Implementation of D2D enabled Mobile Cloudbased Content Distribution Architecture in 5G

NetworksAbdul Moiz, Muhammad Ikram Ashraf, K. Saad B. Liaqat, Shahid Mumtaz∗ and Marcos Katz

Department of Communication Engineering, University of Oulu, Finland∗Instituto de Telecomunicaes Aveiro, Portugal

Email: {amoiz, ikram, mkatz}@ee.oulu.fiKanwar.S.Liaqat@student.oulu.fi, smumtaz@av.it.pt∗

Abstract—The technological advancement has increased thedemand for different kind of multimedia traffic and energy-consuming applications and therefore posing new challenges fornetwork operators to cope up with the current and future re-quirements. The three characteristic challenges faced by networkoperators are enhanced system throughput, dynamic environmentadaptability and productive utilization of the available resources.While substantial amount of work has been done in this context,particularly on cooperative and cognitive networks, the very ap-proach has certain limitations and shortcomings. In this respect,D2D (Device-to-Device) communication offers promising gainssuch as cellular data offloading, energy savings and bandwidthefficiency. D2D also offers efficient distribution of popular contentin short range. Here we propose Mobile Cloud (MC), a novelyet simplistic system architecture that employs cognitive andcooperative strategies to address the above mentioned challenges.The system exploits short-range links to establish a smartsocial network among the nearby devices adapts according toenvironment and uses various cooperation strategies to obtainefficient utilization of resources. In this work, we demonstratethe practical implementation of a self-organizing MC that isformed by a group of devices themselves in close proximityby exploring short-range links. We also propose and implementdifferent content distribution techniques for our MC based test-bed. Furthermore, we also compare and present practical resultsfor different content distribution techniques in terms of systemthroughput.

Keywords-mobile cloud; D2D communication; cooperativecommunication; multi-radio communication

I. INTRODUCTION

The rapid demand of bandwidth intensive applications suchas multimedia streaming, social networks and popular contentdistribution has dramatically changed the usage of wirelessspectral resources [1]. In this respect, D2D communicationis seen as a key technique to boost the wireless capacityand offloading traffic of cellular networks. Recently, 3GPPLTE release 12 has dealt with D2D communications in orderto address the ever-increasing demands for upcoming 5Gnetworks [2].

Reaping number of benefits of D2D communication byexploiting short-range links yields increased network capacity,extended coverage, improved energy efficiency and enhanceddata offload [3]. Numerous D2D applications are studied and

presented in the literature such as service discovery, trackingof objects and content sharing in physically close proximity.This advancement has also revolutionized the smartphoneindustry. These phones are low cost yet powerful to performnumerous tasks and operations simultaneously. The concept ofphones has changed from voice calls to a complete hand-heldcomputers. These smart devices are equipped with numeroustechnologies (WiFi, ZigBee or Bluetooth) which can be uti-lized to perform a task more intelligently and efficiently.

D2D communication can be classified as in-band and out-band D2D communication [4]. In in-band D2D communica-tion, licensed spectrum is used for both D2D and cellularlinks. In addition to this, there can be dedicated as well asshared radio resources for both cellular and D2D link. Theprimary disadvantage of using in-band communication is theinterference caused by D2D links to cellular links and viceversa. In contrast, out-band communication has a leverage ofusing unlicensed band therefore eliminating the interferencebetween both links. One of the key challenges in these D2Dnetworks is the selection of the anchor node1 for the contentdistribution within a group of nodes. Let’s consider an exampleof close-proximity scenario, where users are seeking for thesame content (e.g., popular content). In a conventional setting,the base station often end up serving all users with multipleduplicate transmission which leads inefficient utilization ofthe spectrum resources. In contrast to this, if the user devicesperform collaboration in such a manner that only the anchornode fetch the data from the base station and distribute themto the nearby devices using short-range links. In this way, theperformance of the overall system can be further enhanced.Furthermore, the anchor node can be selected based on variousparameters such as energy, SINR, channel condition, etc. Forsimplicity, we are using node’s available power as a metric foranchor node selection.

The main contribution of our proposed implementationwork is in several aspects which are, 1) an out-band D2Dcommunication network using WiFi, 2) self-organizing D2D

1The terms anchor node, cluster head, cloud leader are interchangeableand used in the same context

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network, and 3) content distribution techniques. The rest ofthe paper is organized as follows. Section II covers some ofthe related work in this field. Section III presents the mobilecloud architecture. Implementation is discussed in Section IVwhich is followed by the results analysis in Section V. Finally,conclusions are drawn in Section VI.

II. RELATED WORK

D2D communication has attracted a lot of attention inthe recent years. Numerous works have been presented inliterature dealing with theoretical and practical aspects ofD2D-enabled networks. A related work is simulated in [5]–[6]and implemented in [7]. In these D2D enabled architectures,usually a D2D server is responsible for the establishment ofdirect links among different devices. Once the communicationlink is established, devices in close proximity trigger contentsharing. In [8], a Sangam architecture is presented in which,a D2D server is used for the formation of the group. Eachmember of the group requires to download the same content.Moreover, the content is divided into number of chunks andeach download depends on battery level /or CPU performanceof the individual member. Finally, after completion of thedownload process, these chunks are shared over WiFi ad-hoc link with other group members. Unlike the Sangamarchitecture, instead of using WiFi ad-hoc links the content islocally distributed over the WiFi Direct links [9]. A Subscribe-and-Send method [10] is investigated to offload the cellulartraffic using the local WiFi D2D communication. The basicidea behind subscribe-and-send architecture is to utilize localD2D communication by waiting certain amount of time forthe content to appear in any nearby device. After this time,the content is downloaded from cellular link anyway. In [11],authors presented a technique to download the specific contentthrough Internet using WiFi and cellular links simultaneously.Moreover, in this work, the content is divided into differentsizes based on the link throughput.

III. MOBILE CLOUD

A Mobile Cloud (MC) is a collaborative arrangement ofmobile devices called cloud nodes (CN) that communicatewith each other through short-range links while these nodescould also be connected to cellular base station using cellularlinks [12]. In MC-based architecture, a node having optimumavailable resources acts as a cloud leader2 (CL). However, thenumber of CL in a cloud is not limited to one and there can bemore than one CL within a MC. The resources or conditionsbased on which the CL is selected can be processing capability,power resources, channel condition, data storage, sensors etc.Each of these resources are assigned different weights on thebasis of importance and scenario. In this manner, CL alsoacts like a relay node between the cellular network and D2Dnetwork (inside MC) which is beneficial for overall systemin saving cellular bandwidth as well as energy by using short

2CL can be also consider as relay node between cellular and D2Dnetwork.

range communication. The performance of MC and its energyefficiency is further investigated in [13].

MC can be classified into three main types of architectureswhich are 1) central cellular controlled, 2) distributed cloudleader, and 3) hybrid [14]. These architectures are differenti-ated based on the role played by the entities. In this paper,we focus on the implementation of a distributed cloud leaderarchitecture. In this type of architecture, CL is responsible forthe formation, content distribution and management of MC.Moreover, this architecture is self-organizing in a way that anew CL is selected whenever a CL leaves the cloud.

One of the key aspect of MC is the efficient distributionof the content among the nodes. Several works have beenpresented in the literature to address the content distributionsuch as [15]-[16]. In [17], the authors presented an idea ofsocial caching. In social caching, the popular content basedon the proximity is stored locally on the nodes for efficientdata access in the close vicinity. Such distribution techniquesfurther boost the performance of the network by exploring theD2D enabled MC based architecture. In this way, the overallsystem model of MC appears to have the essence of cognitiveand cooperative network architecture.

IV. IMPLEMENTATION

Our work mainly focus on the practical implementation ofthe test-bed, our proposed system model is shown in Fig. 1.In our test case scenarios, we use 3G network as backbonecellular network whereas, WiFi network is used for shortrange communication. Android-based Samsung Galaxy S4smartphones are used for MC application for proposed test-bedwhile Linksys EA6300 WiFi router is used for establishment ofWiFi network. Furthermore, the video contents with differentsizes are being stored in the MC content server provided byUniversity of Oulu. The server provides the specific contentupon request and can be accessible through a web link.

Fig. 1. Proposed test-bed

The MC application architecture is shown in Fig. 2. TheMC application is installed and running over all nodes in thecloud network. Once the application is initiated over the nodes,it performs the following tasks:

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Fig. 2. MC app architecture

A. Cloud formation

Cloud formation takes place in two steps. In the first step,all the nodes perform neighbour discovery in which the nodesbroadcast their ID, metric value (MV), and available contents.Once the neighbor discovery is complete and all the nodeshave information of their neighbouring nodes, CL is selectedas the second step. Each node selects its own CL based on thehighest MV. MV is generated by the combination of differentparameters such as cellular RSSI, available battery level,computational power, available storage, cellular services etc.These parameters have different weight for the MV dependingon its importance. In our proposed work, the MV is selectedbased on available battery level (BL) and AC charging statusof the node which is expressed in (1).

MV =

{(BL+α), AC charging = true,(BL), otherwise. (1)

where α is the weight of the parameter.

B. Multi-radio communication

Multi-radio communication is the essential feature of MCarchitecture. In our proposed test-bed, multiple radios canwork simultaneously without interrupting each other. By ex-ploring the multi-radio functionality of cloud nodes, the con-tent can be downloaded in the same time using cellular or WiFiinterface. In order to enable the multi-radio functionality, twosteps are performed. First, a customized version of Androidv4.3 is made for our smartphones. In Android source code,modification has been made in ConnectivityService.java whichis a part of Service module responsible for managing networkconnections. Secondly, the default gateway inside networkrouting table is changed from WiFi interface to cellulardata interface. This specific change in routing table is onlyperformed in CL.

C. Content distribution

As described in the previous sections, one of the mainobjective of our test-bed implementation is content distri-bution. Before explaining the content distribution techniquesconsidered in our work. Let’s assume a scenario where allnodes in the network seeks for the same popular content. Byusing the legacy approach a separate download required byeach individual node over the cellular links. Contrary to this,

MC is used to increase both bandwidth and power efficiencyby downloading the popular content once via cellular link anddistributing it to other nodes using WiFi. In order to analyzedifferent content distribution for our given MC based test-bed,we classified content distribution as in-cloud and out-clouddistribution.

1) In-cloud content distribution: For In-cloud content distri-bution scenario, the requested content resides inside thecloud (i.e., locally stored in the cloud). Therefore oncethe content request is made inside the cloud, the requestedcontent is shared using the short-range links over WiFi.A use case scenario for In-cloud content distribution withnumber of steps is depicted in Fig. 3

Fig. 3. In-cloud content distribution

2) Out-cloud content distribution: Content can be down-loaded from the Internet via CL if the desired content isnot available locally. As shown in Fig. 4, whenever a nodewants to download content from the Internet, a requestwill be sent to CL, content will then be downloaded byCL via cellular link and distributed to all the nodes in thecloud.

Fig. 4. Out-cloud content distribution

In order to analyze the performance of the content distributionsfor different arrangement of nodes, we define four techniques.

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These content distribution techniques are named as star, treewith one relay node (TR1), tree with two relay nodes (TR2)and tree with three relay nodes (TR3). These are star, tree withone relay node (TR1), tree with two relay nodes (TR2) andtree with three relay nodes (TR3). The content is divided intonumber of packets before distributing to other nodes in theMC. Let’s define, parameters for single packet unless definedotherwise.

N = {1, . . . , n} be a set of CNs in MC

txs = start time of download on node x ∈ N|t1s < . . . < tns

txe = end time of download on node x ∈ N|t1e < . . . < tne

tpe = end time of download on previous nodeΔtx = download time of a packet on node x

ΔTx = total download time of the content on node x

Δttrans = Average transmission delay.

Moreover, the parameter tpe for a node x is individuallycalculated for each distribution techniques.(a) Star: In this technique, CL is responsible for the content

distribution in MC. Content is distributed in descendingorder based on MV. Let’s define the parameter tpe foreach node in given Start-based technique for contentdistribution shown in Fig. 5.

Fig. 5. Star technique for content distribution

The total download time for a content is given by:

txs = tpe +Δttrans, (2)

Δtx = txe − txs , (3)

ΔTx =C∑i=1

Δtx(i), (4)

where C denotes total number of packets for a content.(b) Tree: In this distribution technique, CL as well as relay

nodes are responsible for the content distribution. Threevariations of relay nodes have been studied. Fig. 6 showsthe TR-2 technique with 2-relay nodes. The downloadtime at each node for a content is calculated as per(2)–(4). Each packet traverse to the last node, thus effect-ing the total download time. Let’s consider the downloadtime for a packet at the last node is represented by tL.

Therefore, the total download time for a given contentover all nodes is calculated as:

tL = t1s −Δttrans +

f(L)∑i=1

(Δt+Δttrans) (5)

where,

f(1) = 1 (6)

for TR2:

f(x) =

{f(�x

2 �) + 1 mod(x2 ) = 0

f(�x2 �) + 2 mod(x2 ) = 1

(7)

for TR3:

f(x) =

⎧⎪⎨⎪⎩f(�x

3 �) + 2 mod(x3 ) = 0

f(�x3 �) + 3 mod(x3 ) = 1

f(�x3 �) + 1 mod(x3 ) = 2.

(8)

It is worth mentioning that, the expressions (6–8) arespecifically for the given setup of TR2 and TR3.

Fig. 6. Tree technique with two relay node (TR2)

D. Cloud management

We implemented self-driven cloud management, in whichall nodes in the proposed MC broadcast discovery messages.These discovery messages broadcast with regular interval inorder to keep-alive the node presence in the network. It alsohelps to find if there is any other eligible candidate for CL.When a node leaves the MC, every node waits for a certaintime and update its neighboring list based on the availablenodes in the MC. Similarly, a new CL will be elected if oldCL leaves MC.

V. PERFORMANCE ANALYSIS

In this section, we analyze the performance of our proposedMC architecture and compare it with the legacy approach ofdownloading the content using cellular link. For performanceresults MC application following parameters and environmentvariables are considered.

• A single MC is considered with a single CL. The nodeselected as CL operating on the customized Android OS.

• We have N = 7 number of nodes in the system and eachnode seeking for the same content.

• Cloud formation and CL selection is already done.

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Fig. 7. Flowchart for tree formation

• No mobility is considered, such that every node in thenetwork consider to be static.

• Every node in the network operating on WiFi IEEE802.11ac standard.

• Content is considered to be non-real time which is storedon fixed content server accessible through URL (UniformResource Locator)3 via cellular link. The content size forthe measurement results is 5MB.

• The locations for testing the system are chosen based onthe cellular RSSI (Received Signal Strength Indicator)values of the CL. In our proposed work, test results arecollected over the low, normal and high values of RSSI.

• In order to get the complete download time, two timestamps are saved in the log file4, one at the start and oneat the end of the download.

• Cellular RSSI varies randomly due to unpredictable chan-nel conditions. To mitigate its effect, average downloadtime for all iterations (I) and nodes (N ). It is calculatedby the following equation:

ΔTavg =

N∑n=1

I∑i=1

ΔT(n,i)

I ×N, (9)

where, ΔTn,i is the download time of node i at ith

iteration.• The system throughput for the content size (S) is given

by:

Throughput =ΔTavg

S. (10)

3http://www.ee.oulu.fi/∼amoiz/downloads/4/sdcard/Download/COIN testResult/COIN RESULT.txt

Different steps of tree formation algorithm are summa-rized in flow chart shown in Fig. 7. In this flow chart,PN and NN represents previous node and Nth node (orlast node) respectively, and RN denotes number of relaynodes or tree width.

A. ResultsConforming to the above discussion, let us analyze the

performance of MC architecture with respect to download timeand system throughput.

Fig. 8 demonstrates the variation in average download timeon different nodes for given distribution method (TR1). It isshown that, CL is the first node to receive the content inMC therefore, it has least average download time. It is worthto mention, that average download time varies with differentdistribution techniques due to topological arrangement ofnodes.

Fig. 8. Download time of nodes in TR1

Fig. 9 shows the average download time for single node anddifferent distribution techniques. For this experiment we alsoincorporate the effect of RSSI over the download time. It canbe observed that download time increases as channel conditionfor cellular link gets worst. At -65dBm the average downloadtime of a single node is almost similar to other distributiontechniques. It is important to note that MC is serving sevennodes in approximately the same time as one node beingserved using legacy approach. As per our test results, singlenode and MC takes 3.8s, 4.03s respectively to download thedesired content.

Fig. 10 shows the system throughput of the considered MCtest bed. It is shown that, MC outperforms for higher valuesof RSSI, whereas, performance of single node and MC isapproximately similar at lower values of RSSI.

One of the main advantage of the MC MC is to efficientlyutilize the system bandwidth. For instance, considering legacyapproach, if a single node requires B bandwidth units todownload the desired content then, n×B bandwidth units arerequire by n nodes. Contrary to this, MC requires only onenode (CL) to download the desired content from the cellularnetwork, thus saving (n− 1)×B bandwidth units.

VI. CONCLUSION

The goal of this proposed test-bed implementation is todevelop a system model that addresses all the three challenges

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Fig. 9. Download time

Fig. 10. System Throughput

presented in the beginning. Targeting these goals, we presentedMC, a novel yet simplistic approach towards cooperative andcognitive network that has the following properties: 1) Self-organizing D2D network, 2) A system model that exploitsmulti-radio communication, and 3) Sophisticated content dis-tribution techniques.

MC based architecture offers multiple benefits for bothend consumer and the network operator. For consumer withlimited resources, this system model provides better energyefficiency and improved system throughput. Moreover, it pro-vides promising gain considering the popular media content.It also provides a small social network for a group of userswith the common interest.

From the operator prospective, MC proposes several advan-tages. One of the major contribution of the MC is to save thebandwidth. MC also offers network operators to offload thedata traffic for the media contents that is high in demand or thecontent which is more likely to be accessed repeatedly withina group of people in a confined proximity e.g., airport or aclass room. Moreover, MC also offer to expand the networkcoverage to the areas where cellular link quality is not up-tothe mark or suffers from excessive channel degradation.

ACKNOWLEDGMENT

This work is supported by DCE (University of Oulu, Fin-land), Centre for Wireless Communication (Oulu) and COINproject funded by Tekes.

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