infotainment enabled smart cars: a joint communication,...

8408 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 68, NO. 9, SEPTEMBER 2019

Infotainment Enabled Smart Cars: A JointCommunication, Caching, and

Computation ApproachS. M. Ahsan Kazmi , Tri Nguyen Dang, Ibrar Yaqoob, Senior Member, IEEE, Anselme Ndikumana ,

Ejaz Ahmed, Senior Member, IEEE, Rasheed Hussain , Senior Member, IEEE,and Choong Seon Hong , Senior Member, IEEE

Abstract—Remarkable prevalence of cloud computing has en-abled smart cars to provide infotainment services. However, re-trieving infotainment contents from long-distance data centersposes a significant delay, thus hindering to offer stringent latency-aware infotainment services. Multi-access edge computing is apromising option to meet strict latency requirements. However,it imposes severe resource constraints with respect to caching,and computation. Similarly, communication resources utilized tofetch the infotainment contents are scarce. In this paper, we jointlyconsider communication, caching, and computation (3C) to reduceinfotainment content retrieval delay for smart cars. We formulatethe problem as a mix-integer, nonlinear, and nonconvex optimiza-tion to minimize the latency. Furthermore, we relax the formulatedproblem from NP-hard to linear programming. Then, we proposea joint solution (3C) based on the alternative direction methodof multipliers technique, which operates in a distributed manner.We compare the proposed 3C solution with various approaches,namely, greedy, random, and centralized. Simulation results re-veal that the proposed solution reduces delay up to 9% and 28%compared to the greedy and random approaches, respectively.

Index Terms—Smart cars, caching, multi-access edge comput-ing, 5G network, vehicular networks.

Manuscript received January 3, 2019; revised April 29, 2019 and July 5, 2019;accepted July 16, 2019. Date of publication July 23, 2019; date of current versionSeptember 17, 2019. This work was supported in part by the Institute of Iunfor-mation and communications Technology Planning and Evaluation (IITP) underGrant 2019-0-01287, funded by the Korea Government (MSIT), in part by theEvolvable Deep Learning Model Generation Platform for Edge Computing, andin part by the MSIT, Korea, under the Grand Information Technology ResearchCenter support program under Grant IITP-2018-2015-0-00742 supervised by theIITP. The review of this paper was coordinated by the Guest Editors of the SpecialSection on Vehicle Connectivity and Automation Using 5G. (Correspondingauthor: Choong Seon Hong.)

S. M. A. Kazmi is with the Networks and Blockchain Lab, Institute ofInformation Security and Cyber Physical System, Innopolis University, In-nopolis 420500, Russia, and also with the Department of Computer Scienceand Engineering, Kyung Hee University, Seoul 17104, South Korea (e-mail:[email protected]).

T. N. Dang, I. Yaqoob, A. Ndikumana, and C. S. Hong are with theDepartment of Computer Science and Engineering, Kyung Hee University,Seoul 17104, South Korea (e-mail: [email protected]; [email protected];[email protected]; [email protected]).

E. Ahmed is with the Centre for Mobile Cloud Computing Research, Univer-sity of Malaya, Kuala Lumpur 50603, Malaysia, and also with the Department ofComputer Science and Engineering, Kyung Hee University, Seoul 17104, SouthKorea (e-mail: [email protected]).

R. Hussain is with Networks and Blockchain Lab, Institute of InformationSecurity and Cyber-Physical System, Innopolis University, Innopolis 420500,Russia (e-mail: [email protected]).

Digital Object Identifier 10.1109/TVT.2019.2930601

I. INTRODUCTION

SMART cars equipped with miniaturized technology haverevolutionized automobile industries. Tremendous ad-

vances have been made in the sensing, computation and com-munication technologies [1], [2] for establishing a network ofsmart cars. Such network aims to enhance the Quality of Service(QoS) in terms of comfort and convenient of the users, therebyrendering infotainment services. Infotainment enabled smartcars provide informational and entertainment services, resultingin enhancing the experience of drivers and passengers [3], [4].Typically, information services (e.g., road safety, current traf-fic condition, and weather status) enhances driving capability,whereas the entertainment services focus on making the passen-gers’ journey enjoyable (e.g., playing movies, games, and socialmedia) [5], [6]. However, guaranteeing infotainment servicesengenders indispensable latency related issues. Coping with theissues, stringent latency aware solutions can play a leading role.In this paper, we focus on the entertainment service as they havewidely been run using smart devices ubiquitously. However,reaping benefits of entertainment services for the smart car’spassengers requires meeting significant challenges pertaining tolimited resources and delay management.

One of the core challenges in enabling on board entertain-ment services is meeting the delay requirements posed by thepassengers of the smart cars. Typically, this service requiresfetching of the required contents from the content servers whichare placed at the data center or on the edge. These contents canbe requested in multiple formats, where each format represents aspecific quality depending upon users’ requirements. However,accessing contents from the cloud prolong their retrieval time[7]–[9]. To solve this issue, the Multi-access Edge Computing(MEC) technology introduced by the European Telecommuni-cations Standards Institute (ETSI) is considered as a promisingsolution to complement the performance of a central cloud [10],[11]. In our model, we consider that a Base Station (BS) isequipped by an MEC server in which contents can be cachedand computed. However, these MEC servers have limited com-putation and caching capability or resources [12], [13]. Cachingof popular contents at BSs help to reduce the backhaul loadand reduce end-to-end delay, whereas computation of contentscan be performed to convert an available content of a specific

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KAZMI et al.: INFOTAINMENT ENABLED SMART CARS: A JOINT COMMUNICATION, CACHING, AND COMPUTATION APPROACH 8409

format to the required format. Although equipping BSs withMECs significantly reduces the communication delay, they haveresource constraints.

Dedicated short range communications (DSRC)/IEEE802.11p and cellular networks are mostly used to enable con-nectivity in vehicular networks. Vehicular networks based onthe IEEE 802.11p standard are used to support in-motion con-nectivity, i.e., for urgent short range communications or se-curity related messages. However, ensuring flawless Internetconnectivity with IEEE 802.11p is a challenging issue due tothe high mobility of cars. Alternatively, employing Long-TermEvolution (LTE) and Fifth Generation (5G) based technologiesare considered better solutions for offering infotainment services[14]–[17]. Furthermore, the LTE provides a higher channelbandwidth compared to the DSRC which is more suitable forinfotainment services in vehicular networks. For these LTEmerits, the proposed solution is based on it.

A. Related Works

In recent years, in-vehicle infotainment systems have cap-tivated significant attention from automobile industries. Theinfotainment systems offer entertainment that leads to enhancingin-vehicle user experience in terms of comfort and convenience,as discussed in [18]–[20]. Simultaneously, caching of prevalentcontents near to the user network has been gaining significantinterest recently. However, its adaptation in smart cars is hin-dered by the mobility problem. To cope with this problem,a judiciously designed in-vehicle caching framework namedIV-Cache was proposed in [21]. In particular, there have beennumerous works that focus on enabling edge-based caching forsmart cars. For instance, the authors in [22], [23] proposeda framework of relay selection based on edge computing toperform data dissemination, that resulted in providing efficientinfotainment services with lower cost. Another study [24] pro-posed a novel scheme called RICH (RoadsIde CacHe), that aimsto ensure in-order delivery of contents through optimal cachingin a highly dynamic environment with granular information ofcar trajectories. Although the proposed scheme helps to improvethe cache throughput and reduces the backhaul traffic, it stillneeds to be optimized for the infotainment applications in smartcars.

The work in [25] introduced a popularity-aware contentcaching and retrieving scheme for highway vehicular networks.In [4], two markovian models were developed to analyze theperformance of IEEE 802.11p EDCA for vehicle to vehicle info-tainment applications. In Vehicular Content Networks (VCNs),BSs are used to keep the content replica on the edge of thenetwork to provide fast delivery of the specific content whenrequested. However, the limited size of BSs hinders to storea large number of content replicas, and thus caching in BSsbecomes a prevailing issue. To address such issue, the authorsof [26], [27] proposed edge-based dynamic content cachingschemes in BSs. The schemes help to cache the contents atthe edge of VCNs based on the requests of vehicles and thecooperation among BSs. Q. Yuan et al. [28] demonstratedthat bringing caching scheme at the wireless edge node and

enabling content sharing using vehicular networks can helpto improve the quality of automated driving services by re-ducing resource utilization. This study has utilized time seriesanalysis to predict the service content demands which leadsto caching the popular content at base stations (BSs). Further-more, the study proposed space division multiple access-basedand indexed coding enabled broadcasting with the objectiveof improving the efficiency of content sharing among smartcars.

To deal with the time-varying channels, adaptive bitrate(ABR) streaming has been deployed in vehicular networks.However, caching at the edge of the network to support ABRstreaming is one of the crucial issues. To cope with such issue,the work in [29] proposed a two-time scale dynamic cachemechanism for ABR streaming in vehicular networks.

In summary, none of the afore-mentioned solutions considercaching, computation, and communication (3C) jointly but indi-vidually to reduce delays with respect to infotainment services.Evidently, the joint consideration of 3C can unlock the full po-tential of infotainment services in smart cars. Thus, we proposea joint solution (3C).

B. Contributions

In this paper, we formulate the joint optimization problem forminimizing the delay with respect to communication, caching,and computational capacities. The designed problem turns out tobe an NP-hard problem due to the presence of integer constraints.The solution of the problem requires an exponential effort whichcannot be tolerated in vehicular networks due to high mobility.Therefore, we relax the designed problem and present a novelsolution based on the alternative direction method of multipliers(ADMM) technique [30] that can operate in a distributed fashion.Our key contributions can be summarized as follows:� We devise a joint 3C system model to reduce end-to-end

delay for infotainment services in smart cars.� We formulate an optimization problem for the joint 3C

model with the objective of minimizing delay and enhanc-ing the quality of experience of the smart car users. Theformulated problem is a mix-integer nonlinear, non-convexoptimization problem and is combinatorial in nature. Itis thus extremely difficult to obtain the globally optimalsolution for the formulated problem. Therefore, to obtain alow complexity solution, we use the relaxation technique tore-formulate the problem to a linear optimization problem.

� We propose a distributed algorithm to solve the relaxedproblem based on the ADMM technique. We introducean auxiliary variable to control the convergence rate ofthe proposed ADMM based algorithm and also prove itsconvergence.

� We compare our proposed 3C solution with various ap-proaches, namely, greedy, random, and centralized. In com-parison with greedy and random approaches, the proposedsolution reduces delay upto 9% and 28%, respectively.

These contributions are given in separate sections fromSection II to Section IV. We provide concluding remarks inSection V.


Fig. 1. Illustration of our system model.

II. SYSTEM MODEL AND PROBLEM FORMULATION

As shown in Fig. 1, we consider a service area consisting ofa data center and a set of BSs denoted by R � {0, 1, 2, ..., R},where the data center is indexed as 0 and BSs as j. We considerthat each BS j ∈ R has access to the data center via a wiredbackhaul of capacityωj,0. Moreover, each BSs j is also equippedwith a MEC server. In our model, we assume each MEC serverhas both caching and computational capabilities. We representthe cache and computational capacities of a MEC server by Cj

and Pj , respectively. A BS j ∈ R provides broadband service

to a set of Nj smart cars and let N � ∪|R|j=1Nj denote the set of

all smart cars in the service area.In our model, we consider that each smart car requests some

broadband service which requires to fetch contents from thecontent server available at the data center. Typically, an MECenabled BS downloads and pre-caches contents that have highprobabilities of being requested in a given region. We use M todenote the set ofM contents, where each content hasK differentformats based on the quality. We assume K = {1, 2, . . . ,K}1

represents different formats of a content. In our model, weassume that the smart car specifies the format of the requiredcontents and we use s(m,k) to denote the size of the content mwith format k in megabits.

The summary of our key notations is available in Table I.Next, we define our communication, caching and the compu-

tation models.

A. Communication Model

This subsection discusses our communication model. Com-munication is required in order to fetch the required contents

1For simulation results, we assume K = {1, 2, 3, 4} which represents fourdifferent formats of a content, i.e., 240p, 360p, 720p, 1040p. Then, a requestfor a content (m,k) with m = 1, k = 1 represents the mth content with 240pformat.

TABLE ISUMMARY OF THE KEY NOTATIONS

from the data center to the MEC enabled BSs and from the BSsto the smart cars.

First, we discuss the case in which contents are to be down-loaded from the data center to the BSs. In our model, we assumeif content is not cached in the BSs, it requires to be fetched fromthe data center to the BSs via a fiber backhaul link of capacityωj,0. Then, the transmission delay for downloading contents mwith format k from the data center to the BS j is expressed as:

τ(m,k)j,0 =

s(m,k)

ωj,0, (1)

Second, we discuss the case in which BS has already cachedthe requested content m with format k. In that case, the BSwill directly serve the requested content (m, k) to the smartcar i ∈ N via wireless channels. In our model, we considereach BS j has a wireless channel of bandwidth Bj which isshared using the time-division multiplexing approach betweenthe smart cars. Moreover, we assume that the channel conditionsare not changing during the allocation process. Additionally,the allocation process is completed during a single time frame,thus, channel gains for different time frames will be considereddifferent. Note that in this model, we do not consider radioresource allocation aspects such as shadowing, fast fading, andinterference management.2 Then, the data rate for a wireless linkbetween a smart car i ∈ N and any BS j ∈ R can be expressedas follows:

ωi,j = ζi,jBj log2

(1 + ϕj |Gi,j |2

), (2)

where ζi,j is the authorized bandwidth for the car i to the BS jand can be considered as the fraction of the BS owned bandwidthBj , Gi,j is the channel gain between BS j and smart car i, andϕj is the scalar factor that represents the transmission power ofBS j. Then, the transmission delay of fetching content (m, k)

2This assumption holds as no wireless channel allocation scheme is employedin this work which can be an interesting extension in the future.


from BS j to user i can be calculated using the following:

τ(m,k)i,j =

s(m,k)

ωi,j. (3)

Next, we also make another consideration pertaining to thetime required ti,j for a smart car i to exit the coverage radius γjof a BS j as follows:

ti,j =2γjμi

, (4)

where μi represents the traveling speed of a smart car i. Notethat (4) can be used to serve as a threshold in which a smart car ineeds to be served by a BS j. Furthermore, when τ

(m,k)i,j ≤ ti,j ,

the smart car i can start downloading the required file. However,when τ (m,k)

i,j > ti,j , then a smart car can select the next BS to usefor successfully receiving the content. Note that at a given time,the content is downloaded from only one BS. To discover the BSsavailable in its route, the BSs selection approach described in[6], [31] can be utilized. To enable the aforementioned approach,we assume that a cellular network is available for smart cars, andAccess Network Discovery and Selection Function (ANDSF) isimplemented at the core of the cellular network. To get the BSsavailable in the route of a smart car, the smart car requires to haveGlobal Positioning System (GPS) and needs to send its speed,location, and direction to the ANDSF server using the cellularnetwork. Then, the ANDSF server sends back the coordinatesof BSs available in the route of the smart car. The smart car usesits GPS to calculate the distance to reach each BS and selectsthe next BS.

B. Caching Model

Next, we discuss the caching model. The aim of cachingcontents at the BSs is to reduce the access delay and save thelimited backhaul bandwidth.

In our model, we assume that each MEC enabled BS j hascache storage of Cj which is typically very limited compared tothe number of contents. Therefore, each BS j only cache contentsthat have a high probability of being requested in its coveragearea. Lets assume that pmj and qkj represent the probabilitiesof a requested content m and format k at a BS j, respectively.Furthermore, assume that the probability distribution of p, andq are independent. Then, the probability of a request (m, k) atBS j is represented by p

(m,k)j = pmj qkj . Note that each BS j has

to follow the cache allocation constraint which can be expressedas follows:

M∑

m=1

K∑

k=1

x(m,k)j s(m,k) ≤ Cj , (5)

where x(m,k)j represent the caching decision indicator variable

which identifies if a BS j will cache content (m, k), defined asfollows:

x(m,k)j =

{1, if BS j caches content (m, k),

0, otherwise.(6)

Furthermore, we also define a cache hit indicator at the BS jfor indicating if a content (m, k) requested by smart car i ∈ Nis cached at the BS j:

h(m,k)i,j =

⎧⎪⎨

⎪⎩

1, if the content (m, k) requested by smart car

i is cached at BS j,

0, otherwise.(7)

As each BS j ∈ R has limited cache capacity Cj . Therefore,with respect to cache allocation constraint in (5), each BSj ∈ R pre-caches one format of contents. Hereafter, unless stateotherwise, we use (m, k′) to denote a cached format k′ ∈ K ofany content m and (m, k) to denote any format k ∈ K differentthan the cached format k′ of content m. Based on the demandfrom smart car for content (m, k′) that reaches BS j, (m, k′)can be retrieved from the cache storage of the BS. However,if the smart car requests content (m, k), where k �= k′, is notavailable in cache storage of a BS j, i.e., x

(m,k)j �= 1, two

possible options can be adopted to retrieve the content (m, k).One option is to compute the content from the cached format(m, k′) to the required format (m, k) while the second optionis to download the content (m, k) from the DC. In the nextsubsection, we discuss about the computation model to convertthe required content format from the cached content format, i.e.,(m, k′) → (m, k).

C. Computation Model for Cached Content

In this subsection, we discuss the computation model for thecached contents, where MEC server are used to satisfy smartcar demands related to contents delivery. As described in [32],we consider that the quality of cached contents can be adjustedbased on smart car capabilities in terms of display and networkconnection. In our model, we assume that smart cars can requestcontents of different quality or format. Thus, it is quite possiblethat a BS might have not cached a content m with the requestedformat k (k �= k′) due to limited cache capacity. However, aBS might have the content m with a different format thanthe requested format, i.e., (m, k′). Then, a BS can decide toeither compute the requested content (m, k) from the cachedformat (m, k′) or download the content (m, k) from the DC.This decision is performed based on smart car requirements andthe local state of the load at the BS. There are a number oftechniques for content quality-improvement that a MEC enabledBS can adopt to compute the cached contents, but here we brieflydiscuss the most utilized techniques including but not limited toTranscoding, Super-Resolution (SR), and Low-Resolution (LR)techniques.� Transcoding: This approach can be used to trans-

form/transcod a low quality content (e.g., video of 240pat a bit-rate of 2 Mbps) to high quality content (e.g., videoof 1040p at a bit-rate of 10 Mbps) using computationalresources of a MEC server. This transcoding technique isdescribed in [6], [32], [33]. This approach can be used whensmart cars request contents of higher quality that are notcached in BSs. In addition, smart cars may demand contentwith a certain format such as H.264 that is not cached at


BSs. However, the BSs may have the same content in theircache storage with a different format such as MPEG-2.Therefore, rather than forwarding the requests of the smartcar to remote data centers, the BSs can transcord thecached content to the desired format (MPEG-2 to H.264),and return requested content (H.264) to smart cars. Toperform transcoding, it is assumed that each MEC serverhas transcoder system such as HEVC-SHVC transcoder[33] installed at the BS. When a BS receives the demandfor content (m, k) from the smart car, it checks its cachestorage whether the format k′ of content m is cached ornot. In case the content (m, k′) exists in the cache storage,the MEC server retrieves the content (m, k′) and puts itinto a transcoder system as an input. Then, the transcodertranscods content (m, k′) to content (m, k), where content(m, k) is the output of transcoding. Finally, the MEC servertransmits the requested content (m, k) to the smart car.Furthermore, the transcoding task has a computationalcost, where the cost of transcoding can be regarded ascomputational delay and computation resources utilization[34]. However, if content (m, k′) is not available in thecache storage, the BS forwards the demand for content(m, k) to the DC.

� Super-Resolution: This technique is used for video andimage files, and it consists of estimating high resolutionfile from a lower resolution file. Here, assuming that thelower resolution of content is cached at BS, where MECserver can perform digital processing to convert the cachedlower resolution of content format into a required higherresolution of content format. The conversion can be per-formed using artificial intelligence approaches such as deeplearning [35], [36]. To estimate a high resolution content(m, k) from a lower resolution cached content (m, k′), amulti layer Convolutional Neural Networks (CNN) modelas described in [35], [36] can be used. This approachworks as follows: a content (m, k′) can be used as aninput for the first Convolutional layer which extracts thefeatures of the content (m, k′) and represent them in termsof high-dimension vector of lower resolution overlappingpatches. Then, the second Convolutional layer appliesnon-linear mapping to convert the high-dimension vec-tor of lower resolution patches to another high dimen-sional vector of high-resolution patches. Finally, the lastlayer aggregates/combines the high dimensional vector ofhigh-resolution patches within the spatial neighborhoodand generates the high-resolution required content format(m, k). Furthermore, we consider the smart cars are sensi-tive to the delays and to adopt this approach, training theCNN model and test the model would be infeasible in termsof delay. To overcome this challenge, we assume that eachBS already has the CNN model, which is already trainedand tested offline for super-resolution purpose. In otherwords, for estimating high-resolution content (m, k) froma lower resolution cached content (m, k′), the BS does notneed to make CNN model, train it, and test it. Here, theBS just uses already trained and tested CNN model. Forthis reason, we don’t consider the computation complexity

of deep learning (CNN model) in our computation model.However, thus using the trained and tested CNN modelrequire CPU utilization, we consider computational delayand CPU computation resources utilization as a cost forperforming quality improvement.

� Low-Resolution: It is considered as an inverse techniqueof above-described Super-Resolution for video and imagefiles. Based on the display capability of smart cars, it mayhappen that they request lower resolution contents (m, k)of the high resolution cached content (m, k′) at BSs. Here,assuming that the BS has a high resolution content (m, k′)in its cache memory. In such a scenario, rather than for-warding request for (m, k) to remote cloud, BS can use itsMEC server to perform the conversion (m, k′) → (m, k)using trained and tested CNN model.

In this work, we use cache, process, and transmit approach.This approach only transmits the computed content withoutcaching it if it is not popular, thereby preventing redundancyand ensuring an efficient cache utilization.

Assume converting a cached content (m, k′) to content (m, k)

requires the computation resources h(m,k)j of MEC enabled BSs

j, where computational resource allocation h(m,k)j for a BS j is

defined as :

h(m,k)j = f(s(m,k),Al), ∀j ∈ R. (8)

where Al denoted the quality improvement approach, e.g, a1 isthe transcoding approach. However, in this work, we assume alinear model of computation for simplicity. Then, we can defineh(m,k)j for a BS j as [37]

h(m,k)j = �

(m,k)j s(m,k), ∀j ∈ R. (9)

where �(m,k)j represents the computation workload in terms

of CPU cycles per bit required for converting cached content(m, k′) to (m, k), y(m,k)

j represents the computation decisionvariable, which is expressed as:

y(m,k)j =

⎧⎪⎨

⎪⎩

1, if content (m, k′) is converted to

format (m, k) at BS j,

0, otherwise,

(10)

In addition, the computation resource at a BS is limited, thus,computation allocation has to satisfy the following constraint:

K∑

k=1

M∑

m=1

h(m,k)j y

(m,k)j ≤ Pj , ∀j ∈ R. (11)

Then, the computational delay to convert cached content(m, k′) into desired content (m, k) at a BS j is given by:

l(m,k)j =

(s(m,k))2

h(m,k)j

(12)

However, when the MEC server does not have enough compu-tation resources to satisfy the above constraint (11), it forwardsthe request to the data center.


D. Problem Formulation

In this section, we formulate a novel problem aiming tominimize the total delay for the smart cars in the service area.The total delay experienced by a smart car i ∈ N for retrievingcontent (m, k) from a BS j ∈ R is given by σ

(m,k)i,j :

σ(m,k)i,j = x

(m,k)j τ

(m,k)i,j +

(1 − x

(m,k)j

)

×[y(m,k)j

(τ(m,k)i,j + lm,k

j

)

+(

1 − ym,kj

)(τ(m,k)i,j + τ

(m,k)j,0

)]. (13)

Then, the total delay experienced by smart car i for retrievingall its contents from a BS j can be given by:

φi,j =M∑

m=1

K∑

k=1

p(m,k)j σ

(m,k)i,j (14)

Therefore, for minimizing delay φi,j , the optimization prob-lem can be expressed as follows:

minx,y

N∑

i=1

R∑

j=1

φi,j (15a)

subject to:

σ(m,k)i,j ≤ ti,j , ∀i ∈ N , ∀j ∈ R, (15b)

M∑

m=1

K∑

k=1

s(m,k)x(m,k)j ≤ Cj , ∀j ∈ R, (15c)

M∑

m=1

K∑

k=1

y(m,k)j h(m,k) ≤ Pj , ∀j ∈ R, (15d)

x(m,k)j = {0, 1}, ∀j ∈ R, ∀m ∈ M, ∀k ∈ K, (15e)

y(m,k)j = {0, 1}, ∀m ∈ M, ∀k ∈ K. (15f)

The first constraint in (15b) ensures that the delay thresholddeadline is not violated, i.e., the smart car is only served by aBS during its stay in the coverage of a BS (ti,j). The constraintsin (15c) and (15d) guarantee that caching and computationalresources are not violated, i.e., the smart car is only served by aBS if it has the required resources to fulfill the request. Moreover,the total size of all formats cached at a BS should be less thanthe cache capacity. Similarly, the total computations performedat a BS should be less than the computation capacity of a BS.Finally, the binary decision variables are given by constraints(15e) and (15f).

The formulated optimization problem in (15a) is a large-scalemulti-dimensional Knapsack problem which falls in the NP-hardcategory and such problems require exponential time to obtaina solution in a centralized manner. It imposes a complexityof O(2M×R×N×K) which makes it intractable for a practicalsystem with a large number of smart cars, BSs, and contentswith different formats. On the other hand, a distributed solutionfor such problems is highly desirable as they enable parallelcomputing and problem decomposability. Therefore, to tacklethe above problem in (15a), first, we relax both integer variables

into continuous variable x ∈ [0, 1], y ∈ [0, 1] to form a linerprogram. Then, we derive a distributed solution based on theADMM technique which will be explained in Section III.

III. SOLUTION APPROACH: DISTRIBUTED

ADMM-BASED APPROACH

In this section, first, we present a relaxed problem and itsinterpretation in terms of our model. Then, we discuss thesolution approach.

As stated in the previous section, the formulated problemin (16) is a non-convex and intractable problem. Therefore,we relax the binary variables of cache and compute decisionsfrom integers to continuous variables. This can be interpreted asfractional storage of contents at the cache storage and fractionalprocessing of contents at the processing unit. In such a case, arequested content can be fetched as a fraction from the cachestorage, a fraction can be processed at the BS and a fraction canbe fetched from the data center. Then, we reformulate the delayperceived by a smart car i ∈ N as follow:

σ(m,k)i,j = x

(m,k)j τ

(m,k)i,j + y

(m,k)j

(τ(m,k)i,j + l(m,k)

)

+ z(m,k)j

(τ(m,k)i,j + τ

(m,k)j,0

), (16)

where x represent the cache decision, y denotes the processingdecision, and z denotes the download decision from the datacenter. Note that lower cache capacity at BSs causes communi-cation cost to increase in terms of backhaul bandwidth utilizationand end-to-end delay. On the other hand, increasing cache ca-pacity at BSs causes high cache hits and higher utilization ofthe computing capacity, where based on demands, the qualityof cached contents can be improved by using computationresources. Therefore, to deal with this 3C tradeoff under thelatency constraint, we formulate an optimization problem in (16)that jointly addresses communication, caching, and computationas follows:

minx,y,z

N∑

i=1

R∑

j=1

φi,j (17a)

subject to:

σ(m,k)i,j ≤ ti,j , ∀i ∈ N, ∀j ∈ R (17b)

M∑

m=1

K∑

k=1

s(m,k)x(m,k)j ≤ Cj , ∀j ∈ R (17c)

M∑

m=1

K∑

k=1

y(m,k)j h(m,k) ≤ Pj , ∀j ∈ R (17d)

x(m,k)j + y

(m,k)j + z

(m,k)j = 1, ∀j ∈ R,∀m ∈ M, ∀k ∈ K

(17e)

0 ≤ x(m,k) ≤ 1, ∀m ∈ M, ∀k ∈ K, (17f)

0 ≤ y(m,k) ≤ 1, ∀m ∈ M, ∀k ∈ K, (17g)

0 ≤ z(m,k) ≤ 1, ∀m ∈ M, ∀k ∈ K. (17h)


In the relaxed problem stated in (17a), we have added anadditional constraint (17e) which reflects that the sum of frac-tions make up the complete content in terms of its size anddo not exceed the total content’s size, i.e., fraction of contentwill be fetched from cache space, a fraction after computationand a fraction from the data center. Moreover, the integer con-straints are relaxed as shown by constraints (17f), (17g) and(17h). Based on the definition of convex function, our objectivegiven in (17a) is a convex function. Constrains {17b . . . 17h}is either linear or convex set. Thus, our relaxed problem is aconvex problem and is solvable in polynomial time by usingLinear Programming(LP) method. However, finding a solutionrequires much more information and a central system to solve theproblem which increases the complexity. Therefore, we proposea novel efficient distributed solution that can be solved at eachagent and requires only local information to obtain a solution.Furthermore, it has been stated in [38], [30] that a unique optimalsolution always exists for 17a as it falls under the category ofconvex optimization problem.

A. Distributed Algorithm

In this section, we propose a distributed algorithm based onthe ADMM method. We assume that once a BS j receives arequest for (m, k), it will inform the status of x

(m,k)j to the

smart car i. Then, the smart car can decide to make a decisiony(m,k)j based on local informations such as its threshold ti,j and

computational resources h(m,k)j . This decision y

(m,k)j is then

send to the BS j.Lets assume a vector ξi represented as follows:

ξi = [τ(m,k)i , τ

(m,k)i + l

(m,k)j , τ

(m,k)i + τ

(m,k)0 ]T , (18)

and similarly another vector x(m,k)j given as follows:

x(m,k)j = [x

(m,k)j , y

(m,k)j , z

(m,k)j ]T . (19)

Then, the delay at a smart car i for content (m, k) from a BSj can be rewritten as follows:

fi

(xm,kj

)= ξTi x

(m,k)j (20)

Similarly, the total delay of user i at BS j can be also re writtenas follows:

fj(xj) �N∑

i=1

M∑

m=1

K∑

k=1

fi

(x(m,k)j

)(21)

Note that the problem stated in (17a) can decomposed intosubproblems and each one can be implemented at a BS. Letsdefine the feasible set of x as follows:

X = {x|(17b), (17c), (17d), (17e), (17f), (17g), (17h)}(22)

Furthermore, we introduce an auxiliary variable to use the lo-cal information. In constraint (16d), the computational resourceis managed by the BS. As stated, a smart car is only aware of thecurrent available capacity of a BS. Based on this information,each smart car aims to minimize its delay by stating its demand

for computation. This demand is then sent to the BS by eachsmart car. On receiving all demands, a BS uses the ζ variableto balance among the demands posed by the smart cars and itscurrent computation capacity which is given as follows:

g(ζ) = IX (ζ) =

{0, ζ ∈ X∞, otherwise

(23)

Then, our problem stated in (17a) is equivalent to the follow-ing problem:

minx

R∑

j=1

fj(xj) + g(ζ) (24a)

subject to:

xj = ζ, ∀j = 1, . . . , R. (24b)

Our goal is to solve the problem stated in (24a) by applying theADMM method. Therefore, we take the augmented Lagrangianof the problem stated in (24a) as follows:

Lρ(x, λ) =

N∑

i=1

R∑

j=1

fj(xj) + λ(x− ζ) +ρ

2||x− ζ||22, (25)

where λ represent the Lagrange multiplier associated to theconstraint (24b) and ρ is a constant.

Then, following the ADMM technique x, ζ and λ are sequen-tially updated. The results are given as follows:

xt+1j =argmin

(fj(xj)+λtT

j (x− ζt)+ρ

2||xj−ζt||22

)(26)

ζt+1=argmin

⎛

⎝g(ζ)+R∑

j=1

(λtTj ζ+

ρ

2||xt+1

j −ζt||22)⎞

⎠ (27)

λt+1j = λt+1

j + ρ(xt+1j − ζt+1) (28)

Note that the primal variable x can be updated in a parallelfashion at the smart car side, while the remaining two variablesζ, and λ are updated at the BS side. The x−update is obtainedby solving the following:

minxj

(fj(xj) + λtT

j (xj − ζt) +ρ

2||xj − ζt||22

)(29a)

subject to:

x(m,k)j + y

(m,k)j + z

(m,k)j ≤ 1, ∀m ∈ M, ∀k ∈ K (29b)

xj ∈ X (29c)

The solution to obtain the x−update is a convex problem asthe objective function is convex, whereas the constraints areeither linear, affine or closed convex set. Therefore, the optimalsolution always exists. As the x−update is a convex problem,we use the solver in Julia language namely Convex.jl [39] to findthe optimal solution.

Next, we define the stopping criteria for the proposed ADMMbased algorithm from the reformulated Linear Programmingproblem.

Algorithm 1 presents the pseudo-code of the proposed dis-tributed algorithm. The algorithm starts by initializing the


Algorithm 1: Distributed Algorithm (ADMM-Based) ForInfotainment Enabled Smart Cars.

1: Input: R, N , M, C, P ;2: Output: Minimize the total delay;3: Initialization:

max_iteration = 10000, ρ = 0.5, α = 0.5,abstol = 10−4, reltol = 10−2, t = 1,x0j ≥ 0, λ0

j ≥ 0, ζ ≥ 0, ∀j ∈ R;4: Update the communication capacity following (2);5: for t ≤ max_iteration do6: for j ∈ R do7: xt

j =

argmin(fj(xj) + λtT

j (x− ζt) + ρ2 ||xj − ζt||22

);

8: ζt =

argmin

(

g(ζ) +R∑

j=1

(λtTj ζ + ρ

2 ||xt+1j − ζt||22

))

;

9: λtj = λt+1

j + ρ(xt+1j − ζt+1), ∀j ∈ R;

10: obj[t] = (17a)(xt, ζt, λt);11: rnorm[t] = ||x− ζ||, snorm[t] =

|| − ρ× (ζt − ζt−1)||;12: if rnorm[t] ≤ εpri(abstol, reltol) ∩ snorm[t] ≤

εdual(abstol, reltol) then13: break;14: Return total delay.

ADMM global constants, variables, and parameters as seen inline 3. The input of the algorithm includes the set of BSs R,smart cars N , content catalog M, computational P and cachingcapacities C of all BSs (line 1). Then, following the ADMMalgorithm for Linear Programming(LP), an iterative ADMMprocess is started that updates its variables sequentially x, ζand λ. Initially, the BSs will inform the smart cars regarding theinformation about Cache capacity (Cj), Computation capacity(Pj), and CPU cycles per unit of data (�j). Then, all vehicleswill calculate the x variable via (28), simultaneously. Thisresult is then sent to the BSs by the smart cars, in responseto which the BS updates ζ, and λ variables following the (26)and (27), respectively. Note that additional signaling is involvedin sending these values from the BS to the smart cars andvice versa. The x, ζ, and λ variables can be represented byfour bytes each. After this, the BS will broadcast these twovariables to all cars, then, the cars update the x variable again ina parallel manner. This update stops once the ADMM conver-gence criteria rnorm[t] ≤ εpri(abstol, reltol) ∩ snorm[t] ≤εdual(abstol, reltol) is satisfied (lines 12–14), where the primalrnorm[t] and the dual residuals snorm[t] become less thanor equal to the εpri(abstol, reltol) and εdual(abstol, reltol)variables respectively.

The iteration complexity of the ADMM based approach de-pends upon the the ε parameter as is represented as O(1/ε) [40].Moreover, the required complexity at each iteration can be givenby observing the updates in each iteration. Thex-update requiresO(M ×K), where M and K represents the contents and itsformat, respectively. As there exist no close-form solution for

computing the x-update, we use the convex solver to find asolution. Therefore, the complexity of x-update iteration de-pends upon the solver and the used platform. Next, the ζ-updaterequires O(R) as its a projection function, thus, it has a linearcomplexity. Lastly, the λ-update, we have a constant complexitygiven as O(1). Note that as the number of BSs R are veryless compared to the contents M , we can ignore it. Thus, theworst case complexity for a single iteration is O(M ×K).Let the maximum number of iteration to achieve an ε-optimalsolution be represented by T , then, the total execution time ofalgorithm is T ×O(M ×K). This means that our approach canconverge within a defined time frame by setting the parametersof ε,K, andM .

IV. SIMULATION RESULTS AND ANALYSIS

In this section, we present the numerical analysis to evaluatethe performance of our joint 3C approach. We use the Julialanguage 0.6.2, Convex.jl, ECOS.jl [39] as our simulation tool toevaluate the performance of our proposal. We numerically eval-uate the results on a single computer due to the limit of physicaldevices with the following specifications: Intel(R) Core(TM)i5-4690 CPU 3.50 (GHz), RAM 28.0 (GB), GPU GTX 10603 (GB).

A. Simulation Setup

In our simulation setup, we consider a set of BSs |R| = 10, setof smart cars |N | that range from |N | = {5 ∼ 50} smart cars, acontent catalog of |M| = 50 contents,3 where the content sizefor each format is chosen as {75.0, 187.5, 375.0, 600.0}(MB)representing k = {1, 2, 3, 4}, respectively. Moreover, we as-sumed that each BS j has a cache capacity ranging fromCj = {10∼40}(GB) reflecting 10% → 80% of content cata-log, and a computational capacity ranging from Pj = {300 ∼1000}(cycles). In addition, for downloading contents, each BSj ∈ R has to be connected to the data center with a wiredbackhaul of capacity equal to ωj,0 = 50(Mbps), while eachBS j ∈ R has a wireless bandwidth of ωi,j = {1∼5}(Mbps).Furthermore, in our simulations, we set the control parametersfor the proposed algorithm as max_iteration = 1000, ρ =0.5, α = 0.5, abstol = 10−4, and reltol = 10−2. Where α isthe coefficient required to setup the Zipf distribution. In a servicearea, we consider that the speed of each smart car i ∈ N varies inthe range ofμi = {10∼18}(m/s). Therefore, with the departuretime, speed, and BSs location, our algorithm determines eachsmart car i ∈ N that is connected to a BS j ∈ R and the timeti,j required to exit the coverage radius γj of the BS j. Moreover,

the transmission delay τ(m,k)i,j for retrieving content s(m,k) from

BS j is also determined based on these information. Note that, allstatistical results are averaged over a large number of simulationruns of random locations of smart cars, velocity, and channelgains.

3The methodologies developed in this paper can also be applied to any valueof contents. The motivation for our choice (i.e., 50 contents) is for the sake ofsimulation simplicity.


B. Performance Metrics

Delay: In infotainment enabled smart cars, a demand forcontent s(m,k) arrives at a BS j which results in either ofthe following two cases. In the first case, the content s(m,k)

may be available in BS’s cache storage Cj through which thedemand can be fulfilled. However, in case of specific contentformat s(m,k) unavailability in the BS’s cache, it needs to beconverted to the requested format k from the cached format andsend to the smart car. Thus, the delay should capture both theaforementioned cases. Therefore, we consider the total delayas the time period between sending the demand for contents(m,k) and receiving the content from the BS j. Furthermore, thetransmission delay is considered as the difference between totaldelay and computation delay. In addition, we use backhaul delayto visualize transmission delay between BSs and data center viawired backhaul links. This delay is required in the case if a BSdoes not have enough resources to fulfill the requested demand,then, it forwards the request to the data center which sends therequired contents via the backhaul links. Here, we measure thedelay in terms of seconds.

Cache Hit Ratio: In infotainment enabled smart cars, we alsoevaluate the number of cache hits and misses for the requestedcontent. A cache hit h(m,k)

i,j ∈ {0, 1} occurs when the requested

content s(m,k) is fetched from BS j cache storage Cj with orwithout computation. Cache hit contributes to minimizing delaybecause it reduces backhaul traffic volume exchange betweensmart cars and data center. On the other hand, a cache miss 1 −h(m,k)i,j occurs when the requested content s(m,k) is not available

in the cache storage Cj . Cache miss at BS increases backhaultraffic and also result in the increase of backhaul delay.

C. Performance Benchmarks

To evaluate our proposed ADMM-based scheme, we compareAlgorithm 1 which operates in a distributed fashion with threeschemes, namely, the ‘Greedy’, ‘Random’, and ‘Centralized’schemes. We have summarized the features of these schemes asfollows:

Greedy Algorithm: This scheme is also called the “greedyheuristic algorithm”. In this algorithm, we can achieve a lo-cally optimal solution that approximates to the globally optimalsolution at each iteration. However, the greedy algorithm cannot guarantee a global optimal solution. The complexity of thisapproach is similar to the breadth-first search (BFS) that has acomplexity of O(N 2) [41].

Random Algorithm: This algorithm is characterized by thedegree of randomness using uniform random distribution as theinputs with the intent to achieve high performance in terms ofaverage values over all possible choices of inputs.

Centralized Algorithm: This algorithm requires a coordinatorand require complete information as inputs for solving the for-mulated problem in a centralized manner and has a complexityof O(N log(N )) [42]. Note that the centralized algorithm isdesigned to solve the relaxed problem stated in (17a).

All aforementioned comparing algorithms run on acentralized server, and it is assumed that the centralized server

Fig. 2. Total delay with 10 BS vs. number of smart cars (users).

is located at the data center. On the other hand, the proposedADMM-based algorithm runs on each BS j ∈ R. Moreover, thecomplexity of the proposed approach is similar to the ADMMapproach which is O(1/ε) [43], where ε–optimal solution andN = R×M ×K.

D. Simulation Results

In this subsection, we present our simulation results. In Fig. 2,we compare the total average delay versus the number of smartscars or users in the service area. For this simulation, we use|R| = 10 BSs and increase the number of smart cars from 5 to50 cars in the service area. Note that unless otherwise stated,we use users and smart cars interchangeably. It can be inferredfrom the simulation results that the total delay increases as thenumber of users in the area increases. In other words, the increasein the number of smart cars contributes to growing communica-tion, caching, and computational resources utilization. However,when the number of smart cars |N | = 50, we reach maximumcommunication, caching, and computational resources utiliza-tion. Moreover, it can also be seen that the results achieved fromthe centralized and the proposed ADMM-based (Algorithm 1)schemes are in-differentiable and are similar. Furthermore, theexperienced delay by using the proposed scheme is lower thanthe other two benchmarks, i.e., Greedy and Random algorithms.A performance gain in terms of lower total delay of up to 9%and 28% is observed when compared to the Greedy and Randomalgorithms, respectively. A similar trend can also be seen for thecomputation delay with an increase in the number of contentsas shown in Fig. 3.

In the next simulation, our goal is to observe the backhaulload with the increase in the number of contents with fixed cachecapacity, and computational capacity. As we assume that eachBS j has limited cache storage and computation capacity. If aBS cannot satisfy the demands posed by the smart cars, the BSforwards unsatisfied demands to the data center which results inbackhaul resource utilization. Thus, our objective of BS cachingis to minimize total delay in which backhaul delay is included.Therefore, Fig. 4 shows the backhaul delay with an increasingnumber of contents. It can be seen that when the number of


Fig. 3. Computation delay with 10 BS vs. number of contents.

Fig. 4. Total bachkhaul delay vs. number of contents.

contents in the service area is less than 35, the BSs satisfiesthe demands for all infotainment contents, thus, no traffic onthe backhaul and the backhaul delay is zero. However, when thenumber of contents |M| becomes greater than 35 contents, somedemands that are not satisfied by BSs are forwarded to the datacenter which results in backhaul delay and total delay as well.

Next, our goal is to analyze the convergence of the algorithm.Fig. 5 shows the convergence of objective function. In this figure,we can see that at 10th iteration, our objective function violatesthe resources constraints. Thus, to overcome this issue, ouralgorithm continues to run until we reach the convergence pointor stationary point which is considered as the optimal solutionthat does not violate the resources constraints. Moreover, for theproposed ADMM-based algorithm (Algorithm 1), the solutionshould be a vector that contains an optimal value, primal residualnorm, dual residual norm, and the tolerance threshold for bothnorms. Therefore, in Fig. 6 we present the dual residual normand εdual(abstol, reltol). We run the proposed algorithm untilsnorm[t] ≤ εdual(abstol, reltol) which is one of the convergecriteria, i.e. when the optimality gap is negligible. Further-more, in Fig. 7 we also present the primal residual norm andεpri(abstol, reltol). In this figure, the proposed algorithm runs

Fig. 5. The convergence of the objective function.

Fig. 6. Dual residual norm.

Fig. 7. Primal residual norm.

until rnorm[t] ≤ εpri(abstol, reltol). By visualizing the resultsof both figures (Figs. 6 and 7), we confirm that our objectivefunction converges to a minimum point which is a stationarypoint that satisfy the problem constraints. The convergence value


Fig. 8. Total delay with 10 BS vs. cache size.

Fig. 9. Computation delay with 10 BS vs. computational capcity.

is global optimal solution which have been shown in [38], [44],[45].

In the next simulation, we present the average total delay byvarying the cache capacity in Fig. 8. To evaluate how the cachesize will affect the total delay, we vary the cache size from, 10%to 80% of the content catalog size which will is equivalent to 5 −40(GB). We observe that the proposed approach outperforms allother approaches and achieves an indistinguishable performancewhen compared to the centralized approach. Moreover, we caninfer that the average total delay decreases proportionally withthe cache size. Similarly, in Fig. 9, we observe the same patternin the reduction of computational delay once the computationalcapacity at BSs is increased.

To reduce backhaul delay and bandwidth consumption, wecache infotainment contents at BSs. Fig. 10 shows the nor-malized cache hits at BSs, where the cache hits increase withZipf parameter (α). Here, we vary α parameter from 0.4 to1.0 to evaluate the cache hit. In other words, the demands forinfotainment content follow the Zipf distribution and once αbecomes large, more contents become popular which resultsin the increase of cache hits and subsequently reduction in thebackhaul traffic.

Finally, we compare the performance of the proposed ap-proach with the optimal solution as shown in Fig. 11. As theoriginal optimization problem is a combinatorial problem, wehave calculated the optimal solution using the exhaustive search

Fig. 10. Cache hit rate for different α parameters.

Fig. 11. Optimal Solution vs. ADMM based proposal.

approach. The exhaustive search method can only be appliedin a centralized manner in which all network information isassumed to be known and available at the centralized controllerwhich might be impractical for large scale systems. However,we cannot apply the exhaustive search method for a largescale network due to the combinatorial nature of the problem.Therefore, we have taken a small scale network to compareour proposed approach with the optimal solution. The networksettings for this simulation include a maximum of 40 smartcars, 10 infotainment contents with 4 formats, i.e., 40 contentsand 5 BSs with caching and computation capabilities of 10 GBand 1000 cycles, respectively. We observe that the proposedapproach increases the total delay by only 6% compared to theoptimal solution for a network size of 40 smart cars. Hence,we can state that the proposed approach has a reasonably goodperformance compared to the optimal solution in terms of delay.

V. CONCLUSION

In this paper, we proposed the joint communication, caching,and computation (3C) model for infotainment enabled smartcars. We formulated our proposal as an optimization problemthat minimizes delay for accessing infotainment services sub-ject to the communication, computation, and caching resourcesconstraints. Since, the formulated problem was mix-integer,non-linear, and non-convex optimization problem, we converted


the formulated problem into a linear programming problem andproposed a novel iterative algorithm, which is based on theADMM approach. Through extensive numerical analysis, theresults show that the solution of the proposed linear program-ming problem converges to the global optimal solution of therelaxed problem. In addition, our proposal reduces the delaysexperienced by smart cars in getting entertainment services,achieves fast convergence, and increases the cache hit ratio.In the future, we aim to extend our work by considering keywireless channel aspects such as shadowing, fast fading, and in-terference management for offering more adequate infotainmentservices in smart cars.

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https://dx.doi.org/10.1109/TMC.2019.2908403


S. M. Ahsan Kazmi received the master’s de-gree in communication system engineering from theNational University of Sciences and Technology,Islamabad, Pakistan, in 2012, and the Ph.D. degree incomputer science and engineering from Kyung HeeUniversity (KHU), Seoul, South Korea. He is with theInstitute of Information Security and Cyber PhysicalSystem, Innopolis University, Innopolis, Tatarstan,Russia, where he is currently an Assistant Profes-sor. His research interests include applying analyticaltechniques of optimization and game theory to radio

resource management for future cellular networks. He received the Best KHUThesis Award in engineering in 2017 and several best paper awards fromprestigious conferences.

Tri Nguyen Dang received the B.S. degree in infor-mation teaching from the Hue University’s Collegeof Education, Hue, Vietnam, in 2014. He is currentlyworking toward the Ph.D. degree in computer scienceand engineering with Kyung Hee University, SouthKorea. His research interests include network opti-mization and mobile cloud computing.

Ibrar Yaqoob (S’16–M’18–SM’19) received thePh.D. degree in computer science from the Universityof Malaya, Kuala Lumpur, Malaysia, in 2017. Hehas been listed among top researchers by ThomsonReuters (Web of Science) based on the number ofcitations earned in last three years in six categories ofComputer Science. He is a Research Professor withthe Department of Computer Science and Engineer-ing, Kyung Hee University, Seoul, South Korea, fromwhere he completed his Postdoctoral fellowship un-der the prestigious grant of Brain Korea 21st Century

Plus. He was a former researcher and developer with the Centre for Mobile CloudComputing Research, University of Malaya. His numerous research paper arevery famous and among the most downloaded in top journals. He is currentlyserving/served as a Guest/Associate Editor in various Journals. He has beeninvolved in a number of conferences and workshops in various capacities. Hisresearch interests include big data, edge computing, mobile cloud computing,the Internet of Things, and computer networks.

Anselme Ndikumana received the B.S. degree incomputer science from the National University ofRwanda, Butare, Rwanda, in 2007. He is currentlyworking toward the Ph.D. degree with the Departmentof Computer Science and Engineering, Kyung HeeUniversity, Seoul, South Korea. His professional ex-perience includes a Chief Information Officer, a Sys-tem Analyst, and a Database Administrator at RwandaUtilities Regulatory Authority from 2008 to 2014. Hisresearch interests include deep learning, multiaccessedge computing, information centric networking, andin-network caching.

Ejaz Ahmed (S’12–M’17–SM’18) received thePh.D. degree in computer science from the Universityof Malaya, Kuala Lumpur, Malaysia. He is an Asso-ciate Technical Editor/Editor of the IEEE COMMU-NICATIONS SURVEYS AND TUTORIALS, IEEE COM-MUNICATIONS MAGAZINE, IEEE ACCESS, ElsevierJNCA, KSII TIIS, and Elsevier FGCS. He was a Chairand Co-Chair with several international conferences.His research interests include mobile cloud com-puting, mobile edge computing, Internet of Things,cognitive radio networks, and big data.

Rasheed Hussain (S’12–M’17–SM’19) received theB.S. degree in computer software engineering fromthe University of Engineering and Technology, Pe-shawar, Pakistan, in 2007, the M.S. and Ph.D. degreesin computer science and engineering from HanyangUniversity, Seoul, South Korea, in 2010 and 2015,respectively. He worked as a Postdoctoral Fellowwith Hanyang University, South Korea, from March2015 to August 2015 and as a Guest Researcher andConsultant with the University of Amsterdam fromSeptember 2015 to May 2016. He also worked as an

Assistant Professor with Innopolis University, Innopolis, Russia from June 2016to December 2018. He is currently an Associate Professor and the Head of theMS program in security and network engineering at Innopolis University, Russia.He is also the Head of the Networks and Blockchain Lab, Innopolis University.His research interests include information security and privacy and particularlysecurity and privacy issues in Vehicular ad hoc networks (VANETs), vehicularclouds, vehicular social networking, applied cryptography, Internet of Things,content-centric networking, cloud computing, and blockchain. He was editorialboard member for various journals including IEEE ACCESS, IEEE INTERNET

INITIATIVE, Internet Technology Letters, Wiley, and is a reviewer for most of theIEEE TRANSACTIONS, Springer, and Elsevier Journals. He is Technical ProgramCommittee Member of various conferences such as IEEE Vehicular TechnologyConference, IEEE Vehicular Networking Conference, IEEE Globecom, IEEEInternational Conference on Connected Vehicles and Expo, and so on. He isa Certified Trainer for Instructional Skills Workshop. Furthermore, he is alsoACM Distinguished Speaker.

Choong Seon Hong (S’95–M’07–SM’11) receivedthe B.S. and M.S. degrees in electronic engineeringfrom Kyung Hee University, Seoul, South Korea, in1983 and 1985, respectively, and the Ph.D. degreefrom Keio University, Tokyo, Japan, in 1997. In 1988,he was with 1048 KT, where he was involved inbroadband networks as a member of Technical Staff.Since 1993, he has been with Keio University. He waswith the Telecommunications Network Laboratory,KT, as a Senior Member of Technical Staff and asthe Director of the Networking Research Team until

1999. Since 1999, he has been a Professor with the Department of ComputerScience and Engineering, Kyung Hee University. His research interests includefuture Internet, ad hoc networks, network management, and network security.He is a member of the ACM, the IEICE, the IPSJ, the KIISE, the KICS, theKIPS, and the OSIA. He was the General Chair, the TPC Chair/member, anOrganizing Committee Member of international conferences, such as NOMS,IM, APNOMS, E2EMON, CCNC, ADSN, ICPP, DIM, WISA, BcN, TINA,SAINT, and ICOIN. He was an Associate Editor of the IEEE TRANSACTIONS

ON NETWORK AND SERVICE MANAGEMENT and the IEEE JOURNAL OF COM-MUNICATIONS AND NETWORKS. He is currently an Associate Editor of theInternational Journal of Network Management, and an Associate TechnicalEditor of the IEEE Communications Magazine.

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