vehicle-assisted device-to-device data delivery for smart grid

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 65, NO. 4, APRIL 2016 2325

Vehicle-Assisted Device-to-Device Data Deliveryfor Smart Grid

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

Abstract—In this paper, we propose a heterogeneous frameworkto deliver smart grid (SG) data cost effectively. The data generatedby distributed SG loads, and generation units should be deliveredto the utility control center (UCC) within the tolerated delay,which is crucial for SG applications. To this end, a heterogeneouscommunication framework is proposed, where the cellular net-work (CN) provides ubiquitous yet expensive data transmission,and vehicle-assisted device-to-device (D2D) communications areleveraged to offload the CN by delivering the delay-tolerant SGdata in a store–carry–forward fashion with low cost. To improvethe offloading and cost performance of the proposed framework,we put effort in the following aspects: 1) optimal forwardingschemes to optimally select vehicles to carry and forward thedata; and 2) mode selection and dynamic resource allocation tomaximize the amount of data delivered by D2D communications,reduce the cost of SG data delivery, and guarantee the fairnessamong SG users. Simulation results are given to validate proposedapproaches and demonstrate that the proposed framework isefficient in saving cost for the utility and offloading the CN.

Index Terms—Device-to-device (D2D) communications, off-loading, resource allocation, smart grid (SG), vehicularcommunication.

I. INTRODUCTION

SMART GRID (SG) has recently attracted much researchattention from both the power and communication fields.

SG refers to a modernized and advanced power system thataims to monitor, manage, and deliver electric power in a moreefficient and reliable manner by incorporating state-of-the-artcommunication, computing, and control technologies into thetraditional power grid [1]. With SG, many benefits could beachieved, such as generation diversification, demand response,reduction of overall carbon footprint, etc. [2].

In SG, smart meters and other intelligent devices are denselydeployed throughout the grid for monitoring, management, andcontrol. SG data are generated by these devices and should bereliably transmitted to the utility to carry out SG functionalities.A salient feature of SG data is that the amount of the SG

Manuscript received September 10, 2014; revised January 16, 2015; acceptedMarch 5, 2015. Date of publication March 20, 2015; date of current versionApril 14, 2016. The review of this paper was coordinated by Prof. J. Tang.

N. Cheng, N. Lu, N. Zhang, X. Shen, and J. W. Mark are with the Departmentof Electrical and Computer Engineering, University of Waterloo, Waterloo,ON N2L 3G1, Canada (e-mail: [email protected]; [email protected];[email protected]; [email protected]; [email protected]).

T. Yang is with Navigation College, Dalian Maritime University, Liaoning116026, China (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2015.2415512

TABLE IEXAMPLES OF DELAY-TOLERANT SG APPLICATIONS

data is tremendous. For example, compared with the currentmonthly metering, the smart meters measure and store meterreadings much more frequently (e.g., every 2 min), which yieldsa 8000-fold increase in daily data [3]. It is predicted that thetotal amount of SG data will increase to over 75 200 TB in2015 [4]. In addition to the data volume, delay requirementsof SG applications vary from seconds to hours. In other words,some applications can tolerate certain delays, e.g., the economicdispatch is performed every 5 min for California IndependentSystem Operator (CAISO) [5]. A recent study [6] has presenteddifferent categories of SG applications and specified the delayand bandwidth requirements of different SG applications. Inthis paper, the proposed vehicle-assisted data delivery frame-work is targeted on delay-tolerant SG applications, examples ofwhich are given in Table I.

Transmitting the massive SG data is about to swamp existingcommunication infrastructures and thereby poses cumbersomechallenges. Power line communication (PLC) has been consid-ered a candidate for transferring the data of SG applications [7],which can offer local area network (LAN) connectivity, Internetaccess, and certain command and control capabilities. However,low penetration of PLC devices, the interoperability problem,and short communication range become impediments for thesuccess of PLC in the market [7], [8]. Smart energy profile solu-tions are proposed to use ZigBee, Wi-Fi, and PLC for meteringand home area networks (HANs) [9]. However, the limitation incapacity and in the communication range of such technologiesmakes them proper for microgrid network infrastructure ratherthan the long-distance data transmission. In addition, the costmight be very high to deploy the fiber-optic network in all thedistributed SG loads and generation units [10]. Recently, cellu-lar technology, e.g., GSM, Third-Generation (3G), and Long-Term Evolution (LTE), has emerged as a viable alternativefor SG data transmission. IEEE Standards Board has approvedP2030 smart grid (SG) interoperability standards developmentto make guidelines for SG interoperability of energy technologyand information technology operation with the electric power

0018-9545 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

2326 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 65, NO. 4, APRIL 2016

system. In P2030, 3G cellular systems have been recommendedas the communication backhaul network for SG. However,the mobile data tsunami, i.e., the explosive growth of mobiledata traffic, has already introduced an overloading problem inthe cellular network (CN) [11]. Thus, the transmission of themassive SG data may very likely further congest the CN anddegrade the performance of both SG and other mobile appli-cations. Moreover, the cost (e.g., subscription fees) of usingCNs to transmit a large amount of SG data may be prohibitive.Therefore, to mitigate the congestion of CNs and reduce thecost incurred by SG data transfer, an efficient heterogeneousdata delivery framework is necessary, which can deliver theSG data by means of both CN and complementary networktechnologies.

As a promising solution to offload CNs, device-to-device(D2D) communication technology has gained much attentionrecently. The basic tenet of D2D communications is that mo-bile users can communicate directly with each other insteadof using CNs and the backhaul networks. By utilizing theproximity of mobile users and direct data transmission, D2Dcommunications can typically offload CNs, reduce the cost, andprovide better quality of service (QoS) for mobile users [12].Wi-Fi access points (APs) deployed at hotspots such as home,malls, and workplaces by individuals or cellular carriers canalso be used to offload CNs [13]. Mobile users can downloadand upload data through APs instead of cellular base stations(BSs) when Wi-Fi access is available. Basically, D2D commu-nications can be classified into two categories: fully controlledD2D mode and loosely controlled D2D mode [14]. In the fullycontrolled mode, D2D users are completely controlled by theCN, including device discovery, connection setup, and others.The device discovery and D2D connection setup can be donequickly since the BS controls the whole network and has deepcontextual information. There are two resource reuse modes infully controlled D2D communications, i.e., underlaying D2Dcommunication and overlaying D2D communication. In un-derlaying D2D communication, cellular resources are reusedfor D2D communications. In order to control the interference,the spectrum resource should be carefully allocated. The over-laying D2D communication is employed in the paper, wherededicated cellular resources are allocated for D2D communica-tions. The amount of allocated resources depends on targetedperformance of cellular and D2D communications as well asthe status of the CN, such as the number of users, traffic load,etc. As D2D communications share the cellular licensed band,D2D users are charged by the cellular operator. In looselycontrolled mode, D2D communications are carried out with lessor no involvement of the CN and usually use the unlicensedband, such as Wi-Fi or Bluetooth. Therefore, the charge for thismode is typically lower than that of fully controlled D2D mode.Considering features of different modes, it would be wise tochoose the appropriate mode under different circumstances.

In this paper, we propose a heterogeneous data deliveryframework where a vehicle-assisted two-hop D2D communi-cation network serves as a complementary network to the CNto transmit SG data, in order to offload the CN and reducethe cost for SG data transmission. In the proposed framework,entities that produce, store, and transmit SG data are named

data sources (DSs), which can be distributed SG loads such ashouses and buildings, and SG generation units such as windturbines and photovoltaic panels. D2D communications are uti-lized to forward SG data from DSs to passing vehicles. When avehicle contacts roadside APs (RAPs), it can upload the carrieddata to the utility control center (UCC). RAPs can be a drive-thru Wi-Fi network deployed throughout the city by cellularcarriers or the roadside infrastructure deployed by the electricutility. If the data fails to be delivered before the deadline(i.e., delay requirement), it is transmitted through the CN im-mediately. Such a vehicle-assisted data dissemination networkis usually referred to as a vehicular delay-tolerant network(VDTN) [15]. More specifically, to maximize the chance ofdata delivery, two optimal forwarding schemes are proposed forDSs to select the best vehicle to carry the data. Since differentD2D transmission modes can achieve different data deliveryperformance and incur different costs, a mode selection schemeis proposed to minimize the overall expected cost for the utility.As D2D communications share the cellular resources in fullycontrolled mode, the cellular resources may not be sufficient tosupport all D2D communications simultaneously, particularlywhen the density of DSs is high in a certain geographic area.Hence, we present a dynamic resource allocation scheme tomaximize the average data delivery ratio, reduce the cost, andguarantee fairness among DSs.

The reasons for using vehicle-assisted SG data delivery areas follows. First, Internet access is predicted to become astandard feature of motor vehicles [16]. Therefore, vehicles canhave the communication capability with cellular BSs or Wi-FiAPs. Second, the mobility of vehicles introduces intermittentconnectivity to RAPs and facilitates opportunistic delivery forthe delay-tolerant SG data. Some measurements have beendone to evaluate the performance of such vehicle drive-thrunetworks and proposed enhanced mechanisms, such as fastconnection establishment and reliable data transfer, to improvethe performance that may be degraded by the high vehiclemobility [17]–[19]. It is shown that a vehicle can meet an APto initial data transfer every tens of seconds in average, andtransmit maximum 100 MB of data in one such transmissionopportunity. It is shown that the store–carry–forward paradigmis suitable for disseminating delay-tolerant data, particularlywhen the vehicle density is high, e.g., 4000 veh/mi2 in thedowntown area of San Francisco, CA, USA [20]. Finally,the deployment of the carrier Wi-Fi networks facilitates thevehicle-assisted SG data delivery. One of the notable issuesin providing Wi-Fi access to vehicles is the authentication andassociation process, which could cost a considerable amountof the short connection time resulted by high mobility ofvehicles. The problem can be addressed by recent advances inPasspoint/Hotspot 2.0, which makes Wi-Fi more competitiveto provide secure connectivity and seamless roaming [21]. InHotspot 2.0, a new preassociation protocol is introduced, wheremobile devices are allowed to obtain the information about theservices and service providers that can be reached via a hotspotbefore it associates. Based on the information, the mobiledevices can identify which APs are suitable and authenticateto a remote service provider [e.g., a mobile network operator(MNO)], which is much faster than requiring authentication

CHENG et al.: VEHICLE-ASSISTED D2D DATA DELIVERY FOR SG 2327

before learning such information. Assisted by Hotspot 2.0,pioneering MNOs, e.g., AT&T and China Mobile, have rolledout carrier Wi-Fi networks and integrated them with the CN.Carrier Wi-Fi is a powerful, cost-effective, and manageable toolfor MNOs to increase the network capacity and can provide thesupport of easy access, guaranteed QoS, security, and roamingwith advanced Wi-Fi functionality, as well as new businessmodels and services. Furthermore, the carrier Wi-Fi networkscan be used to deliver the SG data to avoid the security problemsthat are led by public Wi-Fi access and save the capital andoperational expenditure of utility to deploy and maintain RAPs.

The contributions of this paper are as follows. First, anheterogeneous data delivery framework is designed to incorpo-rates D2D communications and vehicle-assisted data deliveryto offload SG data from CNs and reduce the data transfer cost.To the best of our knowledge, this paper represents the firststudy that considers the effect of the opportunistic data deliveryon designing aspects of D2D communications. Second, optimalforwarding schemes are proposed, through which optimal ve-hicles are selected to carry and forward the data, to improvethe probability of successful delivery. Third, mode selectionand resource allocation schemes are provided to maximize thetotal average delivery ratio, guarantee fairness among DSs, andreduce the cost for the utility.

Implementing the proposed heterogeneous data deliveryframework, the CN can be considerably offloaded since a largeamount of SG data is delivered through RAPs using Wi-Fi.In addition, the delivery performance of data in areas withpoor cellular coverage can be improved. Through the rewardmechanism and resource allocation scheme, the money paidby the utility to transfer SG data can be saved. Moreover, theproposed data delivery framework can be also applied to otherdata upload/download scenarios with little changes.

The remainder of this paper is organized as follows.Section II reviews the related work. Section III describes thesystem model. In Section IV, two data forwarding schemesbased on optimal stopping rules are proposed and analyzed. InSection V, the mode selection and resource allocation schemesare introduced. Section VI evaluates the performance of theproposed framework. Section VII concludes this paper.

II. RELATED RESEARCH WORK

D2D communication technology is a recent research focusand is in active development, mostly in the context of LTE CNs.With D2D communications, different transmission modes maycoexist. For instance, in our vehicle-assisted D2D offloadingframework, the existence of both Wi-Fi and cellular radiosprovides three available modes for data transmission, which isdescribed in Section III-C. Typically, the communication modeis selected based on different criteria. In [22], the optimal modeis selected for all the devices based on the system equation,which captures the information of link gains, noise levels, etc.Another considered criterion is to maximize the power effi-ciency, which is achieved by a joint mode selection and powerallocation scheme proposed in [23]. The mode selection basedon transmission cost and rate requirement is studied in [24] forLTE-advanced networks. In this paper, different from existing

works, the mode selection scheme is designed to minimize thecost for the utility by considering the delivery performance, i.e.,the amount of data delivered by D2D communications, and thetransmission cost of each mode.

In D2D communication underlaying CNs, the resources suchas power and time–frequency resources should be scheduledcarefully to reduce interference and improve network perfor-mance. In addition, in SG, when DS density is high, it isrequired to efficiently allocate the limited resources. The re-source allocation problem in D2D communications is discussedin [25]–[27]. In [25], three D2D modes, namely nonorthogonalsharing, orthogonal sharing, and cellular mode are studied,and the optimal transmission power is given. In [26], optimalpower allocation is analyzed for the secondary users that op-portunistically use D2D mode and licensed resources. In [27],a network-assisted method is proposed to intelligently manageresources of devices. Resource blocks (RBs) and power arejointly allocated to guarantee the signal quality of all users. Inour proposed resource allocation scheme, the main target is tomaximize the overall data delivery performance and guaranteefairness among DSs.

The feasibility of vehicle-assisted data delivery for delay-tolerant applications is validated in [15] and [28]. In [15], avehicle-assisted data forwarding scheme is proposed, taking thepredictable vehicle mobility into consideration. The optimalpacket transmission in VDTNs is studied in [28]. An optimaldecision is obtained based on the constrained Markov decisionprocess and then the optimal packet transmission rate is chosen.

III. SYSTEM MODEL

In this paper, we propose a D2D data delivery framework thatutilizes vehicles, roadside networks, and D2D communicationsto assist to deliver the delay-tolerant SG data.1 SG data canbe forwarded to and carried by vehicles and uploaded to UCCthrough RAPs, instead of being transmitted through the CN.A summary of the mathematical notations used in this paperis given in Table II. The framework is applicable to differentvehicle densities and mobility patterns, and RAP deployments,although they might have impacts on the performance of theframework.

A. Network Model

As shown in Fig. 1, we consider an urban area with DSsgenerating and transferring SG data. LTE CNs, which supportboth common cellular communications and the D2D underlaycommunications, are assumed to fully cover the area. DSs andvehicles are considered equipped with both a Wi-Fi radio and acellular radio to transmit SG data, as assumed in [10]. RAPs areconnected to the UCC and deployed throughout the city. DSsare geographically divided into clusters. Denote by C the set ofDSs within a cluster with the area of Ω. Let ρ be the averagedensity of DSs in a cluster. The number of DSs in the cluster isN = |C|, with the expectation E[N ] = ρΩ.

1From now on, we use SG data and delay-tolerant SG data interchangeably.


TABLE IIUSEFUL NOTATIONS

Fig. 1. Network model.

B. Application Model

We aim to utilize D2D communications to transfer the delay-tolerant SG data to UCC. Each DS generates a data block (DB,denoted by B) every generation interval tg by aggregating thedata from smart meters, sensors, and generation units in a smallgeographic area (e.g., a building) using HAN technologies andtemporarily stores it. The utility requests the data from all DSsevery request interval (RI), which is denoted by T (T > tg).NB = �T/tg� represents the number of DBs generated in aDS within an RI. Thus, DBs generated within an RI should besuccessfully delivered before the end of the RI.

C. Communication Model

As shown in Fig. 2, a two-hop vehicle-assisted forward–carry–upload D2D communication framework is proposed forSG data delivery as a complementary network to the CN. In ourframework, we define D2D communications using Wi-Fi radiosas Wi-Fi mode (MW ) and D2D communications using cellularradios as cellular mode (MC ). Correspondingly, the original

Fig. 2. Two-hop communication model.

cellular communications through the CN and the EvolvedNode B (eNB) is defined as direct cellular mode (MD). InMW , a standard Wi-Fi technology is used, e.g., IEEE 802.11g,whereas in MC , the underlaying cellular resources are utilizedfor D2D communications. Vehicles are identified as deliveryagents of SG data. In the first communication hop, the DSchooses a passing vehicle as the D2D receiver and forwards thestored DBs, by either MW or MC . In the second communicationhop, the vehicle carrying DBs tries the best effort to upload thedata when it encounters a RAP.

Each DB can be forwarded to at most one vehicle. By doingso, there is only one duplication of each DB, which yieldsan optimal throughput of the network [29]. At the end of anRI, DBs that are not received are considered not deliverablethrough D2D communications and thus are transferred throughMD. The pros and cons of different D2D modes are listed asfollows.

• MW : This is a loosely controlled D2D mode that uses thefree Wi-Fi band. The communication radius is assumed100 m, which is the communication range for reliableand high-rate data transmission based on [30] and [31].Because of the high vehicle mobility, there may be someproblem of communication between vehicle and RAPs.It is shown in [30] and many other measurement papers


that the communication between a vehicle and an RAPcan experience three phases. When the vehicle just movesin or will leave the coverage area of the RAP, the datatransmission is very unreliable and inefficient because ofthe connection establishment delay, low SNR, and slowrate adaptation, etc. Thus, we consider the communicationrange of MW to be 100 m, within which the performanceof data transmission is good.

• MC : This is a fully controlled D2D mode which reuses theLTE uplink (UL) resources. We assume that D2D commu-nications nonorthogonally shares the UL resources withthe cellular users, in which a resource can be sharedby both a D2D pair and a common cellular user, ratherthan exclusively allocated to either of them. A similarassumption is shown [25] and [32]. The transmissionpower P is controlled to manage the interference to eNBor to guarantee a minimum rate of normal cellular users. Away to achieve this can be found in [25]. The transmissionrange is determined by the allowed transmission powerbut is typically larger than that of MW .

• MD: These are common cellular communications througheNBs. Data can be transmitted with a negligible delay. Aswe discussed, this mode would pose a burden on the CNfor handling SG data traffic.

D. Cost and Reward Model

We define the cost as the money paid for delivering SG data,including using D2D and cellular communications. Differenttransmission modes incur different costs. MW uses free Wi-Fiband; thus, the cost is neglected in this paper. DSs using MC

reuse cellular resources and may require the involvement of theeNB such as device discovery, session setup, and interferencemanagement. Thus, MC incurs cost CC to transmit each DB.The cost of MD, which is denoted by CDC , is the highest amongthe three modes.2 Normally accessing Wi-Fi can incur somecost. However, since the cost is much lower than CC and CDC ,in this paper, we simply set it to zero.

In our framework, DSs pay the cost for using MC and MD.Since SG has yet to become reality, there remains great uncer-tainty as to how to use cellular communications. For example,the deployment strategy for cellular devices is not clear. Forsome small-scale customer-owned sites, the communicationmodules are expected to be deployed by the owners [33].Therefore, we consider that DSs deploy cellular devices andthus pay CC and CDC for each DB transmission using MC andMD, respectively. Benefited from the transmitted SG data, theutility rewards DSs for transmitting the SG data. For simplicity,we consider

Ri = γC (1)

where Ri is the reward given to DS i (denoted by Si) fortransferring a DB, C is the cost to transmit a DB, and γ ≥ 1

2The actual cost should be based on transmitting unit size of data. In thispaper, we assume each DB with the same size for simplicity; thus, the cost isincurred for transmitting each DB.

is called the reward parameter. To motivate DSs to transmit SGdata using D2D communications, γ > 1 for MC and γ = 1 forMD. Note that, for MW ,Ri is set to a constant θ.

E. Mobility Model

Vehicles on the road typically have different velocities anddirections. We consider independent movements of vehicles,i.e., the trajectory of one vehicle is independent with that ofothers. At a specific time instant, the remaining time for avehicle to meet the next RAP is referred to as the meeting timeof the vehicle, which is denoted by Tm. Due to the intermittentconnectivity between vehicles and RAPs, it is considered thatTm follows an exponential distribution with parameter μ, i.e.,Tm ∼ exp(μ), where X ∼ exp(α) denotes that random vari-able X follows an exponential distribution with parameter α. Asimilar model can be seen in [34]. Furthermore, vehicle arrivalsat Si follow a Poisson process N = (Nt)t≥0 with parameter λi.Note that λi depends on the communication range of Si, whichis denoted by Ri, and a larger Ri implies a larger λi. Moreover,the road traffic can impact μ and λi, e.g., λi at rush hour isoften larger than that at night at the same location. It is assumedthat vehicles are equipped with GPS devices and digital maps,which contain the location information of the deployed RAPs.Thus, a vehicle can estimate Tm at any time, using its currentlocation, navigation, and RAP information.

IV. OPTIMAL-STOPPING-BASED

OPPORTUNISTIC FORWARDING

Here, optimal forwarding schemes are proposed for DSs tooptimally choose vehicles as D2D receivers to forward DBsto maximize the successful delivery probability, based on thedeadline of the DB, and the statistics of vehicle arrival andmeeting time. The performance of the proposed forwardingschemes, including expected delivery delay and delivery prob-ability, is theoretically analyzed. The result will be used fordesigning mode selection and resource allocation schemes inSection V.

A. Problem Formulation

Since a DB can be forwarded only once to a vehicle and allDBs that are not delivered by the deadline will be transmittedusing the most expensive MD, it is expected that DSs should op-timally choose vehicles to forward DBs so that the probabilitythat the data are successfully delivered is as high as possible. Inthis way, more data can be delivered by D2D communications,which can efficiently offload the CN and reduce the cost forthe utility.

Consider a typical DB, e.g., Bj . The delivery probabilityof Bj , which is denoted by Pd,j , is defined as the probabilitythat Bj is successfully delivered to UCC before its deadline,by using D2D communications. Denote by vj the vehicle thatis chosen to carry Bj , t0 the generation instant of Bj , treq therequest instant of Bj , and Tl = treq − t0 the life time of Bj , asshown in Fig. 3. Moreover, denote by tf the instant when Bj

is forwarded to vj , tr the instant when vj meets the first RAP


Fig. 3. Time duration in the analysis.

after carrying Bj , and tu the instant when Bj is received byUCC. The first contact delay (FCD) of Bj , which is denotedby DF,j , is defined as the time duration from the generation ofBj to the time vj meets the first RAP after carrying Bj , i.e.,DF,j = tr − t0.

To successfully deliver Bj , vj should contact at least oneRAP before treq. We define TEX = tu − tr as the time fora DB to be actually received by the UCC after the vehiclecarrying the DB contacts the first RAP. TEX is a randomvariable for each DB, depending on the amount of data carriedby the vehicle and the communication environment such astransmission rate, sojourn contact time with the RAP, vehicledensity, etc. Therefore, the delivery probability of Bj can berepresented by the probability that the summation of DF,j andTEX is no larger than Tl, i.e.,

Pd,j = Pr(DF,j + TEX,j ≤ Tl,j) = FTEX(Tl,j −DF,j) (2)

where FTEXis the cumulative distribution function (cdf) of

TEX. According to (2), to increase the delivery probability, weaim to minimize the FCD of DBs, i.e., to minimize

DF,j = Tva,j + Tvd,j (3)

where Tva,j = tf − t0 is the time duration from the instantwhen Bj is generated to the instant when Bj is forwarded to vj ,and Tvd,j = tr − tf is the time duration taking vj to meet thefirst RAP after carrying Bj , which follows the same distributionas Tm. For DSs, choosing different vehicles to forward Bj leadsto different DF,j because both Tva,j and Tvd,j are randomvariables. However, when a vehicle arrives at Si,DF,j can beobserved by Si based on the following observations. Obviously,Si has the knowledge of Tva when a vehicle arrives. Moreover,vehicles can obtain estimated Tm and provide it to Si as Tvd. Tominimize FCD of a DB, DSs should choose a vehicle that offersminimum DF as the forwarder. It is formulated as an optimalforwarding problem, based on the theory of optimal stopping.

For optimal stopping problem [35], let G = (Gn)n≥0 be asequence of random variables, which can be observed at time n.For each stage n, one can make a decision to stop and receivethe known reward Rn = R(Gn), where R(Gn) is a functionof Gn, or to continue and observe Gn+1. However, the pastreward cannot be recalled. The optimal stopping problem is tostop at stage n to maximize the expected reward (or minimizethe expected cost). In the following, we propose two optimalforwarding schemes to minimize FCD.

B. Time-Oriented Optimal Forwarding

When Bj is generated in Si,Si can decide the best time toset up a D2D link and forward Bj to a vehicle. This forwardingscheme is called time-oriented optimal forwarding (TOOF).Divide Tl into small time slots with identical length Δtn, andthe number of time slots is Nt = �Tl/Δtn�. Thus, Tva can beapproximated by nΔtn if Bj is forwarded in the nth slot. Tofind the optimal time slot to forward DBs is an optimal stoppingproblem with finite horizon Nt. Such a problem can be solvedby backward induction [36].

In TOOF, there are Nt stages (time slots), i.e., the stagen = 1, 2, . . . , Nt.Si tries to forward Bj no later than stageNt. Therefore, we can first find the optimal forwarding ruleat stage Nt − 1. By knowing the optimal forwarding rule atstage Nt − 1, we can find the optimal forwarding rule at stageNt − 2 and inductively to stage 1. Let V Nt

n denote the minimumexpected FCD to forward Bj starting from stage n, i.e.,

V Ntn = E

[min

{Rn(Tvd), V

Ntn+1

}]where Rn(Tvd) = nΔtn + Tvd and E is the expectation. WithNt, V

Nt

Nt= E[RNt

(Tvd)] = Tl + (1/μ), at the last stage, in-ductively, at stage n

V Ntn = E

[min

{Rn(Tvd), V

Ntn+1

}]

=

∞∫0

min{nΔtn + Tvd, V

Ntn+1

}dFTvd

(Tvd)

=

VNtn+1∫0

(nΔtn + x)μe−μxdx+

∞∫V

Ntn+1

V Ntn+1μe

−μxdx

=

(nΔtn +

1μ

)(1 − e−μV

Ntn+1

)(4)

where FTvd(x) = 1 − e−μx is the cdf of Tvd. Thus, the optimal

rule at each stage n, i.e., V Ntn is obtained, whose value is

nondecreasing with n. TOOF is to forward Bj in the first slot n′

in which a vehicle can offer Tvd ≤ V Nt

n′+1 − n′Δtn, using eitherMW or MC .

C. Vehicle-Oriented Optimal Forwarding

The time-oriented forwarding scheme is to choose the besttime to forward the stored DSs, which has a finite horizon.However, if we consider the forwarding problem from theperspective of individual forwarders, i.e., vehicles, the forward-ing strategy is different. The forwarding scheme of selectingthe exact vehicle to forward the DB is called vehicle-orientedoptimal forwarding (VOOF). Let v1, v2, v3, . . . be the vehiclesthat sequentially pass by Si after the generation of Bj accordingto Poisson arrival with parameter λi, and Xk be the timeduration to wait for vk+1 if Si decides not to forward Bj tovk. X0 is the time duration before v1 comes. Thus, {Xk} canbe seen as the cost paid to wait for following offers. Accordingto the property of Poisson process, {Xk} are independent


Fig. 4. Choosing proper Δtn. (a) Analytical FCD with respect to Δtn and λi. (b) Relation between λi and optimal Δtn.

and identically distributed (i.i.d.) random variables followingexponential distribution with the mean 1/λi. Thus, the problemof choosing a vehicle to forward Bj is then the optimal stoppingproblem with infinite horizon, in order to minimize

Rk = Tvd,k +

k−1∑n=0

Xn

where Tvd,k is Tvd of vk .Consider the case that Si pays X0 and observes Tvd,1 =

tvd,1. Let V ∗ denote the optimal rule of forwarding Bj to avehicle. Thus, if tvd,1 > V ∗,Si should continue to wait for thenext vehicle. If tvd,1 ≤ V ∗,Si should forward the DB to thisvehicle. If Si decides to continue, then tvd,1 is lost, and X0

has already been paid. Because the past vehicles cannot be usedagain, when the next vehicle arrives, the problem is like startingover again and is invariant in time [36]. Thus, the argument oftvd,1 can be made at each vehicle arrival. For Si, if it decidesnot to choose an arbitrary vk to forward Bj , then the minimumexpected FCD after vk is V ∗ + E[Xk]. Thus, V ∗ satisfies

V ∗ = E [min{Tvd,k +Xk, V∗ +Xk}]

= E[Xk] + E [min{Tvd,k, V∗}]

=1λi

+

V ∗∫0

xdFTvd,k(x) +

∞∫V ∗

V ∗dFTvd,k(x)

=1λi

+1μ

(1 − e−μV ∗)

. (5)

Then, by solving (5), the optimal forwarding rule V ∗ can beobtained. In VOOF, the first vehicle vk, which can offerTvd,k ≤V ∗, will be selected to carry Bj .

D. Discussion

1) Difference Between TOOF and VOOF: In TOOF, the DSsknow how to optimally forward the DB in a given time interval.However, they do not care which vehicle forwards DBs or evenwhether DBs can be forwarded within an arbitrary interval.Furthermore, since the forwarding rule of DBs is related to

Tl, a vehicle may not be qualified to carry all DBs, given thatthere are more than one DB currently stored, which makes thisscheme more complex. The calculation of optimal forwardingrule V Nt

n is through backward induction, which is easier thanthat of VOOF.

In VOOF, the forwarding scheme is from the perspective ofindividual vehicles. The optimal forwarding rules for differentDBs are same and unchanged over time. Therefore, a vehiclethat satisfies Tvd,k ≤ V ∗ can carry all stored DBs in one time.Tva of each vehicle is considered the summation of a series ofi.i.d. random variables. Although accurate, it is more complexto solve the optimal forwarding rule V ∗ using (5).

2) Choosing Proper Δtn: In TOOF, the choice of the in-terval length, i.e., Δtn, is crucial for the performance of thescheme. A larger value of Δtn will reduce the accuracy ofestimating Tva in the derivation of optimal rules; on the otherhand, a smaller value of Δtn will increase the computationalcomplexity of optimal rule E(V Nt

n ). The choice of Δtn isstrongly dependent on the vehicle arrival rate λi. Intuitively,when the vehicle arrival rate is high, it is better to reduceΔtn to increase the accuracy of the optimal rules. However,when λi is small, it is more feasible to increase Δtn to reduceunnecessary calculation and the number of intervals in whichthere is no vehicle arriving. Using the results obtained earlier,the analytical FCD of TOOF with respect to different Δtn andλi is shown in Fig. 4(a). For each value of λi, there is an optimalvalue of Δtn, in which FCD is minimum. Fig. 4(b) shows therelation between λi and optimalΔtn. It can be seen that optimalΔtn has an inverse proportional relation with λi, which is inaccordance with the intuition.

3) Expected First Contact Delay and Delivery Probability:FCD is crucial for the overall performance of the proposed datadelivery method, and our optimal forwarding schemes aim toreduce FCD as much as possible. Therefore, we theoreticallyanalyze FCD and the delivery probability that can be achievedby both forwarding schemes.

Denote EFCD−TOOF by the expected FCD of Bj usingTOOF, which can be calculated by

EFCD−TOOF =

Nt∑i=1

(i−1∏k=1

PNT,k

)(1 − PNT,i)EFCD,i (6)


where PNT,k is the probability that, in the kth slot, there is novehicle chosen to carry Bj , and EFCD,i is the expected FCDif Bj is carried by a vehicle in the ith slot. Since vehiclesarrive following a Poisson process, PNT,k can be calculated asfollows:

PNT,k =

∞∑m=0

Pn(m)PmNTv,k (7)

where Pn(m) represents the probability that m vehicles arrivewithin Δtn and PNTv,k represents the probability that a vehiclearrives within the kth slot but is not eligible to carry Bj ,i.e., Tvd > V Nt

k+1 − kΔtn.Pn(m) = e(−λiΔtn)((λiΔtn)m/m!)

for Si.PNTv,k is calculated as follows:

PNTv,k = Pr((Tvd + kΔtn) > V Nt

k+1

)= 1 − FTvd

(V Nt

k+1 − kΔtn

)= e−μ(V Nt

k+1−kΔtn). (8)

EFCD,i can be calculated as the expectation of FCD in (3), i.e.,

EFCD,i = E

[Tvd|Tvd ≤

(V Nti+1 − iΔtn

)]+ iΔtn

=

∫ VNti+1−iΔtn

0 xdFTvd(x)

FTvd

(V Nti+1 − iΔtn

) + iΔtn

=

1μ −

(V Nti+1 − iΔtn + 1

μ

)e−μ(V Nt

i+1−iΔtn)

1 − e−μ(V Nti+1−iΔtn)

+ iΔtn.

(9)

Then, we can obtain the expected FCD for TOOF using (6). Thedelivery probability of Bj can be calculated using (2) and (6) asfollows:

PTOOF=

Nt∑i=1

(i−1∏k=1

PNT,k

)(1 − PNT,i)FTEX

(Tl − EFCD,i).

(10)

The expected FCD and delivery probability by using VOOFcan be calculated in a similar way.

4) Security and Privacy Issue: The security issues are veryimportant in SG since poor security would offset any costeffectiveness in SG. In vehicle-assisted SG data delivery mech-anisms, the sensitive SG data might be exposed to the randomvehicles or modified by malicious vehicular users. In the lit-erature, secure data forwarding schemes have been proposedfor vehicular networks, which are proved to satisfy certainsecurity requirements, e.g., data confidentiality, integrity, au-thentication, and privacy preservation. In [37], a social-basedprivacy-preserving packet-forwarding protocol for vehicularDTNs called SPRING is proposed in which RAPs deployedin intersections are used to forward packets among vehiclesin a delay-tolerant manner to improve the delivery ratio andnetwork reliability, whereas the privacy preservation can beachieved, and most attacks, e.g., packet analysis attack, black

(gray) hole attacks, and packet tracing attack, can be resisted.In SPRING, the privacy preservation and attack resistance isachieved through a conditional privacy-preserving authentica-tion technique, which is a group signature mechanism dedicatedfor vehicular communications. Similar schemes can be found in[38] and [39]. These schemes can preserve the privacy and resistsecurity attacks when the packets are forwarded to vehicles tostore, carry, and forward. Therefore, the related methods andtechniques can be utilized in our proposed vehicle-assisted datadelivery method to achieve privacy preservation and secure datadelivery in SG.

V. MODE SELECTION AND RESOURCE ALLOCATION

The forwarding schemes are proposed and analyzed based onvehicle arrival rate λ. In the proposed framework, there are twoD2D transmission modes, i.e., MW and MC . Different modeswith different λ can have different delivery performance andtransmission costs. Based on the analysis of delivery probabilityin Section IV, a novel mode selection scheme is proposed toreduce the expected cost for the utility. Furthermore, a dynamicresource allocation scheme is introduced for DSs using MC inorder to increase the total average delivery ratio and guaranteefairness among DSs.

A. Problem Formulation

1) Mode Selection: Note that MD has the highest cost andposes a burden on the CN. Therefore, it is only used to transmitDBs, which are not successfully delivered by the deadline.For D2D modes, i.e., MW and MC , they should be carefullyselected. If MW is used, DSs can forward the data to vehicleswith no cost. However, due to the limited communication range,it is not that easy for DSs to find an optimal forwarder (asshown in Section IV). Therefore, the total delivery ratio couldbe low. On the other hand, if MC is used, transmission costis incurred when forwarding DBs to vehicles. Nevertheless,the total delivery ratio could be higher than that of MW dueto a larger communication range. As all undelivered DBs bythe deadline will be transmitted using the most costly MD, themode selection should be considered to reduce the cost.

2) Resource Allocation: With the development of SG andthe increasing popularity of smart meters and distributed gen-erations, there would be a growing deployment of DSs thatgenerate and transmit SG data. As a result, the DS density ρcould be very high. On the other hand, cellular resources arelimited. For example, if LTE UL or downlink (DL) resourcesare reused by D2D communications, the number of D2D pairsthat are allowed to transmit simultaneously can be calculatedby Ws/Wb, where Ws is the system bandwidth, and Wb is thesize of a resource block allocated to a D2D pair [40]. Given afixed amount of resources, the number of D2D pairs that can besupported is limited. In the literature, a resource spatial reusescheme can be applied to support more D2D pairs. However, itis still possible that, even within a DS cluster, the total resourcesare not enough to support all the D2D pairs, particularly whenρ is high. Thus, how to efficiently allocate limited resources insuch a dense scenario needs to be studied.


From (1), DSs that achieve higher delivery ratio will getmore reward than others. This implies a fairness problem: SomeDSs are unsatisfied with the reward obtained because they arerequired to conduct MC transmissions as others (as a result ofmode selection) but get less of a reward. As an alternative,the utility can reward the same amount to all DSs using MC

regardless of how much data they deliver. However, some DSsmay refuse to transmit in order to save money. Thus, an intuitivesolution is to dynamically allocate the resources to make eachDS achieve a long-term average delivery ratio that is larger thana delivery ratio requirement Ψ̄ and maximize Ψ̄. By doing this,the fairness among DSs can be guaranteed, more SG data canbe offloaded via D2D delivery, and the data transfer cost can bereduced.

Consider an RI, e.g., the kth RI, the delivery ratio of DBsgenerated in Si can be expressed as

Ψki =

κki

nDB=

κkiT

Δtg

(11)

where κki is the number of DBs that are generated in Si

within RIk and successfully delivered before the end of RIk byusing D2D communications. Then, the long-term average datadelivery ratio Ψi can be calculated by

Ψi = lim infK→∞

1K

K∑k=1

Ψki . (12)

Let C′ denote the set of DSs using MC in a cluster. Ourobjective is to design a resource allocation scheme η to

maxη

Ψ̄ (13)

s.t. Ψi ≥ Ψ̄ ∀Si ∈ C′. (14)

The fairness can be guaranteed because if an arbitrary Si

achieves a larger Ψi, it indicates that Si obtains too muchresource; thus, Ψ̄ is not maximized. To this end, a resource allo-cation scheme called fair delivery-ratio maximization resourceallocation (FDMRA) is proposed to dynamically allocate LTEUL resources to DSs using MC .

B. Mode Selection

With the knowledge of the delivery probability of DBs, wecan obtain the expected cost of using MW and MC for anarbitrary DS, which is denoted by Si, respectively. Then, theD2D mode is determined for Si to reduced the total cost for theutility.

Assume that the maximum transmission range of MW is dmW .We estimate the communication range of MC through a simplepath-loss model

Pr = cPtd−α (15)

where d is the distance between D2D transmitter and receiver,c is the path-loss constant, α is the path-loss exponent, and Pt

and Pr are D2D transmission and receive power using MC ,respectively. Assume that the target rate of the D2D pair to

transmit DBs is rD2D. Then, the target signal-to-interference-and-noise ratio (SINR) at D2D receivers can be calculated bySINRD2D = 2rD2D/Wb − 1. On the other hand, by the definitionof SINR, we have

SINRD2D =Pr

I0,i(16)

where I0,i is the maximum interference-plus-noise power thatcan be experienced by a D2D receiver within the cluster that Si

belongs to, which is denoted by Fi. Given the locations of allclusters, I0,i can be calculated by

I0,i =∑

Fj =Fi

Ij,i + n0 (17)

where Ij,i is the maximum interference from cluster Fj to Fi,and n0 is the noise power. Since DSs in a cluster will not reusethe same resource, there is no interference from other D2D pairsin the same cluster. Ij,i can be calculated by

Ij,i = cPmaxt,j

(dmini,j

)−α(18)

where Pmaxt,j is the maximum allowed transmission power of

DSs using MC in cluster Fj , and dmini,j is the minimum distance

between cluster Fj and Fi. From (15) and (16), the D2Dcommunication range is given by

d =

⎡⎣I0

(2

rD2DB − 1

)cPt

⎤⎦− 1

α

. (19)

Finally, by substituting c, α, I0, and the maximum transmis-sion power into (19), we can get the maximum expectedD2D communication range of Si using MC , which is denotedby dmi,C .

Then, we can estimate the expected cost for the utility whena DS uses MW or MC , by considering the expected D2Dcommunication range of both modes. The expected cost ofusing MW is

CU,W =

(1 − 1

nDB

nDB∑k=1

[Pd|λ (dmW ) , Nt(k)]

)· CDC (20)

and the expected cost of using MC can be calculated by (21)

CU,C =γCCnDB

nDB∑k=1

[Pd|λ

(dmi,C), Nt(k)

]

+ CDC

(1 − 1

nDB

nDB∑k=1

[Pd|λ

(dmi,C), Nt(k)

])(21)

where [Pd|λ,Nt] is the delivery probability of an individualDB, given the vehicle arrival rate λ and the lifetime of thisDB Tl = NtΔtn, which can be obtained from (10). Nt(k) canbe calculated by Nt(k) = (T − (k − 1)tg)/Δtn. By compar-ison, the mode with less expected cost is then selected. Themode selection decision does not change with time since λ is


determined by D2D communication ranges, which are static fora DS through (19).

C. Cellular Resource Allocation

We consider a dense deployment of DSs in a specific cluster.Recall that C′ is the set of DSs using MC in this cluster with|C′| = Nc. The number of available resources (RBs) is M . Tomaximize Ψ̄ while satisfying the constraint (14), we propose anovel resource allocation scheme called FDMRA by means ofstochastic optimization theory [41].

We establish a virtual queue for each Si ∈ C′. The queuedynamics are given by

Qi(k + 1) = Qi(k)−Ψk+1i + Ψ̄ (22)

where Qi(k) is the virtual queue backlog of Si in the kth RI.Let Qi(0) = 0 for all Si ∈ C

′. In (22), the arrival rate Ψ̄ is therequired long-term average delivery ratio, and the departure rateE[Ψk

i ] is the actual delivery ratio in the kth RI. Thus, the queuebacklog Qi(k) indicates the information of whether Si satisfiesthe long-term average delivery ratio requirement Ψ̄ by the endof the kth RI. If Qi(k) is positive, it indicates that the deliveryratio of Si by the kth RI is smaller than Ψ̄, and vice versa.

Definition 1: Q(k) is said to be stable if

Pr

{lim sup

k→∞

Q(k)

k≤ 0

}= 1 (23)

which means ∀Si ∈ C′, lim supk→∞ Qi(k)/k ≤ 0 with proba-bility one.

Lemma 1: Given the set C′, the delivery ratio requirement(14) is satisfied if and only if Q(k) is stable.

Proof: Using the law of telescoping sums to (22), we have

Qi(k)−Qi(0) = kΨ̄−k∑

j=1

Ψji . (24)

With Qi(0) = 0 and rearrangement of terms, we have

lim supk→∞

Qi(k)

k= Ψ̄− lim sup

k→∞

1k

k∑j=1

Ψji . (25)

If Q(k) is stable, with probability one,lim supk→∞(Qi(k)/k) ≤ 0 for all Si ∈ C

′. Then, thefollowing holds with probability one:

lim supk→∞

1k

k∑j=1

Ψji ≥ Ψ̄ ∀Si ∈ C

′. (26)

Therefore, the constraint (14) is satisfied. The necessity can beproved similarly. �

Utilizing the concept of virtual queue, a resource allocationscheme called FDMRA is proposed.

Fair Delivery-Ratio Maximization Resource Allocation: Inthe beginning of each RI (RIk), the queue backlogQ(k) of eachDS in C′ is announced to the eNB by the utility. The queuebacklogs are decreasingly ordered so that Q1(k) ≥ Q2(k) ≥. . . ≥ QN(k). In each slot within RIk (slot is defined in TOOF

in Section IV), RBs are allocated to DSs in C′, which have the

largest Q(k) and find an optimal forwarder.Definition 2: A resource allocation scheme η is called a max-

weight allocation scheme if it maximizes

Nc∑i=1

[Qi(k)]+ ·Ψk

i (η) (27)

where Ψki (η) is the actual delivery ratio of Si in kth RI under

the allocation scheme η.Lemma 2: FDMRA is a max-weight allocation scheme.

With FDMRA and Ψ̄ < E[Ψ(ηFDMRA)],Q(k) is stable, whereE[Ψ(ηFDMRA)] is the expected delivery ratio for each DS.

Proof: We define a nonnegative Lyapunov functionL(Q(k)) as follows:

L (Q(k))Δ=

12

∑i∈C′

([Qi(k)]

+)2 . (28)

Thus

L (Q(k + 1))− L (Q(k))

=12

∑i∈C′

(([Qi(k + 1)]+

)2 − ([Qi(k)]+)2)

≤ 12

∑i∈C′

(([Qi(k)]

+ −Ψk+1i (η) + Ψ̄

)2 − ([Qv(k)]+)2)

=1

2

∑i∈C′

((Ψk+1

i (η)− Ψ̄)2−2 [Qv(k)]

+(Ψk+1i (η)−Ψ̄

)).

(29)

Define the Lyapunov drift by

Δ(Q(k))Δ= E [L (Q(k + 1))− L (Q(k)) |Q(k)] . (30)

Therefore, we have

Δ(Q(k)) =12

∑i∈C′

E

[(Ψk+1

i (η)− Ψ̄)2 |Q(k)

]

−∑i∈C′

E[[Qi(k)]

+ (Ψk+1i (η)− Ψ̄

)|Q(k)

]≤ B +

∑i∈C′

[Qi(k)]+ Ψ̄

−∑i∈C′

[Qi(k)]+E[Ψk+1

i (η)|Q(k)]

(31)

where B is constant so that

12

∑i∈C′

E

[(Ψk+1

i (η)− Ψ̄)2 |Q(k)

]≤ B

since both Ψk+1i (η) and Ψ̄ can be upper bounded by 1. A

resource allocation that maximizes∑i∈C′

[Qi(k)]+E[Ψk+1

i (η)|Q(k)]

(32)


TABLE IIISIMULATION PARAMETERS

is needed to minimize Δ(Q(k)), which is called a max-weightallocation. The proposed FDMRA scheme is a max-weightallocation since it guarantees that, at any time, the resources areallocated to those DSs, which have the largest Q(k). In otherwords, with FDMRA applied, DSs with larger Q(k) can obtainhigher delivery ratio Ψk

i , which in turn maximizes (32). Thus,we have∑i∈C′

[Qi(k)]+E[Ψk+1

i (ηFDMRA)|Q(k)]

≥∑i∈C′

[Qi(k)]+E[Ψk+1

i (η∗)|Q(k)]

(33)

where η∗ is a non-max-weight resource allocation scheme.Substituting (33) into (31) yields

Δ(Q(k)) ≤ B +∑i∈C′

[Qi(k)]+ Ψ̄

−∑i∈C′

[Qi(k)]+E[Ψk+1

i (ηFDMRA)|Q(k)]

= B −∑i∈C′

[Qi(k)]+ (

E[Ψk+1

i (ηFDMRA)|Q(k)]− Ψ̄)

= B −∑i∈C′

[Qi(k)]+ ε (34)

where ε = E[Ψk+1i (ηFDMRA)]− Ψ̄ > 0 is the difference be-

tween the expected delivery ratio using FDMRA and the re-quired delivery ratio. By utilizing FDMRA, cellular resourcesare evenly allocated to each Si ∈ C

′ from the long-term per-spective. Thus, we have E[Ψk

i (ηFDMRA)] = E[Ψ(ηFDMRA)],∀Si ∈ C′. Therefore, with

ε = E [Ψ(ηFDMRA)]− Ψ̄ (35)

and taking expectation, E[Δ(Q(k))] is upper bounded

E [Δ (Q(k))] ≤ B − ε∑i∈C′

E[[Qi(k)]

+] . (36)

Using the definition of Δ(Q(k)) in (30), we have

E [Δ (Q(k))] = E [E [L (Q(k + 1))− L (Q(k))|Q(k)]

= E [L (Q(k + 1))]− E [L (Q(k))] . (37)

Substituting (37) into (36) and doing telescoping sums over k ∈{1, 2, . . . ,K − 1} yields

E [L (Q(K))]−E [L (Q(0))]≤BK−ε

K−1∑k=1

∑i∈C′

E[[Qi(k)]

+] .(38)

By taking a lim sup and using the fact that E[L(Q(K))] ≥ 0and E[L(Q(0))] = 0, we have

lim supK→∞

1K

K−1∑k=1

∑i∈C′

E[[Qi(k)]

+] ≤ B

ε. (39)

Thus, all queues are stable, and the long-term average backlogis upper bounded by B/ε. �

Theorem 1: By applying FDMRA, the constraint (14) issatisfied, and the optimization target Ψ̄ = E[Ψ(ηFDMRA)]− ε,where ε is a positive number.

From Lemmas 1 and 2, Theorem 1 can be easily proved. The-orem 1 indicates that, by using FDMRA, each DS can achievethe long-term average delivery ratio that is larger than Ψ̄, whichis upper bounded by and arbitrarily close to E[Ψ(ηFDMRA)].The derivation of E[Ψ(ηFDMRA)] is given in Appendix.

VI. PERFORMANCE EVALUATION

Here, we conduct simulations to evaluate the performanceof the proposed offloading framework. An urban scenario isconsidered with LTE coverage and randomly deployed RAPs.We employ event-driven simulation, where events include DBgeneration, request, and vehicle arrival at DSs and contact withRAPs. Simulation parameters are listed in Table III.

The performance of the proposed optimal forwardingschemes are shown in Fig. 5. The effect of λ and μ is studied.Simulation and analytical results of FCD are shown in Fig. 5(a)and (b). In Fig. 5(a), the impact of the vehicle arrival rate λis presented. The increase in λ reduces the time for DSs towait for the eligible D2D receiver; thus, the FCD is reduced.In Fig. 5(b), the impact of mean meeting time 1/μ is shown.A small value of 1/μ indicates that the value of Tvd is small,which leads to a small value of FCD. The reason for VOOFachieving smaller FCD than TOOF is that, in TOOF, vehiclearrivals within a time slot are considered arrivals at the be-ginning of the time slot to simplify the scheme. Moreover,it is shown that the theoretical and simulation results matcheach other, which validates the analysis. The overall deliveryratio of DBs within an RI is shown in Fig. 5(c) and (d). Wecompare the delivery ratio of using TOOF and VOOF with thatof a simple scheme called best effort (BE), in which the firstvehicle with Tvd < treq − tf is selected. BE is to guarantee thatvehicles carrying DBs can contact at least one RAP before therequest time. However, BE fails to consider TEX and achievesthe lowest delivery ratio among the three.

The performance of the mode selection scheme is shown inFig. 6, where CC is set to 1. Fig. 6(a) gives the percentage of


Fig. 5. Performance of optimal forwarding scheme. (a) FCD versus λ. (b) FCD versus μ. (c) Deliver ratio versus λ. (d) Deliver ratio versus μ.

DSs that select MC with respect to the direct cellular com-munication cost CDC and reward parameter γ. It can be seenthat with the increase in CDC , the number of DSs selecting MC

increases because with larger CDC , the increase in delivery ratiowill save more cost. The percentage decreases with the increaseof reward parameter γ. This is because the utility pays more forrewarding DSs using MC . Fig. 6(b) shows the average cost forthe utility with the appropriate mode selected for each DS. Thecost of the proposed scheme is compared with that of the casein which only MW is utilized for D2D communications. It canbe seen that the proposed mode selection scheme can achieve alower average cost.

The performance of the proposed FDMRA scheme is shownin Fig. 7. In Fig. 7(a), the average vehicle arrival rate λis set to 1 per min, and the long-term (over 70 h) averagedelivery ratio of each DS and the theoretical delivery ratio ofFDMRA (E[Ψ(ηFDMRA)]) are compared. It can be seen thatDSs achieve almost the same long-term average delivery ratio,which demonstrates fairness of FDMRA. Moreover, the long-term average delivery ratios of DSs are close to theoreticalresult E[Ψ(ηFDMRA)], which validates our analysis. The de-livery ratios decrease with the increase in DS density ρ andthe decrease in the number of RB M . This is because a largenumber of DSs in a cluster or a small number of RBs will makethe resource scarcity problem more severe. Fig. 7(b) shows therelationship between the delivery ratios and the average vehicle

arrival rate λ when M is set to 10. With the increase in λ,the delivery ratios also increase because DSs can easily findan optimal vehicle to forward DBs.

The overall performance of the proposed D2D based offload-ing scheme is shown in Fig. 8, where CDC , CC , and γ are setto 7, 1, and 1.3, respectively. Clusters are randomly located inthe cell. We compare the proposed offloading framework withtwo schemes in which only MW is used, and both MW and MC

are used without cellular resource spatial reuse within a cell.It is shown in Fig. 8(a) that, by using the proposed offloadingscheme, about 49% to 58% SG data can be offloaded fromthe CN, according to different DS densities. Compared withthe other two schemes (on average data offloading percentageof 25% and 8%, respectively), the proposed framework canefficiently offload the CN. Fig. 8(b) shows the total saved costfor the utility. By offloading more, fewer data are transmittedusing MD, which in turn reduces more cost for the utility,particularly when ρ is high.

VII. CONCLUSION

In this paper, we have developed a vehicle-assisted offloadingframework for SG delay-tolerant applications through D2Dcommunications. We have first proposed two optimal forward-ing schemes for DSs to optimally select a vehicle to forwardSG data. Then, we have designed a mode selection scheme


Fig. 6. Performance of mode selection scheme. (a) Mode selection results.(b) Average cost for the utility.

and a resource allocation scheme called FDMRA to minimizethe expected cost and maximize the data delivery ratio, respec-tively. Simulation results have validated the performance of theproposed forwarding, mode selection, and resource allocationschemes and demonstrated that the proposed framework canoffload up to 58% SG data from the CN, guarantee the fairnessamong DSs, and save much cost for the utility, particularlywhen DS density is high. Our future work will considerthe differentiation of SG applications in the data offloadingframework.

APPENDIX

CALCULATION OF EXPECTED DELIVERY

RATIO IN FDMRA

We calculate the expected delivery ratio for DSs in an RIusing MC and FDMRA. For simplicity, it is considered thatthe maximum transmission powers Pi are the same for DSswithin the same cluster. Thus, by (19), D2D communicationranges for DSs in a cluster are the same and so are the vehiclearrival rates λ. We focus on the expected delivery ratio whenTOOF is used, whereas the case in which VOOF is employedis similar. Because the case N ≤ M is trivial, we consider thecase N > M , where the number of resources is smaller thanthat of potential D2D pairs.

Fig. 7. Performance of FDMRA. (a) Delivery ratio with respect to RB number.(b) Delivery ratio with respect to average λ.

Recall that, if a DS can have the resource and forward DBswhenever it wants to, the expected delivery ratio of DBs in theDS within an RI is

E[PRI] =T

tg

∑Tl

Nt(Tl)∑i=1

(i−1∏k=1

PNT,k

)(1 − PNT,i)

· FTEX(Tl − EFCD,i) (40)

where Tl ∈ {T, T − tg, T − 2tg, . . . , tg, 0} is the lifetime DBs,and Nt(Tl) = �Tl/tg�. Thus, in an RI, the M DSs with thelargest queue backlog Q(k) (we denote this set of DSs byCM ) will all achieve an expected delivery ratio E[PRI] becauseFDMRA will allocate an RB to these DSs whenever they findan optimal vehicle to forward data. However, in each slot,not all DSs in CM can find an optimal forwarder due to theoptimal forwarding rules described in Section IV. Thus, DSs inC′ \ CM may be allocated with RBs when they have DBs toforward.

Consider a DB with lifetime Tl. Note that each DS in C′

generates a DB at the same time, and the number of such DBsis N . In a time slot k, the probability Po that a DS will find anoptimal forwarder is given by Po = 1 − PNT,k , where PNT,k


Fig. 8. Overall performance. (a) Overall offloading performance. (b) Overallamount of saved cost.

can be calculated by (7). In the first time slot, it is expectedthat MPo RBs are allocated to DSs in CM . Thus, the expectednumber of DBs in C′ \ CM , which are forwarded in slot 1, is

n1f = min {M(1 − Po), (N −M)Po} . (41)

Therefore, in the second slot, the number of DBs to be for-warded in CM is M(1 − Po). We have the expected numberof DBs in C′ \ CM , which are forwarded in slot 2, i.e.,

n2f =min

{M−M(1−Po)Po,

[N−M−n1

f

]+Po

}. (42)

Similarly, in slot s

nsf =min

⎧⎨⎩M−M(1−Po)

s−1Po,

[N−M−

s−1∑i=1

nif

]+Po

⎫⎬⎭ .

(43)

Based on the analysis in Section IV-D.3, the expected numberout of the N −M DBs in C′ \ CM , which can be successfullydelivered to the UCC, is

nd(Tl) =

Nt(Tl)∑i=1

nifFTEX

(Tl − EFCD,i) (44)

where EFCD,i can be calculated by (9). Thus, the number ofDBs that are generated in DSs in C′ \ CM and successfullydelivered to UCC within a whole RI is

nd,RI =∑Tl

nd(Tl) =∑Tl

Nt(Tl)∑i=1

nifFTEX

(Tl − EFCD,i) (45)

where Tl ∈ {T, T − tg, T − 2tg, . . . , tg, 0}. Finally, the ex-pected delivery ratio for a DS in an RI is

E [Ψ(ηFDMRA)]=

MTE[PRI]tg

+ nd,RI

N Ttg

=M

NE[PRI]+

tgnd,RI

NT.

(46)

REFERENCES

[1] H. Farhangi, “The path of the smart grid,” IEEE Power Energy Mag.,vol. 8, no. 1, pp. 18–28, Jan./Feb. 2010.

[2] M. Fouda, Z. Fadlullah, N. Kato, R. Lu, and X. Shen, “A lightweightmessage authentication scheme for smart grid communications,” IEEETrans. Smart Grid, vol. 2, no. 4, pp. 675–685, Dec. 2011.

[3] P. Nyeng and J. Ostergaard, “Information and communications systemsfor control-by-price of distributed energy resources and flexible demand,”IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 334–341, Jun. 2011.

[4] R. Yu et al., “Cognitive radio based hierarchical communicationsinfrastructure for smart grid,” IEEE Netw., vol. 25, no. 5, pp. 6–14,Sep./Oct. 2011.

[5] Y. V. Makarov, C. Loutan, J. Ma, and P. de Mello, “Operational impacts ofwind generation on california power systems,” IEEE Trans. Power Syst.,vol. 24, no. 2, pp. 1039–1050, May 2009.

[6] Application Domain Partitioning for Smart Grid Security. [Online].Available: http://www.trilliantinc.com/de/education/upcoming-webinar-application-domain-partitioning-for-smart-grid-security

[7] S. Galli, A. Scaglione, and Z. Wang, “For the grid and through the grid:The role of power line communications in the smart grid,” Proc. IEEE,vol. 99, no. 6, pp. 998–1027, Jun. 2011.

[8] G. Deconinck, “An evaluation of two-way communication meansfor advanced metering in flanders (Belgium),” in Proc. IEEE IMTC,Victoria, BC, Canada, May 2008, pp. 900–905.

[9] ZigBee Alliance, Smart Energy Profile 2. [Online]. Available:http://www.zigbee.org/zigbee-for-developers/applicationstandards/zigbeesmartenergy/

[10] H. Liang, B. J. Choi, A. Abdrabou, W. Zhuang, and X. Shen, “De-centralized economic dispatch in microgrids via heterogeneous wirelessnetworks,” IEEE J. Sel. Areas Commun., vol. 30, no. 6, pp. 1061–1074,Jul. 2012.

[11] Cisco, Cisco Visual Networking Index: Global Mobile Data TrafficForecast Update. [Online]. Available: http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.html

[12] N. Golrezaei, A. F. Molisch, and A. G. Dimakis, “Base-station as-sisted device-to-device communications for high-throughput wirelessvideo networks,” in Proc. IEEE ICC, Ottawa, ON, Canada, Jun 2012,pp. 7077–7081.

[13] K. Lee, J. Lee, Y. Yi, I. Rhee, and S. Chong, “Mobile data offloading:How much can wifi deliver?” IEEE/ACM Trans. Netw., vol. 21, no. 2,pp. 536–550, Apr. 2013.

[14] L. Lei, Z. Zhong, C. Lin, and X. Shen, “Operator controlled device-to-device communications in LTE-advanced networks,” IEEE WirelessCommun., vol. 19, no. 3, pp. 96–104, Jun. 2012.

[15] J. Zhao and G. Cao, “VADD: Vehicle-assisted data delivery in vehicular adhoc networks,” IEEE Trans. Veh. Technol., vol. 57, no. 3, pp. 1910–1922,May 2008.

[16] KPMG’s Global Automotive Executive Survey. [Online]. Available:http://www.kpmg.com/GE/en/IssuesAndInsights/ArticlesPublications/Documents/Global-automotive-executive-survey-2012.pdf

[17] J. Eriksson, H. Balakrishnan, and S. Madden, “Cabernet: Vehicular con-tent delivery using WiFi,” in Proc. ACM MobiCom, San Francisco, CA,USA, Sep. 2008.

[18] V. Bychkovsky, B. Hull, A. Miu, H. Balakrishnan, and S. Madden,“A measurement study of vehicular Internet access using in situ Wi-Fi


networks,” in Proc. ACM MobiCom, Los Angeles, CA, USA,Sep. 2006, pp. 199–210.

[19] N. Cheng, N. Lu, N. Zhang, X. Shen, and J. Mark, “Vehicular WiFioffloading: Challenges and solutions,” Veh. Commun., vol. 1, no. 1,pp. 13–21, Jan. 2014.

[20] High Density and High Concentrations of Cars. [Online]. Available: http://www.planetizen.com/node/45622

[21] Seemless Wi-Fi Offload: From Vision to Reality. [Online].Available: http://www.enterprisechannelsmea.com/FileManagerImages/12047.pdf

[22] S. Hakola, T. Chen, J. Lehtomaki, and T. Koskela, “Device-to-Device(D2D) communication in cellular network-performance analysis of op-timum and practical communication mode selection,” in Proc. IEEEWCNC, Sydney, N.S.W., Australia, Apr. 2010, pp. 1–6.

[23] M. Jung, K. Hwang, and S. Choi, “Joint mode selection and powerallocation scheme for power-efficient Device-to-Device (D2D) com-munication,” in Proc. IEEE VTC, Yokohama, Japan, May 2012,pp. 1–5.

[24] K. Akkarajitsakul, P. Phunchongharn, E. Hossain, and V. K. Bhargava,“Mode selection for energy-efficient D2D communications in LTE-advanced networks: A coalitional game approach,” in Proc. IEEE ICCS,Singapore, Jun. 2012, pp. 488–492.

[25] C.-H. Yu, K. Doppler, C. B. Ribeiro, and O. Tirkkonen, “Resource shar-ing optimization for device-to-device communication underlaying cellularnetworks,” IEEE Trans. Wireless Commun., vol. 10, no. 8, pp. 2752–2763,Aug. 2011.

[26] P. Cheng, L. Deng, H. Yu, Y. Xu, and H. Wang, “Resource allo-cation for cognitive networks with D2D communication: An evolu-tionary approach,” in Proc. IEEE WCNC, Paris, France, Apr. 2012,pp. 2671–2676.

[27] A.-H. Tsai, L.-C. Wang, J.-H. Huang, and T.-M. Lin, “Intelligent resourcemanagement for device-to-device (D2D) communications in heteroge-neous networks,” in Proc. IEEE WPMC, Taipei, Taiwan, Sep. 2012,pp. 75–79.

[28] D. Niyato and P. Wang, “Optimization of the mobile router and trafficsources in vehicular delay-tolerant network,” IEEE Trans. Veh. Technol.,vol. 58, no. 9, pp. 5095–5104, Nov. 2009.

[29] N. Lu, T. Luan, M. Wang, X. Shen, and F. Bai, “Bounds of asymptoticperformance limits of social-proximity vehicular networks,” IEEE/ACMTrans. Netw., vol. 22, no. 3, pp. 812–825, Jun. 2014.

[30] J. Ott and D. Kutscher, “Drive-thru Internet: IEEE 802.11 b forautomobile,” in Proc. IEEE INFOCOM, Hong Kong, Mar. 2004,pp. 362–373.

[31] W. L. Tan, W. C. Lau, O. Yue, and T. H. Hui, “Analytical models andperformance evaluation of drive-thru Internet systems,” IEEE J. Sel. AreasCommun., vol. 29, no. 1, pp. 207–222, Jan. 2011.

[32] X. Ma, R. Yin, G. Yu, and Z. Zhang, “A distributed relay selection methodfor relay assisted device-to-device communication system,” in Proc. IEEEPIMRC, Sydney, N.S.W., Australia, Sep. 2012, pp. 1020–1024.

[33] Interview With Dave Bassett. [Online]. Available: http://smartgrid.ieee.org/questions-and-answers/

[34] T. Luan, L. Cai, J. Chen, X. Shen, and F. Bai, “Engineering a distributedinfrastructure for large-scale cost-effective content dissemination overurban vehicular networks,” IEEE Trans. Veh. Technol., vol. 63, no. 3,pp. 1419–1435, Mar. 2014.

[35] G. Peskir, A. Shiryaev, and A. Shiryaev, Optimal Stopping and Free-Boundary Problems, vol. 10. Cambridge, MA, USA: Birkhaüser,2006.

[36] “Optimal Stopping and Applications.” [Online]. Available: http://www.math.ucla.edu/~tom/Stopping/Contents.html

[37] R. Lu, X. Lin, and X. Shen, “Spring: A social-based privacy-preservingpacket forwarding protocol for vehicular delay tolerant networks,” inProc. IEEE INFOCOM, 2010, pp. 1–9.

[38] X. Lin, R. Lu, X. Liang, and X. Shen, “STAP: A social-tier-assisted packetforwarding protocol for achieving receiver-location privacy preservationin VANETs,” in Proc. IEEE INFOCOM, 2011, pp. 2147–2155.

[39] K. Zhang, X. Liang, R. Lu, and X. Shen, “VSLP: Voronoi-socialspot-aided packet forwarding protocol with receiver location privacy inMSNS,” in Proc. IEEE GLOBECOM, Anaheim, CA, USA, Dec. 2012,pp. 348–353.

[40] LTE Resource Guide, Anritsu, Kanagawa, Japan. [Online]. Available:http://www.anritsu.com/en-GB/Media-Room/Newsletters/files/anritsu_lte_guide.pdf

[41] M. J. Neely, “Stochastic network optimization with application to com-munication and queueing systems,” in Synthesis Lectures on Communi-cation Networks, vol. 3. San Rafael, CA, USA: Morgan and Claypool,2010, pp. 1–211.

Nan Cheng (S’13) received the B.S. and M.S. de-grees from Tongji University, Shanghai, China, in2009 and 2012, respectively. He is currently workingtoward the Ph.D. degree with the Department ofElectrical and Computer Engineering, the Universityof Waterloo, Waterloo, ON, Canada.

Since 2012, he has been a Research Assistant withthe Broadband Communication Research group, De-partment of Electrical and Computer Engineering,University of Waterloo. His research interests in-clude vehicular communication networks, cognitive

radio networks, resource allocation in smart grid, and cellular traffic offloading.

Ning Lu (S’12) received the B.Sc. and M.Sc. de-grees from Tongji University, Shanghai, China, in2007 and 2010, respectively. He is currently workingtoward the Ph.D. degree with the Department ofElectrical and Computer Engineering, University ofWaterloo, Waterloo, ON, Canada. His current re-search interests include capacity and delay analysis,media access control, and routing protocol design forvehicular networks.

Mr. Lu served as a Technical Program CommitteeMember for the 2012 IEEE International Symposium

on Personal, Indoor, and Mobile Radio Communications.

Ning Zhang (S’12) received the B.Sc. degree fromBeijing Jiaotong University, Beijing, China, in 2007and the M.Sc. degree from Beijing University ofPosts and Telecommunications in 2010. He is cur-rently working toward the Ph.D. degree with theDepartment of Electrical and Computer Engineering,University of Waterloo, Waterloo, ON, Canada.

His current research interests include cooperativenetworking, cognitive radio networks, physical-layersecurity, and vehicular networks.

Tingting Yang (M’13) received the B.Sc. and Ph.D.degrees from Dalian Maritime University, Dalian,China, in 2004 and 2010, respectively.

Since September 2012, she has been a Visit-ing Scholar with the Broadband CommunicationsResearch Laboratory, Department of Electrical andComputer Engineering, University of Waterloo, ON,Canada. She is currently an Associate Professorwith the Navigation College, Dalian Maritime Uni-versity. Her research interests include maritimewideband communication networks, delay-tolerant

networking, and green wireless communication.Dr. Yang served as a Technical Program Committee Member for the 2014

IEEE Conference on Smart Grid Communications and the 2014 IEEE Inter-national Conference on Scalable Computing and Communications, as well asthe 2014 and 2015 IEEE International Conference on Communications. Sheserves as the Associate Editor-in-Chief for the IET Communications and as theAdvisory Editor for SpringerPlus.


Xuemin (Sherman) Shen (M’97–SM’02–F’09) re-ceived the B.Sc. degree from Dalian Maritime Uni-versity, Dalian, China, in 1982 and the M.Sc. andPh.D. degrees from Rutgers University, Camden, NJ,USA, in 1987 and 1990, respectively, all in electricalengineering.

From 2004 to 2008, he was the AssociateChair for Graduate Studies with the Department ofElectrical and Computer Engineering, University ofWaterloo, ON, Canada, where he is currently a Pro-fessor and a University Research Chair. He is a

coauthor or editor of six books and has published many papers and bookchapters on wireless communications and networks and control and filtering.His research interests include resource management in interconnected wire-less/wired networks, wireless network security, wireless body area networks,and vehicular ad hoc and sensor networks.

Dr. Shen served as the Technical Program Committee Chair for the 2010Fall IEEE Vehicular Technology Conference (IEEE VTC), the Symposia Chairfor the 2010 IEEE International Conference on Communications (IEEE ICC),the Tutorial Chair for the 2008 IEEE ICC and 2011 Spring IEEE VTC, theTechnical Program Committee Chair for the 2007 IEEE Global Communica-tions Conference, the General Cochair for the 2006 International Conferenceon Quality of Service in Heterogeneous Wired/Wireless Networks and the2007 International Conference on Communications and Networking in China,and the Chair for IEEE Communications Society Technical Committee onWireless Communications and Peer-to-Peer Communications and Networking.He also serves/served as the Editor-in-Chief for IEEE NETWORK, Peer-to-Peer Networking and Applications, and IET Communications; a FoundingArea Editor for IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS;an Associate Editor for IEEE TRANSACTIONS ON VEHICULAR TECHNOL-OGY, Computer Networks, and ACM Wireless Networks; and the Guest Editorfor IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, IEEEWIRELESS COMMUNICATIONS, IEEE COMMUNICATIONS MAGAZINE, andACM Mobile Networks and Applications. He is a Distinguished Lecturer ofIEEE Vehicular Technology and Communications Societies, a Fellow of theCanadian Academy of Engineering and the Engineering Institute of Canada,and a registered Professional Engineer in Ontario, Canada.

Jon W. Mark (M’62–SM’80–F’88–LF’03) receivedthe Ph.D. degree in electrical engineering fromMcMaster University, Hamilton, ON, Canada,in 1970.

Since September 1970, he has been with theDepartment of Electrical and Computer Engineering,University of Waterloo, Waterloo, ON, where he iscurrently a Distinguished Professor Emeritus. FromJuly 1984 to June 1990, he served as the DepartmentChair. In 1996, he established the Center for Wire-less Communications, University of Waterloo, and

is currently serving as its founding Director. He has been on sabbatical leaveat the following places: IBM Thomas J. Watson Research Center, YorktownHeights, NY, USA, as a Visiting Research Scientist (1976–1977); AT&T BellLaboratories, Murray Hill, NJ, USA, as a Resident Consultant (1982–1983);Laboratoire MASI, Universit Pierre et Marie Curie, Paris, France, as an InvitedProfessor (1990–1991); and Department of Electrical Engineering, NationalUniversity of Singapore, Singapore, as a Visiting Professor (1994–1995). Hehas previously worked in the areas of adaptive equalization, image and videocoding, spread spectrum communications, computer communication networks,asynchronous-transfer-mode switch design, and traffic management. His cur-rent research interests include broadband wireless communications, resourceand mobility management, and cross-domain interworking.

Dr. Mark served as a member of the IEEE Communications Society AwardsCommittee from 1995 to 1998. He served as an Editor for IEEE TRANSAC-TIONS ON COMMUNICATIONS from 1983 to 1990 and for Wireless Networksfrom 1993 to 2004, as a member of the Inter-Society Steering Committee ofthe IEEE/ACM TRANSACTIONS ON NETWORKING from 1992 to 2003 andas an Associate Editor for Telecommunication Systems from 1994 to 2004.He received the 2000 Canadian Award for Telecommunications Research andthe 2000 Award of Merit of the Education Foundation from the Federation ofChinese Canadian Professionals. He is a Fellow of the Canadian Academy ofEngineering.