

C-E partner selection algorithm when the proposed incentivemechanism is applied. That is because the source can activelyaffect the power allocation of the intermediate nodes byintroducing the reward allocation weights. Through adjustingthe weights, the intermediate nodes can be better stimulatedto further improve the secrecy rate.

Fig. 10 shows the utilities of intermediate nodes, averagedover fading distribution using the same network scenario inFig. 6. Compared with Fig. 8, it can be seen that moreintermediate nodes are encouraged/stimulated to contribute toincrease the secrecy rate when PSO algorithm is applied.

VII. CONCLUSION

In this paper, we have proposed a cooperative frameworkto enhance security when multiple eavesdroppers exist. Twopartner selection algorithms have been devised, which canselect suitable relays or jammers to maximize the secrecy rate.A game-theoretic incentive mechanism has been proposed tostimulate the partners to participate into cooperation. With theproposed cooperative framework, security can be significantlyenhanced by preventing eavesdroppers from decoding the mes-sage transmitted, which can be applied to a network withoutinfrastructure for secure information transfer, or for secret keyexchange. Moreover, the proposed cooperative framework canbe combined with the upper layer cryptographic schemes tofurther enhance the security.

In the future, we will consider secure communications forscenario with the multiple source-destination pairs. In sucha scenario, the source nodes will compete with each otherto recruit from intermediate nodes for security enhancementwhile the intermediate nodes will have more options to gainreward from different source nodes.



10

APPENDIX

A. Derivation of (27)

The objective function can be rewritten as follows:

∂Ui

∂Pi=

riPm


j⊆C Pjrj

)2 − λ2 = 0

=⇒∑j⊆C

Pjrj =

√ri∑

j ̸=i,j⊆C Pjrj

λ2

=⇒Pi =1

ri(

√riPm

∑j ̸=i,j⊆C Pjrj

λ2−

∑j ̸=i,j⊆C

Pjrj)

Since the power cannot be negative and greater than themaximum power, we have (27).

B. Derivation of (28)

To solve the optimal transmission power of the selectedpartners, let

∂Ui

∂Pi= 0 =⇒

riPm


j⊆C Pjrj

)2 = λ2 (41)

Then, we haver1Pm

∑j ̸=1,j⊆C Pjrj

(∑

j⊆C Pjrj)2 = λ2

...riPm


(∑

j⊆C Pjrj)2 = λ2

...rnPm

∑j ̸=n,j⊆C Pjrj

(∑

j⊆C Pjrj)2 = λ2

(42)

Therefore, ∑j ̸=1,j⊆C Pjrj =

rir1


...∑j ̸=n,j⊆C Pjrj =

rirn


(43)

Since the summation of the left side equal to the summationof the right side, we have,

(n∑

j=1

rirj) ·

∑j ̸=i,j∈C

Pjrj = (n− 1)(∑

j ̸=i,j∈C

Pjrj + Piri). (44)

In addition,

Pi =1

ri(

√riPm


λ2−

∑j ̸=i,j⊆C

Pjrj) (45)

Then, we can calculate Pi as follows:

Pi =PmriBi

λ2(ri +Bi)2(46)

where Bi =(n−1)ri∑n

j=1rirj

−n+1.

Since Pi cannot be negative and greater than the maximum

power, we have

P ∗i = [min{ PmriBi

λ2(ri +Bi)2, Pmax}]+ (47)

REFERENCES

[1] X. Liang, K. Zhang, X. Shen, and X. Lin, “Security and privacyin mobile social networks: challenges and solutions,” IEEE WirelessCommunications, vol. 21, no. 1, pp. 33–41, 2014.

[2] H. V. Poor, “Information and inference in the wireless physical layer,”IEEE Wireless Communications, vol. 19, no. 2, pp. 40–47, 2012.

[3] L. Ozarow and A. Wyner, “Wire-tap channel ii,” in Advances inCryptology. Springer, pp. 33–50, 1985.

[4] J. Huang and A. Swindlehurst, “Robust secure transmission in misochannels based on worst-case optimization,” IEEE Transactions onSignal Processing, vol. 60, no. 4, pp. 1696–1707, 2012.

[5] D. Goeckel, S. Vasudevan, D. Towsley, S. Adams, Z. Ding, and K. Le-ung, “Artificial noise generation from cooperative relays for everlastingsecrecy in two-hop wireless networks,” IEEE Journal on Selected Areasin Communications, vol. 29, no. 10, pp. 2067–2076, 2011.

[6] N. Anand, S. Lee, and E. Knightly, “Strobe: Actively securing wirelesscommunications using zero-forcing beamforming,” in Proc. of IEEEINFOCOM’12, 2012.

[7] C. E. Shannon, “Communication theory of secrecy systems*,” Bellsystem technical journal, vol. 28, no. 4, pp. 656–715, 1949.

[8] L. Dong, Z. Han, A. Petropulu, and H. V. Poor, “Improving wirelessphysical layer security via cooperating relays,” IEEE Transactions onSignal Processing, vol. 58, no. 3, pp. 1875–1888, 2010.

[9] J. Yang, I.-M. Kim, and D. I. Kim, “Optimal cooperative jammingfor multiuser broadcast channel with multiple eavesdroppers,” IEEETransactions on Wireless Communications, vol. 12, no. 6, pp. 2840–2852, 2013.

[10] H. Wang, Q. Yin, and X. G. Xia, “Distributed beamforming for physical-layer security of two-way relay networks,” IEEE Transactions on SignalProcessing, vol. 60, pp. 3532–3545, 2012.

[11] N. Zhang, N. Lu, N. Cheng, J. W. Mark, and X. Shen, “Cooperativespectrum access towards secure information transfer for crns,” IEEEJournal on Selected Areas in Communications, vol. 31, no. 11, pp. 2453–2464, 2013.

[12] G. Zheng, L. Choo, and K. Wong, “Optimal cooperative jamming toenhance physical layer security using relays,” IEEE Transactions onSignal Processing, vol. 59, no. 3, pp. 1317–1322, 2011.

[13] J. Li, A. Petropulu, and S. Weber, “On cooperative relaying schemesfor wireless physical layer security,” IEEE Transactions on SignalProcessing, vol. 59, no. 99, pp. 4985–4997, 2011.

[14] Z. Han, N. Marina, M. Debbah, and A. Hjørungnes, “Physical layersecurity game: interaction between source, eavesdropper, and friendlyjammer,” EURASIP Journal on Wireless Communications and Network-ing, vol. 2009, pp. 1–11, 2009.

[15] M. Mahmoud and X. Shen, “Fescim: fair, efficient, and secure co-operation incentive mechanism for multihop cellular networks,” IEEETransactions on Mobile Computing, vol. 11, pp. 753–766, 2012.

[16] M. Wen, K. Zhang, J. Lei, X. Liang, R. Deng, and X. Shen, “Cit:A credit-based incentive tariff scheme with fraud-traceability for smartgrid,” Security and Communication Networks, 2013.

[17] I. Krikidis, J. S. Thompson, and S. McLaughlin, “Relay selection forsecure cooperative networks with jamming,” IEEE Transactions onWireless Communications, vol. 8, no. 10, pp. 5003–5011, 2009.

[18] Y. Zou, X. Wang, and W. Shen, “Optimal relay selection for physical-layer security in cooperative wireless networks,” IEEE Journal onSelected Areas in Communications, vol. 31, pp. 2099–2111, 2013.

[19] N. Zhang, N. Cheng, N. Lu, H. Zhou, J. W. Mark, and X. Shen, “Risk-aware cooperative spectrum access for multi-channel cognitive radionetworks,” IEEE Journal on Selected Areas in Communications, vol. 32,no. 3, pp. 516–527, 2014.

[20] R. Zhang, L. Song, Z. Han, and B. Jiao, “Physical layer security fortwo-way untrusted relaying with friendly jammers,” IEEE Transactionson Vehicular Technology, vol. 61, no. 8, pp. 3693–3704, 2012.

[21] H.-M. Wang, M. Luo, X.-G. Xia, and Q. Yin, “Joint cooperativebeamforming and jamming to secure af relay systems with individualpower constraint and no eavesdropper’s csi,” IEEE Signal ProcessingLetters, vol. 20, pp. 39–42, 2013.

[22] H. Wang, M. Luo, Q. Yin, and X. Xia, “Hybrid cooperative beamformingand jamming for physical-layer security of two-way relay networks,”IEEE Transactions on Information Forensics and Security, vol. 8, no. 12,pp. 2007–2020, 2013.



11

[23] N. Zhang, N. Cheng, X. Zhang, N. Lu, J. W. Mark, and X. Shen, “Acooperative scheme for secure communications with partner selectionand incentive mechanism,” in Proc. of IEEE WCSP’14, 2014.

[24] J. N. Laneman, D. N. Tse, and G. W. Wornell, “Cooperative diversityin wireless networks: Efficient protocols and outage behavior,” IEEETransactions on Information Theory, vol. 50, no. 12, pp. 3062–3080,2004.

[25] M. G. Damavandi, A. Abbasfar, and D. G. Michelson, “Peak power re-duction of ofdm systems through tone injection via parametric minimumcross-entropy method,” IEEE Transactions on Vehicular Technology,vol. 62, no. 4, pp. 1838–1843, 2013.

[26] R. Y. Rubinstein, “Optimization of computer simulation models withrare events,” European Journal of Operational Research, vol. 99, pp.89–112, 1997.

[27] J. B. Rosen, “Existence and uniqueness of equilibrium points for concaven-person games,” Journal of the Econometric Society Econometrica, pp.520–534, 1965.

[28] Y. Wang, Q. Zhang, Y. Zhang, and P. Chen, “Adaptive resource allocationfor cognitive radio networks with multiple primary networks,” EURASIPJournal on Wireless Communications and Networking, vol. 2012, no. 1,pp. 1–18, 2012.

[29] Q. Xu, X. Li, H. Ji, and X. Du, “Energy-efficient resource allocationfor heterogeneous services in ofdma downlink networks: Systematicperspective,” IEEE Transactions on Vehicular Technology, vol. 63, no. 5,pp. 2071–2082, 2014.

Ning Zhang (S’12) earned the Ph.D degree fromUniversity of Waterloo in 2015. He received hisB.Sc. degree from Beijing Jiaotong University andthe M.Sc. degree from Beijing University of Postsand Telecommunications, Beijing, China, in 2007and 2010, respectively. His current research interestsinclude dynamic spectrum access, 5G, physical layersecurity, and vehicular networks.

Nan Cheng (S’13) is currently a Ph.D. candidatein the department of Electrical and Computer En-gineering, the University of Waterloo, Waterloo,ON, Canada. He received his B.S. degree and M.S.degree from Tongji University, China, in 2009 and2012, respectively. Since 2012, he has been a re-search assistant in the Broadband CommunicationResearch group in ECE Department, the Universityof Waterloo. His research interests include vehicularcommunication networks, cognitive radio networks,and resource allocation in smart grid.

Ning Lu (S’12) earned the Ph.D degree fromUniversity of Waterloo in 2015. He received theB.Sc. and M.Sc. degrees from Tongji University,Shanghai, China, in 2007 and 2010, respectively. Heis currently a Mitacs postdoctoral research fellow inBLiNQ, Ottawa, ON. His current research interestsinclude capacity and delay analysis, media accesscontrol, and routing protocol design for vehicularnetworks.

Xiang Zhang received the B.Eng. degree in In-formation Engineering and the M.Sc. degree inComputer Application Technology from Universityof Electronic Science and Technology of China(UESTC), Chengdu, China, in 2003 and 2009, re-spectively. He is currently a Senior Engineer withthe School of Information and Software Engineering,UESTC. Between 2013 and 2014, he was a VisitingResearcher with the University of Waterloo, Water-loo, ON, Canada. His research interests includes theareas of vehicular communication and networking,

resources management, and multimedia streaming and services.

Jon W. Mark (M’62-SM’80-F’88-LF’03) receivedthe Ph.D. degree in electrical engineering from Mc-Master University in 1970. In September 1970 hejoined the Department of Electrical and ComputerEngineering, University of Waterloo, Waterloo, On-tario, where he is currently a Distinguished ProfessorEmeritus. He served as the Department Chairmanduring the period July 1984-June 1990. In 1996 heestablished the Center for Wireless Communications(CWC) at the University of Waterloo and is currentlyserving as its founding Director. Dr. Mark had been

on sabbatical leave at the following places: IBM Thomas J. Watson ResearchCenter, Yorktown Heights, NY, as a Visiting Research Scientist (1976-77);AT&T Bell Laboratories, Murray Hill, NJ, as a Resident Consultant (1982-83): Laboratoire MASI, universit pierre et marie curie, Paris France, asan Invited Professor (1990-91); and Department of Electrical Engineering,National University of Singapore, as a Visiting Professor (1994-95). He haspreviously worked in the areas of adaptive equalization, image and videocoding, spread spectrum communications, computer communication networks,ATM switch design and traffic management. His current research interests arein broadband wireless communications, resource and mobility management,and cross domain interworking.

Dr. Mark is a Life Fellow of IEEE and a Fellow of the Canadian Academyof Engineering. He is the recipient of the 2000 Canadian Award for Telecom-munications Research and the 2000 Award of Merit of the Education Founda-tion of the Federation of Chinese Canadian Professionals. He was an editor ofIEEE TRANSACTIONS ON COMMUNICATIONS (1983-1990), a memberof the Inter-Society Steering Committee of the IEEE/ACM TRANSACTIONSON NETWORKING (1992-2003), a member of the IEEE CommunicationsSociety Awards Committee (1995-1998), an editor of Wireless Networks(1993-2004), and an associate editor of Telecommunication Systems (1994-2004).

Xuemin (Sherman) Shen (IEEE M’97-SM’02-F’09) received the B.Sc.(1982) degree from DalianMaritime University (China) and the M.Sc. (1987)and Ph.D. degrees (1990) from Rutgers University,New Jersey (USA), all in electrical engineering.He is a Professor and University Research Chair,Department of Electrical and Computer Engineering,University of Waterloo, Canada. He was the Asso-ciate Chair for Graduate Studies from 2004 to 2008.Dr. Shen’s research focuses on resource managementin interconnected wireless/wired networks, wireless

network security, social networks, smart grid, and vehicular ad hoc and sensornetworks. He is a co-author/editor of six books, and has published morethan 600 papers and book chapters in wireless communications and networks,control and filtering. Dr. Shen served as the Technical Program CommitteeChair/Co-Chair for IEEE Infocom’14, IEEE VTC’10 Fall, the Symposia Chairfor IEEE ICC’10, the Tutorial Chair for IEEE VTC’11 Spring and IEEEICC’08, the Technical Program Committee Chair for IEEE Globecom’07,the General Co-Chair for Chinacom’07 and QShine’06, the Chair for IEEECommunications Society Technical Committee on Wireless Communications,and P2P Communications and Networking. He also serves/served as theEditor-in-Chief for IEEE Network, Peer-to-Peer Networking and Application,and IET Communications; a Founding Area Editor for IEEE Transactionson Wireless Communications; an Associate Editor for IEEE Transactionson Vehicular Technology, Computer Networks, and ACM/Wireless Networks,etc.; and the Guest Editor for IEEE JSAC, IEEE Wireless Communications,IEEE Communications Magazine, and ACM Mobile Networks and Appli-cations, etc. Dr. Shen received the Excellent Graduate Supervision Awardin 2006, and the Outstanding Performance Award in 2004, 2007 and 2010from the University of Waterloo, the Premier’s Research Excellence Award(PREA) in 2003 from the Province of Ontario, Canada, and the DistinguishedPerformance Award in 2002 and 2007 from the Faculty of Engineering,University of Waterloo. Dr. Shen is a registered Professional Engineer ofOntario, Canada, an IEEE Fellow, an Engineering Institute of Canada Fellow,a Canadian Academy of Engineering Fellow, and a Distinguished Lecturer ofIEEE Vehicular Technology Society and Communications Society.

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