PIS: A Multi-Dimensional Routing Protocolfor Socially-Aware Networking

Feng Xia, Senior Member, IEEE, Li Liu, Behrouz Jedari, and Sajal K. Das, Fellow, IEEE

Abstract—Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with

social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections,

social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore

the impact of three social features, namely physical proximity, user interests, and social relationship on users’ daily routines. Then,

we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social

dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes

a time slot management mechanism to discover users’ movement similarities in different time periods during a day. We compare the

performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The

experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low

communication overhead.

Index Terms—Mobile social networks, socially-aware networking, routing, physical proximity, interest, social relationship

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1 INTRODUCTION

TODAY a large volume of mobile data traffic is transferredvia 3G cellular networks which make them overloaded.

To tackle this problem, proximity-based wireless technolo-gies such as Bluetooth and Wi-Fi have been used as promis-ing solutions to lighten the network traffic among mobileusers. Socially-aware networking (SAN) [1] is a new trendof delay tolerant networks (DTNs) [2] and opportunisticcommunications [3], [4], which exploits social properties ofmobile carriers (i.e., users) to design efficient networkingprotocols. In this setting, mobile nodes employ store-carry-and-forward mechanism to communicate with each otherwith the help of their short-distance and low-cost devices inorder to share data objects (e.g., pictures, advertisements,software updates) among interested users. The basic idea ofSAN is to exploit the users’ social context information toimprove the performance of data routing protocols. Toachieve this goal, spatio-temporal and connectivity proper-ties of the mobile users are captured in an efficient way.

When a mobile node contacts others, all the encounterednodes could be candidates for relaying her messages tothe destination node. Nevertheless, selecting appropriateencounter nodes is a very challenging problem. One poten-tial approach is to forward a message to nodes which arecloser to the destination (a greedy strategy). In this context,some investigations on human movement patterns havebeen carried out to explore the spatio-temporal properties

of mobile users and predict their future contacts [5], [6], [7].Since the physical location of mobile users vary, more stablesocial factors should be considered to make efficient andeffective routing decisions. To overcome the shortcomingsin mobility based routing approaches, several social-awarerouting protocols for SAN paradigm have been proposed inthe literature (see [8] for a survey). Some recent social-awarerouting protocols such as [9], [10], [11], [12] make forward-ing decisions based on the users’ social characteristics andsimilarities. In particular, social network analysis techni-ques are used to extract meaningful social relationshipsamong mobile users. The main motivation is that the socialbehaviors of mobile nodes have long-term characteristics,which provide reliable connectivity among them.

In this paper, we take multiple social characteristics ofusers into consideration to design a stable routing protocolin the SAN paradigm. Considering the users’ daily routines,we observe that three social factors—namely physicalproximity, interests, and social relationship—influence theperformance of data forwarding. The physical proximity indi-cates direct contacts among mobile nodes. When two nodescome into the communication range of each other, they canexchange their messages. The physical proximity is a popu-lar factor which is used in many existing routing protocolsextensively [9], [13], [14]. The second factor is the user inter-ests which indicate their preferences for data. Based onHomophily concept [15], individuals with common intereststend to meet each other more often and perform similaractions. Furthermore, their interests are stable for a longtime. The third factor is social relationships such as friendship,family, or colleague which describes the users’ personalrelations. Nodes with strong social relationships contacteach other more frequently and regularly. Friendship basedrouting [16] is a well-known example in this regard whichidentify the social ties among mobile nodes based on theircontact history and thus form social communities. The basic

� F. Xia, L. Liu, and B. Jedari are with the School of Software, DalianUniversity of Technology, Dalian, China.E-mail: f.xia@ieee.org, liuli_free@126.com, behrouz_jedari@yahoo.com.

� S. K. Das is with the Computer Science Department, Missouri Universityof Science and Technology, Rolla, MO 65409. E-mail: sdas@mst.edu.

Manuscript received 28 Aug. 2014; revised 14 Nov. 2015; accepted 27 Dec.2015. Date of publication 18 Jan. 2016; date of current version 28 Sept. 2016.For information on obtaining reprints of this article, please send e-mail to:reprints@ieee.org, and reference the Digital Object Identifier below.Digital Object Identifier no. 10.1109/TMC.2016.2517649

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1536-1233� 2016 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.

idea of our novel approach lies in the fact that integration ofthese social attributes can be utilized to improve the overallefficiency of a routing protocol since each of these parame-ters can affect a data delivery protocol in different time peri-ods. For example, One (or more) factors may work in thecurrent time period while others may work in a future timeperiod. It may also be possible that these three factors worksimultaneously in some other occasions.

As shown in Fig. 1, we provide an example to illustrateour idea. Let us consider Alice’s regular mobility pattern indifferent time periods during a working day. She takes a busfrom 7:30 to 8:30, works between 8:30 and 17:00, goes to agym to exercise from 17:00 to 19:00, and comes back home atthe end of the day. In each time period, different social char-acteristics are exhibited. Moreover, she could share datawith different people such as strangers in a bus (geographicproximity), colleagues in her office (colleague relationship),friends (common interests) and familymembers (family rela-tionship). It is difficult to determine which social characteris-tic always plays the decisive role throughout the day.Therefore, Alice could select appropriate forwarders by com-prehensively fusing social properties of people around her,as well as temporal dimensions (time regularity).

In this paper, we propose a multi-dimensional routingprotocol, namely Proximity-Interest-Social relationship (PIS)protocol for socially-aware networking paradigm. The PISprotocol integrates multiple social dimensions of mobileusers in a utility function to select the best intermediatenodes and improve the overall routing performance. In thismethod, a time slot management mechanism is used to man-age the social information of users in different time periods.

Our major contributions can be summarized as follows:

� We analyze real mobility traces of mobile users andexplore three social factors: physical proximity, userinterests, and social relationships in order to make astable and adaptable routing protocol in the SANparadigm. Then, we introduce a multi-dimensionalrouting protocol, called PIS protocol based on thesocial properties of mobile nodes.

� Based on the users’ daily routine, we present a timeslot management mechanism in PIS to keeptrack ofcontact records, self/contact interests, and direct/indirect social relationship information. This mecha-nism uses the social properties of mobile users in dif-ferent time slots to reflect the movement routines indifferent time periods.

� We apply an efficient copy control mechanism tocontrol data congestion in the PIS protocol anddecrease the network overhead.

� We compare PIS to three benchmark routing proto-cols, Epidemic [17], PROPHET [18], and SimBet [13]using SIGCOMM09 [19] and INFOCOM06 [20] realworld traces. The experimental results demonstratethat our protocol guarantee higher performance ofrouting which achieves the highest data deliveryratio with a low communication overhead.

The remainder of this paper is organized as follows.Section 2 provides a review of related works on SAN.Sections 3 and 4 presents the PIS protocol and its implemen-tation details. Section 5 describes the performance evaluationresults of PIS in comparison with other well-known routingprotocols. Finally, we conclude the paper in Section 6.

2 RELATED WORK

It is hard to guarantee a stable end-to-end delivery pathamong mobile nodes in the SAN paradigm. Therefore, mes-sage delivery becomes a challenging issue in this setting. ToTackle this problem, several data routing protocols havebeen proposed in intermittently connected networks. Apotential solution is data flooding in which the whole gener-ated data are transferred to other encountered nodes. Flood-ing-based protocols such as Epidemic routing [17] with anunlimited number of message replications waste the net-work resources dramatically and cause data congestion. Tocope with these problems, several single-copy or multi-copy routing protocols have been proposed aiming to limitthe number of message copies and leverage a tradeoffbetween resource usage and message delivery probability.As an improvement to the Epidemic routing, PROPHET[18] routing uses a delivery predictability metric to calculatehow likely a node will be able to deliver a message to itsdestination. In this method, the contact frequency betweenthe nodes is used as a context information.

Recently, social aspects of mobile users have been widelyutilized to streamline routing decisions in the SAN para-digm (see [8] for a survey). The main reason is that socialattributes and relations of mobile users have generally long-term characteristics and they are less volatile. Essentially,these methods attempt to group nodes into communitiesand/or choose a node with high centrality or similarity(e.g., interests, context, common friends, etc.) with the desti-nation node as the packet forwarder.

For example, Bubble Rap [9] is a prominent social-baseddata forwarding algorithm which focuses on social central-ity. The algorithm structures nodes into communities withdifferent sizes based on social parameters. High popularitynodes and community members of the destination areselected as relays. Besides, nodes have various levels ofpopularity (i.e., rank). Each node has a global ranking and a

Fig. 1. Humanmobility: regularity and social characteristics in daily routine.

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local ranking. A node’s global ranking denotes its popular-ity in the entire society, with its local ranking within itsown community. Messages are forwarded according to theglobal ranking until a node in the destination community isfound. Then, the messages are forwarded in the destinationcommunity via nodes with higher local ranking. Similarly,LocalCom [21] forms the community structure and consid-ers the forwarding between different communities. First,similarity metrics according to the nodes’ contact historyare calculated to construct the neighboring graph. Next, adistributed algorithm detects the underlying communitystructure. Finally, LocalCom selects and prunes gateways toconnect communities, control redundancy and facilitateefficient inter-community forwarding.

Friendship describes close personal relationships as aconcept in sociology. In SAN, the friendship can be definedbetween a pair of nodes. In other words, two nodes needto have long-lasting and regular contacts to be friends ofone another. Friendship based routing [16] considers thecontact history of mobile nodes to measure their socialties and form friendship community. This method considersthree behavioral features of close friendship: high fre-quency, longevity, and regularity, and two metrics calledsocial pressure metric (SPM) and conditional SPM aredefined for direct and indirect friendship.

Based on the Homophily principle [15], nodes with simi-lar interests tend to meet each other more often. Commoninterests between users have led to many innovative proto-cols. As an example, social-aware networking (SANE) [22]is a pioneering forwarding protocol extended from the Epi-demic routing. In SANE, nodes exchange their interest pro-files and then each node starts scanning its buffer formessages to relay. In [23], a peer-to-peer content-based filesharing system, called SPOON, is proposed to take advan-tage of interest. SPOON extracts a node’s interest from itsfiles and groups common-interest nodes as communities.The authors in [24] propose a user-centric data dissemina-tion protocol in DTNs where the contact patterns and inter-ests of mobile users are exploited to measure the nodes’centrality. Similarly, SocialCast [25] is a data disseminationprotocol based on publish/subscribe systems which consid-ers the nodes’ social ties and mobility patterns as well astheir interests to select next intermediate nodes.

In addition to the above-mentioned protocols, BEEINFO[26] is proposed as a combination of interest and swarmintelligence to choose the best forwarder. BEEINFO isinspired by artificial bee colony algorithm and adopts den-sity and social tie to perform the inter-community andintra-community forwarding processes. Although the com-munities are classified by user interests, the exact classifica-tion is not described and further exploration on interest isnot provided.

Some studies have also considered the user mobility reg-ularities into consideration to improve the routing perfor-mance. As an example, Habit [27] constructs the regularitygraph and interest graph for routing protocol by leveraginginformation of nodes’ movement regularity and their socialnetworks. In HiBOp [28], each node exchanges contextinformation only with its community members and storestheir context information. In the forwarding phase, onlynodes of the destination community are selected as

candidate forwarders. It selects packet forwarder accordingto the nodes’ contact history. In fact, HiBOp looks for nodeswhich have the highest match with the known contextattributes of the destination node. SEDUM [29] is a socialnetwork oriented and duration utility-based routing proto-col that considers movement patterns of mobile users toincrease routing performance. The duration utility in thismethod is the ratio of total contact duration between twonodes over a time period. SEDUM considers both contactfrequency and duration in movement patterns of networknodes. In addition, it uses multi-copy routing which can dis-cover the minimum number of copies of a message using anoptimal tree replication algorithm.

Some recent works combine multi social features toexploit valuable information. In [30], the spatial and tempo-ral characteristics of mobile users are analyzed and tempo-ral communities are constructed in order to predict theirfuture contact probabilities. Similarly, CiPRO [31] considersboth spatial and temporal dimensions of human mobilityto predict the context of nodes so that the source deviceknows when and where to start the routing process to maxi-mize the transmission delay and minimize the networkoverhead. M-Dimension routing [32] exploits local informa-tion derived from multiple dimensions such as geographicand social dimensions. ML-SOR [33] extracts social networkinformation from multiple social contexts and combinesthree measures: node centrality, tie strength and a tie pre-dictor thereby avoiding suboptimal paths. Finally, theauthors in [34] observed that the transient social contact pat-terns of mobile nodes during short time periods are usuallydifferent from their cumulative contact patterns. Then theyexploited the transient social contact patterns from threeaspects including transient contact distribution, transientconnectivity, and transient community structure to improvethe forwarding performance.

Considering the shortcomings of the above-mentionedmethods, in this paper, we combine three social dimensionsof mobile users in a utility function to select the best relaynodes in message forwarding. In our proposed multi-dimensional protocol, user movement during a day isdivided into several time periods and a time slot manage-ment mechanism is used to measure the similarity of theusers with respect to their social features. We present thedetails of the PIS protocol in the next section.

3 PIS ROUTING PROTOCOL

We propose a multi-dimensional routing protocol, calledPIS protocol, which exploits the nodes’ physical proximity,interests, and social relationship information to deliver mes-sages among them in an adaptive and reliable way. Com-pared to existing social-based routing protocols, PIS mainlyrelies on multi-dimensional social properties and time regu-larities in order to improve the performance of data routing.

Considering the sample scenario as described in Section1, the three social factors considered in PIS coexist and inter-weave in real SAN applications. Thus, they can effectivelybe used to identify appropriate intermediate nodes in differ-ent time periods of a routing process. To clarify the func-tionality of the PIS protocol, we provide a example routingin Fig. 2. In this figure, node S wants to deliver its messages

XIA ETAL.: PIS: A MULTI-DIMENSIONAL ROUTING PROTOCOL FOR SOCIALLY-AWARE NETWORKING 2827

to nodeD.NodeA, B, andC are intermediate nodes. Nodes S,A, and B contact each other in t1. Note that A and D havehigher physical proximity in time period t5 while, C and Dhave themost common interests in t3. Node B frequently con-tacts others which share their common interests with D in t2.Thus, it has higher opportunity to meet nodes which havecommon interests with D (like node C). The friendshipamong nodes B and D has the highest value in t4. Therefore,the path S-B-C-D is the best forwarding path between nodes SandD to relay their messages in the shortest time. Also, B willbe the best forwarder. Integrating different social factors, themore appropriate forwarder will be selected to implementmore stable delivery, even in different times or scenarios.

The physical proximity is the most reliable feature todesign a routing protocol. In PIS, the physical proximityrefers to the direct contacts among the nodes. We introduceego network [14] to obtain the proximity information of themobile nodes by utilizing their contact history. However,considering the limited power and storage resources in themobile devices, they cannot maintain the global networkstate and can thus only capture the information of their con-tacting nodes (i.e., local information).

In order to overcome the restrictions of the physical loca-tion information, we consider user interests which are morestable and easy to maintain. We call the user interests as self-interest, while there is another kind of interests called contactinterest. To explain the concept of the contact interest, weassume that there is a concentrated area of nodes within acommon interest, such as football. The other nodes which arenot interested in football join and leave this area frequently.Therefore, they have advantages to relay messages to thisarea. On the other hand, the users’ self-interest informationincludes the topics they are interested in. In addition, we con-sider the users’ social relationships in the PIS protocol. Weconsider both the direct and indirect social relationships,where the direct relationships refers to their friends, while theindirect relationship refers to the friends of their friends.

Role of the three social features applied in the PIS proto-col can be observed in the mobile users’ daily routines on aregular basis. This information are used in the PIS protocolto predict the future contacts of mobile nodes. To this aim, aday is divided into several time slots where each social fea-ture is managed in a distinct time slot. Then, we design atime slot management mechanism including distinct mod-ules to process the information in each time slot. Forinstance, if a time slot includes two hours, a day is dividedinto 12 time slots. Fig. 3 illustrates the structure of a timeslot which is divided into three components: Physical Prox-imity, Interest and Social relationship. Furthermore, threemodules are designed to maintain the updated informationin each time slot. We discuss this idea with more details inthe following sections.

3.1 Physical Proximity

The physical proximity includes the nodes’ frequent anddirect contact information in every time slot. Obviously, thelocation information of mobile nodes can be different sincethey may visit different places. Nevertheless, it is revealedthat mobile users normally move between four and sixmajor locations which occupy more than 70 percent of theirmovements locations [35]. Considering this idea, eachmobile node in the PIS protocol maintains an ego network inorder to maintain the physical proximity of other nodeswhere a mobile node is considered as an “age”. In otherwords, the ego network is a matrix which reveals the contactinformation of other nodes. In the PIS protocol, an ego net-work is constructed by each node locally without havingcomplete knowledge of the entire network. Then, in eachtime slot, a node records the frequency of its direct contactinformation. At the same time, it updates the ego networkfor each encountered node locally.

The structure of Physical Proximity module consists ofcontact list and ego network as shown in Fig. 3. The contact listincludes a set of ðnodeID; degreeÞ pairs, where nodeID showsthe identity of encountered nodes and degree representstheir respective contact frequency (encountered time)within a time slot. The Physical Proximity Updating Modulemaintains and analyzes the contact and ego network infor-mation of the nodes during a particular time slot.

3.2 User Interest

The authors in [36] reveal that 80 percent of computer filesfall into 20 percent of their total file categories. Taking thisidea into consideration, 76 participants in SIGCOMM2009data set [19] have 711 interest topics. However, the numberof topics with at least two people interested in is 50, thatis only 7 percent. When we use applications or servicesinstalled in portable devices, our interest is easy to gatherand analyze from our operations. Possibly, when weconnect to mobile social networks, our interest list can bedownloaded from the Internet directly. Additionally, thefunction of fringe nodes should not be ignored. For instance,there may be a nodes concentrated area for one interest. The

Fig. 3. Structure of a time slot in the time slot management mechanism.

Fig. 2. An example of multi-dimensional relaying.

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fringe nodes are not interested in that interest, but fre-quently pass the area. Therefore, the fringe nodes are alsothe good forwarders. Considering utilizing fringe nodeseffectively, the interest metric records not only the node’sself-interest and degree, but also the encountered nodes’contact interest information (for fringe nodes).

The interest structure consists of two parts: Self-Interest(SI) andContact Interest (CI). Both of them store a list of inter-ests. SI records the nodes’s self-interests. CI , which consistsof a list of ðinterest; degreeÞ pair, records interests of encoun-tered nodes and their contact degrees. When a fringe nodehas a higher degree of one kind of interest, it will be higheropportunities to encounter the nodes with this interest.Then, it can be selected as a candidate forwarders. The Inter-est updating module is responsible for recording and updatingthe information of Self-Interest andContact Interest.

3.3 Social Relationship

The strength of the social relationships among mobile userscan be used to predict their contact probabilities. In the SIG-COMM2009 data set, 898 messages are generated where thenumber of unicast messages is 263 (29.3 percent). Since uni-cast happens between friends, top 10 nodes generated 457messages and the number of unicast messages is 172 (37.6percent). Therefore, the friendship is an important featureto explore the number of message transmissions amongsource and destination nodes. In our analysis, we select top10 nodes which generate the highest number of messages.Fig. 4 shows the number of generated broadcast, multicast,and unicast messages in the SIGCOMM2009 data set whichdemonstrates that friendship is an important property inusers’ communication pattern

The PIS protocol considers both direct social relationships(DSo) and indirect social relationships (InSo). Taking the users’friendship information into consideration, the indirectsocial relationships indicate the friends of friends. Whentwo users have common friends, they are expected to havehigher probability to contact each other. DSo denotes the setof nodes with direct social relationships whereas InSo

denotes the set of nodes with indirect social relationships.Let InSo denotes the set of ðnodeID; degreeÞ pairs whichincludes the strength of social relationships among mobilenodes. We design the social relationship updating module tomaintain and update the social relationship information.

4 IMPLEMENTATION OF PIS

Mobile nodes in the SAN paradigm have homophily phe-nomenon which is the tendency of individuals to associateand bond with similar minded others. This is oftenexpressed in the adage “birds of a feather flock together”.The main idea of the PIS protocol relies on the homophilyphenomenon from three social dimensions.

When two nodes A (also denoted NA) and B (or NB) con-tact each other, they update their contact list and exchangetheir interests, social information, and message list. For asample message in the message list, PIS compares the simi-larities of nodes A and B to the destination node D withrespect to the three dimensions. Then, it calculates the simi-larity utility function to select an appropriate forwardingnode according to the similarities of the nodes.

Based on the time slot management mechanism, we get acycle and periodic time slot, as shown in Fig. 5. PIS managesthe information of physical proximity, interest, and socialrelationship in different time slots. In order to make a for-warding decision, PIS compares the corresponding similari-ties in the last time slot i. The time slot i indicates the currenttimewhile i� 1 shows the next one. Generally, the parameteri is closely related to the remaining TTL (time-to-live) of themessages. For example, if TTL of a message is 10 hours andtime slot is 2 hours, i can be assigned as 5 since the messagewill be dropped after 10 hours. PIS compares the similaritiesbetween the nodes in current and next four time slots, whichrepresents the contact probabilities in 10 hours. In addition,we set different weights to each time slot such that the closeris the time slot to the current time, the larger is theweight.

Essential notations and symbols are summarized inTable 1. The detailed message exchange process betweennodes A and B is outlined in four steps as follows. Withoutloss of generality, we only describe the process from theviewpoint of source node A, while the same process hap-pens to node B.

1) First, nodes A and B update their contact list andexchange their social information including contactlist, SI and DSo in order to update the information oftheir ego network, i.e., CI and InSo.

2) Then, nodes A and B exchange their message list. Foreach message in the list of node B, PIS computesthe similarities between nodes A and D with respectto physical proximity, interests, and social relation-ship, respectively, which are denoted as simProA,simInsA and simSocA.

Fig. 4. The number of broadcast, multicast, and unicast messages in theSIGCOMM2009 data set.

Fig. 5. Cycle and periodic time slots.

XIA ETAL.: PIS: A MULTI-DIMENSIONAL ROUTING PROTOCOL FOR SOCIALLY-AWARE NETWORKING 2829

3) The similarities between nodes B and D are calcu-lated and attached in messages when they received,and denoted as simProB, simInsB, and simSocB.Then, PIS computes the similarity deviation valuesamong nodes A and B including simDevPro,simDevIns, and simDevSoc .

4) Finally, the forwarding utility, denoted as simPIS, iscalculated in order to make the forwarding decision.In the last step, PIS takes advantage of copy controlmechanism in order to control data congestion in adata routing process.

4.1 Information Update

PIS utilizes the three social dimensions and time slotmanagement mechanism to make a forwarding decision.When nodes contact, they exchange their contact list, SI ,and DSo in order to update their ego network information,CI , and InSo.

4.1.1 Physical Proximity Update

In the SAN paradigm, the topology of the network changesdynamically due to the mobility of nodes. Meanwhile, it isdifficult for each node to maintain the information of all theother nodes. Ego network is a popular solution used in theintermittent connection networks such as SAN to tackle thisissue. Nodes construct and maintain their own ego networkinformation according to their contact information. Thestructure of an ego network in PIS is designed using a Egomatrix as follows:

Ego ¼ eA;B if A contacts B0 otherwise;

�

where eA;B denotes the contact time among nodes A and Bin a time slot. If nodes A and B contact each other duringthis period, EgoA;B equals eA;B which indicates the contactfrequency of those nodes. Otherwise, EgoA;B is set to 0.

The contact list of the nodes stores their identity as wellas their contact time in a time slot. When two nodes A and Bmeet each other, they update their contact information andexchange their contact list in order to update the Egomatrix.For example, node A receives the contact list of node Band updates the ego matrix of node A. Algorithm Physical

Proximity Updating presents the updating process of thephysical proximity information.

4.1.2 Interest and Social Relationship Update

Management of user interests and social relationships in PIShas similar procedure, and their updating strategies are alsosimilar. Two encounter nodes A and B exchange values SI

and DSo in order to update CI and InSo. Note that CI andInSo adopts similar structure ðinterest=nodeID; degreeÞ,where degree indicates the contact frequency with this inter-est/node. Therefore, we only describe the updating processof the user interests in Algorithm Interest Updating fromnode A’s perspective.

For each interest SI of node B, the updating modulechecks whether node A includes CI or not. If CI containsthe interest, it means that node A has met some nodes withthe same interests as before. Therefore, her interest’s degreeis added by incrementalValue to represent her meeting fre-quency. Otherwise, it implies that it is the first time thatnode A meets the interest. Next, node A adds the interestand its degree is initialized with initialValue.

4.2 Social Similarity Measurement

PIS compares the similarities between intermediate nodesand destination node on the three social features to choose anappropriate intermediate node. For the social features, PISapplies similar strategies to compute their similarities. Here,we compute the similarity of physical proximity (simPro), asan example, to describe the similarity measurement method.Parameter SimProA indicates the physical proximity similar-ity between nodes A and D in i time slots. The value ofsimProA indicates the contact probability of the nodes.

Algorithm 1. Physical Proximity Updating

1: //In current time slot2: Update contact list3: //Update Ego of node A4: Exchange contact list5: forNC in the contact list of node B do6: //NC is the number of contacts of node B7: //degree is the contact frequency of nodes C and B8: EgoC;B ¼ degree9: EgoB;C ¼ degree10: end for

In each time slot t 2 f0; 1; . . . ; i� 1g, the similaritybetween nodes A and D indicates the number of their com-mon contact nodes, which is denoted as simProt. We aim tofind an intermediate node which meets the destinationnode earlier. Therefore, we assign a parameter b to adjustthe weight of time slots (b < 1). The weight of time slot t is

denoted as btþ1. In this way, the social properties in closertime slot will be highlighted and the optimal forwardingnode will be selected. The value of simPro is quantified byEquation (1). The similarity computing algorithm of physi-cal proximity is illustrated in Algorithm Proximity Similar-ity Calculation.

Both interest and social relationship similarities consist oftwo parts: SI/CI and DSo/InSo. For the interest feature, thesimilarity of SI and CI are computed by Equation (2) and

TABLE 1Explanation of Notations

Notation Explanation

A, B Node A, Node BD Destination nodeSI Self-InterestCI Contact InterestDSo Direct social relationshipInSo Indirect social relationshipsimProA Proximity similarity between nodes A and DsimDevPro Proximity similarity deviationsimInsA Interest similarity between nodes A and DsimDevIns Interest similarity deviationsimSocA social relationship similarity between nodes A and DsimDevSoc social relationship similarity deviationsimPIS Similarity utility function

2830 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 15, NO. 11, NOVEMBER 2016

the final simIns can be obtained according to Equation (3).Similarly, Equations (4) and (5) describe how to calculatesimSoc. In Equations (3) and (5), a is a parameter to adjustthe weight of two parts

simPro ¼Xi�1

t¼0

simProt � btþ1; (1)

simInsfSI; CIg ¼Xi�1

t¼0

simInstfSI; CIg � btþ1; (2)

simIns ¼ a� simInsfSIg þ ð1� aÞ � simInsfCIg; (3)

simSocfDSo; InSog ¼Xi�1

t¼0

simSoctfDSo; InSog � btþ1; (4)

simSoc ¼ a� simSocfDSog þ ð1� aÞ � simSocfInSog: (5)

Algorithm 2. Interest Updating

1: //In current time slot2: //SI is the self-interest of node B3: //insB is the member of SI

4: //CI is the contact interest of node A5: for insB in SI do6: if CI .contains(insB) then7: CI .getValue(InsB)+=incrementalValue8: else9: CI .add(InsB, initialValue)10: end if11: end for

Algorithm 3. Proximity Similarity Calculation

1: t ¼ 0 // In current time slot2: simPro ¼ 03: b ¼ 0:84: for t=0 to i�1 do5: sum ¼ 06: for common nodes between nodes A and B do7: //degree is corresponding value in Ego8: sum ¼ sumþ degree9: end for10: simPro ¼ simProþ sum� b

11: b ¼ b� b

12: end for13: return simPro

4.3 Forwarding Utility Function

According to our previous descriptions, the similaritiesbetween nodes A and D with respect to the three socialdimensions are calculated as simProA , simInsA, andsimSocA. Similarly, simProB , simInsB, and simSocB can becomputed to indicate the similarities between nodes B andD. Accordingly, PIS combines the three dimensions to makethe final forwarding decision.

First, the similarity deviation values from three socialdimensions simDevPro, simDevIns, and simDevScoamong nodes A and B are calculated according to Equa-tions (6), (7), and (8), respectively. Then, the forwarding

utility function simPIS is defined using Equation (9),where rþ s þ t ¼ 1

simDevPro ¼ simProA � simProBsimProA þ simProB

; (6)

simDevIns ¼ simInsA � simInsBsimInsA þ simInsB

; (7)

simDevSco ¼ simScoA � simScoBsimScoA þ simScoB

; (8)

simPIS ¼ r� simDevPro

þ s � simDevIns

þ t � simDevSoc:

(9)

4.4 The Importance of Social Parameters

As introduced in the previous section, values r, s, and t arethe tuning parameters in the PIS protocol that help adjustthe significance of the three social dimensions which arehighly dependent on the routing application. Since routingscenarios in real applications are generally complex anddynamic, identifying the importance of each social featureis a non-trivial problem. If the dominant social factors areknown in a data routing context, users can assign reason-able importance to each tuning parameter to improve itsperformance. For instance, in a campus environment, stu-dents usually have regular mobility pattern and classmateshave similar class schedules during weekdays. In this situa-tion, the social relationships among the users (i.e., class-mates) will be a dominant social feature in the messagedelivery. Hence, the value of parameter t should beincreased. However, in weekends, students usually gettogether for various activities based on their interests. Inthis situation, the user interests (i.e., parameter s) shouldget a higher importance. As a result, parameters r, s and t

can affect a social-based routing protocol in different ways.

4.5 Copy Control Mechanism

The PIS protocol makes use of a copy control mechanism tocontrol data congestion in the network. This idea is borrowedfrom the Spray-and-Wait protocol [37]. When a message isgenerated, an initialization value nofCopy in assigned to themessage to indicate the number of copies.When themessageis forwarded, only half of the current value of nofCopy isdelivered. When all the message copies are disseminatedand the value of nofCopy for a message reaches to 1, the mes-sage can not be forwarded anymore. As a result, some for-warding opportunities might be missed. To tackle thisproblem, PIS introduces a range control parameter denotedas g. In PIS, half of the value nofCopy for a message can beforwarded to other nodes if simPIS þ g > 0, where g isassigned to expand the forwarding range. When a node hasonly one message copy, it forwards the message to anothernode if simPIS > 0. Then, it deletes the message from itsbuffer. Thus, the number of message copies is controlledusing parameter nofCopy.

In summary, the forwarding strategy is outlined asfollows:

1) If node A has more than one message copies andsimPIS þ g > 0 , then node A forwards half

XIA ETAL.: PIS: A MULTI-DIMENSIONAL ROUTING PROTOCOL FOR SOCIALLY-AWARE NETWORKING 2831

number of current copies to node B. g is assigned toprevent messages forwarded too early to limit theforwarding range.

2) When node A has only one message copy andsimPIS > 0, it forwards message to node B anddeletes the message copy from itself.

The pseudo-code of PIS algorithm is presented inAlgorithm 4.

Algorithm 4. PIS Algorithm

1: Nodes A and B contact each other2: Upon reception of Hello message from node B do3: Exchange information of contact list, SI andDSo

4: Update Ego, CI and InSo

5: Exchange message list6: forM in the list of messages in node B do7: Compute simProA , simInsA and simSocA8: Compute simDevPro, simDevIns and9: simDevSco10: Compute simPIS11: if nofCopy of M >1 then12: if simPIS þ g > 0 then13: nofCopy ¼ cofCopy/214: transferMsgs.add(M , connection)15: end if16: else if nofCopy of M==1 then17: if simPIS >0 then18: transferMsgs.add(M , connection)19: deleteMsg from node B when transfer done20: end if21: end if22: end for

5 PERFORMANCE EVALUATION

We compare the performance of the PIS protocol usingOpportunistic Network Environment (ONE) [38] simulatorwhich is a trace driven simulator particularly for intermit-tently connected networks.

5.1 Data Sets

In this section, we evaluate the performance of the PIS pro-tocol using two real traces: SIGCOMM09 [19] and INFO-COM06 [20]. The SIGCOMM09 data set was collected usingan opportunistic mobile social application MobiClique dur-ing SIGCOMM 2009 conference. In this data set, around 100smart phones were distributed among a set of volunteersduring two days. During this period, the traces of Bluetoothproximities, opportunistic message creation and dissemina-tion, and activity period of the participants were recorded.In addition, each participant was asked to log on to theirFacebook profile in order to capture the list of her Facebookfriends and interests. The proximity information of theusers was captured every 120 � 10 seconds.

We also use INFOCOM06 data set which was collectedduring IEEE INFOCOM 2006 conference. In this data set, 78participants were asked to carry iMotes device during theconference in order to collect their opportunistic contactsand mobility statistics. Additionally, some personal infor-mation of the participants such as their interests, lan-guage, and affiliation were also collected through a

survey questionnaire which represented their individualand social attributes.

In above data sets, the physical proximity informationcan be obtained by analyzing the nodes’ contact records.While, the interest information of the nodes are stored in thedata sets directly. For the social relationship information, weuse an explicit social relationship attribute in SIGCOMM09data set which is called friendship. In INFOCOM06 data set,on the other hand, there are several kinds of social relation-ships. For example, if the participants in a conference withthe same language communicate with each other frequently,we use language relationship in this data set to establish thesocial relationships among the nodes.

5.2 Simulation Setup

We compare the effectiveness of PIS with three well-knownrouting protocols, such as Epidemic routing [17], PROPHET[18], and SimBet [13]. In the PROPHET, the next intermedi-ate nodes are selected using their contact history. The Epi-demic routing adopts a simple flooding method in whicheach node copies messages in her buffer to other encoun-tered nodes if they have not received them yet. The SimBetutilizes the betweenness centrality and similarities of nodesto choose the next message carriers based on the ego net-work concept. In the SimBet algorithm, the value of the egomatrix is set to 0 if there is no contact and 1 if there exist acontact. While, in PIS, the value of ego network matrix indi-cates the contact frequency of the nodes.

In the simulations, four performance metrics are evalu-ated as followings:

� Delivery ratio: the ratio of successfully delivered mes-sages to the total number of unique messages createdwithin a given period.

� Overhead ratio: the ratio of relayed messages anddelivered messages, reflecting the ratio of messagereplicas propagated into the network.

� Average latency: the average time between the time amessage is generated and the time it is delivered suc-cessfully, including buffering delays.

� Average hop count: the average hop-counts when mes-sages are received successfully.

We run each simulation setting 30 times and calculatethe average values. The simulation parameters are summa-rized in Table 2.

5.3 Experimental Results and Analysis

5.3.1 Efficiency Comparison

In Figs. 6 and 7, we show the performance of the algorithmsin terms of delivery ratio, overhead ratio, average latency,and average hop count using SIGCOMM09 and INFO-COM06 data sets, respectively.

As it can be seen, PIS outperforms the other three proto-cols which have the highest delivery ratio and the lowestoverhead. For example at time 20 hours in Fig. 6, PISforwards 65.74 percent messages, while the delivery ratioof SimBet, PROPHET, and Epidemic are 27.78, 29.63, and25 percent, respectively. The overhead ratio of PIS is 21.83which is far lower than SimBet with 1,684, PROPHET with4,403, and Epidemic with 7,336. Furthermore, the average

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latency of PIS is 6,943 seconds which is smaller than SimBetwith 8,717 seconds, and higher than PROPHET with 3,948seconds, and Epidemic with 5,296 seconds. The averagehop count of PIS is 1.64 as compared to 2.3 for SimBet , 1.31for PROPHET, and 6.5 for Epidemic .

In INFOCOM06 data set, PIS achieves the best efficiency,as shown in Fig. 7. At 15 hours, the delivery ratio ofPIS reaches 67.53 percent which is 22, 40, and, 40 percenthigher than SimBet, PROPHET, and Epidemic, respec-tively. The overhead ratio of PIS is 23 which is far lowerthan SimBet with 1,409, PROPHET with 5,826, and Epi-demic with 9,030. The average latency of PIS is 5,079 swhich are smaller than SimBet with 5,923 seconds andEpidemic with 9,525 seconds, but longer than PROPHETwith 3,507 seconds. However, the average hop count ofPIS is 1.8 where those for SimBet, PROPHET, and Epi-demic are 1.8, 1.1, and 7.7, respectively.

The main reason that PIS outperforms the other protocolsis that it integrates the three social features with time

regularity to predict the future contacts of the nodes whichare more reasonable and stable to select the appropriateintermediate nodes as forwarders. Additionally, PIS adoptsthe copy control mechanism which decreases the routingoverhead considerably. In PIS protocol, when the utilityfunction simPIS is higher than 0 and the number of mes-sage copies is 1, the message is forwarded from a low-valuenode to a high-value node. At the same time, the value ofmessage copy in the node with the low-value node is resetwhen the message is forwarded successfully. This methodnot only controls the number of message copies but alsoimproves the forwarding opportunities.

5.3.2 The Impacts of Parameters

There are some parameters which affect the efficiency of thePIS protocol considerably. To this end, we conduct a set ofsimulations to explore the impact of these parameters.

1. Parameter g: when two nodes A and B meet each other,their similarities with destination node D with respect tothe three social dimensions are calculated. The limited num-ber of messages copies are generated in order to expand theforwarding opportunities. In PIS, the number of messagecopies is controlled by nofCopy, while the transmissionrange of the message copies is controlled by parameter g. Ifg is set to a small value, the message copies are dissemi-nated even if the similarity gap of nodes A and B is small.As a result, the dissemination of message copies will be fin-ished in a narrow range quickly. Increasing the value of g,the transmission range of message copies is enlarged. Dueto the limited number of message copies, the bigger g mayrestrain the copies dissemination which also decreases thecontact opportunities. Thus, an appropriate value for g

should be selected. We note that in different data sets, theinfluence of this value can be different according to thenodes’ contact frequency.

We compare the performance of the protocols with dif-ferent g values on the data sets. In SIGCOMM09, g isassigned as 0.2, 0.4, 0.6, and 0.8. While in INFOCOM06, thevalue of g is set 0.1, 0.2, 0.4, and 0.6. Figs. 8 and 9 show thecomparison results, respectively.

TABLE 2Simulation Parameters

Simulation Parameters Values

Duration period 40 hoursWarm up 5,000 (second)Nodes’ speed 0.5�1.5 (m/s)Wait time at destination 100�200 (second)Interface Type BluetoothTransmit speed 250 KBTransmit range 10 meterMoment model External MovementData set SIGCOMM09, INFOCOM06Event interval 500�650Message size 500�1,024 (MB)Message TTL 10 (hour)Nodes’ buffer 5 (MB)time slot parameter i 6a 0.5b 0.8g 0.8, 0.1r, s and t 1/3, 1/3, and 1/3

Fig. 6. The comparison of PIS with the other routing algorithms using SIGCOMM09 data set.

Fig. 7. Efficiency comparison of PIS with the other routing algorithms using INFOCOM06 data set.

XIA ETAL.: PIS: A MULTI-DIMENSIONAL ROUTING PROTOCOL FOR SOCIALLY-AWARE NETWORKING 2833

In Fig. 8, as the value of g increases, the performance ofPIS is improved using SIGCOMM09 data set. However, asshown in Fig. 9, PIS achieves the highest performance inINFOCOM06 when g is set to 0.1. With the increasing ofvalue g, the performance of PIS declines. Thus, we chooseg 0.8 in SIGCOMM09 and 0.1 in INFOCOM06.

2. Message event interval: Figs. 10 and 11 show the influ-ence of message event interval on the performance metrics.

Five values are set for message event interval which are 100� 250, 200 � 350, 300 � 450, 400 � 550, and 500 � 650. Fromthis figure, as the number of messages increases, the deliv-ery ratio increases and the average latency prolongs. More-over, the time interval 500 � 650 gets the relative higherefficiency due to its lower overhead ratio.

3. Time slot parameter i: the similarity of two encounternodes with respect to the three features is the basic

Fig. 8. Simulation results for PIS under different g using SIGCOMM09 data set.

Fig. 9. Simulation results for PIS under different g using INFOCOM06 data set.

Fig. 10. Simulation results for PIS under different message intervals using SIGCOMM09 data set.

Fig. 11. Simulation results for PIS under different message intervals using INFOCOM06 data set.

Fig. 12. Simulation results for PIS under different time slot parameter i using SIGCOMM09 data set.

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forwarding principle in PIS protocol. PIS adopts the timeslot mechanism to manage the different time slots. In thesimulations, the time slot is set to 1 hour. Time slot parame-ter i identifies the number of time slots participated in asimilarity comparison. In order to analyze the impact ofparameter i on PIS, we assign four values to iwhich are 1, 3,6, 8. Figs. 12 and 13 show the simulation results. As it can beseen from Fig. 12, i ¼ 6 and i ¼ 1 obtain higher performancewith the SIGCOMM09 data set. In Fig. 13, PIS achieves thehighest efficiency when i ¼ 6with the highest delivery ratio,lowest overhead, shortest average latency, and fewer aver-age hop count with the INFOCOM06 data set.

6 CONCLUSION

The social characteristics and behaviors of mobile usershave been extensively utilized in the literature to improvethe performance of routing protocols in SAN paradigm. Inthis paper, we proposed a multi-dimensional routing proto-col, called PIS, which combines three social features ofmobile users with their time regularity in order to design astable and adaptive forwarding scheme. Our simulationexperiments using two real-world mobility data sets haveshown that PIS outperforms other benchmark routing pro-tocols (e.g., SimBet, PROPHET, and Epidemic) in terms ofdata delivery ratio, network overhead, and latency. As partof our future work, we plan to explore the performanceof PIS protocol in large-scale networks.

ACKNOWLEDGMENTS

The authors would like to thank the anonymous reviewersfor constructive comments and suggestions which helpedimproved the quality of the manuscript significantly. Thiswork is partially supported by the Fundamental ResearchFunds for the Central Universities (DUT15YQ112), theNational Natural Science Foundation of China (61572106),and the US National Science Foundation grants CNS-1355505 and CCF-1539318.

REFERENCES

[1] F. Xia, L. Liu, J. Li, J. Ma, and A. V. Vasilakos, “Socially-aware net-working:A survey,” IEEESyst. J., vol. 9, no. 3, pp. 904–921, Sep. 2015.

[2] M. Khabbaz, C. M. Assi, and W. Fawaz, “Disruption-tolerant net-working: A comprehensive survey on recent developments andpersisting challenges,” IEEE Commun. Surveys Tuts., vol. 14, no. 2,pp. 607–640, 2nd Quarter 2012.

[3] M. Conti, S. Giordano, M. May, and A. Passarella, “From opportu-nistic networks to opportunistic computing,” IEEE Commun. Mag.,vol. 48, no. 9, pp. 126–139, Sep. 2010.

[4] J. N. G. Isento, J. J. P. C. Rodrigues, J. A. F. F. Dias, M. C. G. Paula,and A. Vinel, “Vehicular delay-tolerant networks? A novel solu-tion for vehicular communications,” IEEE Intell. Transp. Syst.Mag., vol. 5, no. 4, pp. 10–19, Winter 2013.

[5] P. Pirozmand, G. Wu, B. Jedari, and F. Xia, “Human mobility inopportunistic networks: Characteristics, models and predictionmethods,” J. Netw. Comput. Appl., vol. 42, pp. 45–58, 2014.

[6] C. Song, Z. Qu, N. Blumm, and A.-L. Barab�asi, “Limits of predict-ability in human mobility,” Science, vol. 327, no. 5968, pp. 1018–1021, 2010.

[7] X. Lu, E. Wette, N. Bharti, A. J. Tatem, and L. Bengtsson,“Approaching the limit of predictability in human mobility,”Nature Sci. Rep., vol. 3, p. 2923, 2013.

[8] Y. Zhu, B. Xu, X. Shi, and Y. Wang, “A survey of social-based rout-ing in delay tolerant networks: Positive and negative socialeffects,” IEEE Commun. Surv. Tuts., vol. 15, no. 1, pp. 387–401, 1stQuarter 2013.

[9] H. Pan, J. Crowcroft, and E. Yoneki, “Bubble rap: Social-based for-warding in delay-tolerant networks,” IEEE Trans. Mobile Comput.,vol. 10, no. 11, pp. 1576–1589, Nov. 2011.

[10] K. Zhu, W. Li, and X. Fu, “SMART: A social- and mobile-awarerouting strategy for disruption-tolerant networks,” IEEE Trans.Veh. Technol., vol. 63, no. 7, pp. 3423–3434, Sep. 2014.

[11] J. Wu and Y. Wang, “Hypercube-based multi-path social featurerouting in human contact networks,” IEEE Trans. Comput., vol. 63,no. 2, pp. 383–396, Feb. 2014.

[12] M. Xiao, J. Wu, and L. Huang, “Community-aware opportunisticrouting in mobile social networks,” IEEE Trans. Comput., vol. 63,no. 7, pp. 1682–1695, Jul. 2014.

[13] E.Daly andM.Haahr, “Social network analysis for routing in discon-nected delay-tolerant MANETs,” in Proc. 8th ACM Int. Symp. MobileAdHocNetw. Comput., Montreal, QC, Canada, Sep. 2007, pp. 32–40.

[14] E. M. Daly and M. Haahr, “Social network analysis for informa-tion flow in disconnected delay-tolerant MANETs,” IEEE Trans.Mobile Comput., vol. 8, no. 5, pp. 606–621, May 2009.

[15] M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of afeather: Homophily in social networks,” Annu. Rev. Sociol., vol. 27,no. 1, pp. 415–444, 2001.

[16] E. Bulut and B. K. Szymanski, “Friendship based routing in delaytolerant mobile social networks,” in Proc. IEEE Global Telecommun.Conf., Miami, FL, USA, Dec. 2010, pp. 1–5.

[17] A. Vahdat and D. Becker, “Epidemic routing for partially con-nected ad hoc networks,” Duke Univ., Durham, NC, USA, Tech.Rep. CS-200006, , 2000.

[18] A. Lindgren, A. Doria, and O. Schel�en, “Probabilistic routing inintermittently connected networks,” ACM SIGMOBILE Comput.Commun. Rev., vol. 7, no. 3, pp. 19–20, Jul. 2003.

[19] A.-K. Pietil€ainen, E. Oliver, J. LeBrun, G. Varghese, and C. Diot,“MobiClique: Middleware for mobile social networking,” in Proc.2nd ACM Workshop Online Social Netw., Barcelona, Spain, Aug.2009, pp. 49–54.

[20] J. Scott, R. Gass, J. Crowcroft, P. Hui, C. Diot, and A. Chaintreau.(2009). Crawdad trace cambridge/haggle/imote/infocom2006(v.2009-05-29). Downloaded from http://crawdad. cs. dartmouth.edu/cambridge/haggle/imote/infocom2006

[21] F. Li and J. Wu, “LocalCom: A community-based epidemic for-warding scheme in disruption-tolerant networks,” in Proc. 6thAnnu. IEEE Conf. Sensor, Mesh Ad Hoc Commun. Netw., Rome,Italy,Jun. 2009, pp. 1–9.

[22] A. Mei, G. Morabito, P. Santi, and J. Stefa, “Social-aware statelessroutingin pocket switched networks,” IEEE Trans. Parallel Distrib.Syst., vol. 26, no. 1, pp. 252–261, Jan. 2015.

[23] K. Chen, H. Shen, and H. Zhang, “Leveraging social networks forp2p content-based file sharing in disconnected manets,” IEEETrans. Mobile Comput., vol. 13, no. 2, pp. 235–249, Feb. 2014.

[24] W. Gao and G. Cao, “User-centric data dissemination in disrup-tion tolerant networks,” in Proc. IEEE Int. Conf. Comput. Commun.,Apr. 2011, pp. 3119–3127.

Fig. 13. Simulation results for PIS under different time slot parameter i using INFOCOM06 data set.

XIA ETAL.: PIS: A MULTI-DIMENSIONAL ROUTING PROTOCOL FOR SOCIALLY-AWARE NETWORKING 2835

[25] P. Costa, C. Mascolo, M. Musolesi, and G. P. Picco, “Socially-aware routing for publish-subscribe in delay-tolerant mobile adhoc networks,” IEEE J. Sel. Areas Commun., vol. 26, no. 5, pp. 748–760, Jun. 2008.

[26] F. Xia, L. Liu, J. Li, A. M. Ahmed, L. T. Yang, and J. Ma,“BEEINFO: Interest-based forwarding using artificial bee colonyfor socially aware networking,” IEEE Trans. Veh. Technol., vol. 64,no. 3, pp. 1188–1200, Mar. 2015.

[27] A. J. Mashhadi, S. B. Mokhtar, and L. Capra, “Habit: Leveraginghuman mobility and social network for efficient content dissemi-nation in delay tolerant networks,” in Proc. IEEE Int. Symp. WorldWireless, Mobile Multimedia Netw. Workshops, Kos, Greece,Jun. 2009, pp. 1–6.

[28] B. Chiara, C. Marco, and P. Andrea, “Exploiting users’ social rela-tions to forward data in opportunistic networks: The hibop sol-ution,” Pervasive Mobile Comput., vol. 4, no. 5, pp. 633–657,Oct. 2008.

[29] Z. Li and H. Shen, “Sedum: Exploiting social networks in utility-based distributed routing for DTNs,” IEEE Trans. Comput., vol. 62,no. 1, pp. 83–97, Jan. 2013.

[30] T. Zhou, H. Xu, and Y. Chen, “Spatial and temporal communities-based routing algorithm in human social network,” J. Comput. Inf.Syst., vol. 8, no. 18, pp. 7823–7830, Oct. 2012.

[31] H. A. Nguyen and S. Giordano, “Context information predictionfor social-based routing in opportunistic networks,” Ad HocNetw., vol. 10, no. 8, pp. 1557–1569, Nov. 2012.

[32] L. Gao, M. Li, A. Bonti, W. Zhou, and S. Yu, “Multidimensionalrouting protocol in human-associated delay-tolerant networks,”IEEE Trans. Mobile Comput., vol. 12, no. 11, pp. 2132–2144,Nov. 2013.

[33] A. Socievole, E. Yoneki, F. D. Rango, and J. Crowcroft,“Opportunistic message routing using multi-layer socialnetworks,” in Proc. 2nd ACMWorkshop High Perform. Mobile Oppor-tunistic Syst., 2013, pp. 39–46.

[34] W. Gao, G. Cao, T. L. Porta, and J. Han, “On exploiting transientsocial contact patterns for data forwarding in delay-tolerantnetworks,” IEEE Trans. Mobile Comput., vol. 12, no. 1, pp. 151–165,Jan. 2013.

[35] S. Schonfelder and U. Samaga, “Where do you want to go today?:More observations on daily mobility,” in Proc. 3rd Swiss TransportRes. Conf., 2003, p. 31.

[36] A. Fast, D. Jensen, and B. N. Levine, “Creating social networks toimprove peer-to-peer networking,” in Proc. 11th ACM SIGKDDInt. Conf. Knowl. Discovery Data Mining, Chicago, IL, USA,Aug. 2005, pp. 568–573.

[37] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Spray andwait: An efficient routing scheme for intermittently connectedmobile networks,” in Proc. ACM SIGCOMMWorkshop Delay-Toler-ant Netw., Philadelphia, PA, USA, Aug. 2005, pp. 252–259.

[38] A. Ker€anen, T. K€arkk€ainen, and J. Ott, “Simulating mobility andDTNs with the one,” J. Commun., vol. 5, no. 2, pp. 92–105,Feb. 2010.

Feng Xia (M’07-SM’12) received the BSc andPhD degrees from Zhejiang University, Hang-zhou, China. He was a research fellow atQueensland University of Technology, Australia.He is currently a full professor in the Schoolof Software, Dalian University of Technology,China. He is the (Guest) editor of several interna-tional journals. He serves as a general chair, PCchair, workshop chair, or publicity chair of a num-ber of conferences. He has published two booksand over 190 scientific papers in international

journals and conferences. His research interests include social comput-ing, computational social science, big data, and mobile social networks.He is a senior member of the IEEE and ACM.

Li Liu received the BS and MS degrees in com-puter science and technology from ShandongUniversity of Science and Technology, Qingdao,China, in 2001 and 2004, respectively, and thePhD degree from Dalian University of Technology,China, in 2015. She has been at Shandong Jiao-tong University, Jinan, China, since 2004. She iscurrently a postdoctoral fellow at Dalian Univer-sity of Technology. Her research interests includeopportunistic networks, socially aware network-ing, and mobile social networks.

Behrouz Jedari received the BSc and MScdegrees from the Islamic Azad University,Qazvin, Iran, in 2006 and 2009, respectively. Heis currently working toward the PhD degree in theSchool of Software, Dalian University of Technol-ogy, Dalian, China. His current research interestsinclude delay tolerant networks and social net-work analysis with respect to data communicationand protocol design for new and emerging areassuch as mobile social networks.

Sajal K. Das (F’15) received the BTech degreefrom Calcutta University, Kolkata, India, in 1983,the MS degree from Indian Institute of Science,Bangalore, India, in 1984, and the PhD degreefrom the University of Central Florida, Orlando, in1988. He is the chair in the Computer ScienceDepartment and Daniel St. Clair Endowed chairin Computer Science at the Missouri University ofScience and Technology, Rolla. His researchinterests include theory and practice of wirelessand sensor networks, mobile and pervasive com-

puting, cyberphysical systems, and smart environments. He has pub-lished more than 600 articles in high-quality journals and refereedconference proceedings, 51 invited book chapters, and coauthored fourbooks. He is a fellow of the IEEE.

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