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Please cite this article in press as: A. Rahim, et al., Social acquaintance based routing in Vehicular Social Networks, Future Generation Computer Systems (2017), http://dx.doi.org/10.1016/j.future.2017.07.059. Future Generation Computer Systems ( ) Contents lists available at ScienceDirect Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs Social acquaintance based routing in Vehicular Social Networks Azizur Rahim a , Tie Qiu a, *, Zhaolong Ning a , Jinzhong Wang a, b , Noor Ullah a , Amr Tolba c , d , Feng Xia a a School of Software, Dalian University of Technology, Dalian 116620, China b Department of Sport Information Technology, Shenyang Sport University, Shenyang 110102, China c Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia d Mathematics Department, Faculty of Science, Menoufia University, Shebin El-Kom 32511, Egypt highlights This paper presents a novel protocol based on social acquaintance for data forwarding in VSNs. Social feature metrics are considered for decision-making. Reduced End-to-End delay and improve packet delivery ratio. Simulations were performed to evaluate the proposed protocol under different scenarios. article info Article history: Received 16 March 2017 Received in revised form 3 July 2017 Accepted 23 July 2017 Available online xxxx Keywords: Vehicular Social Networks Cyber-Physical systems Internet of Things Intelligent transport systems Communication architecture Reliable data delivery abstract The concept of Internet of Things (IoT) provides us the opportunity to interconnect different objects with the communication and processing capabilities for a diverse range of applications. Recently, Vehicular Social Networks (VSNs) have been introduced through the combination of relevant concepts from two primary disciplines, i.e., social networks and Vehicular Ad hoc Networks (VANETs). Inspired from the social acquaintance in our daily life, we present a Social Acquaintance based Routing Protocol (SARP) for VSNs, which collectively consider three social feature metrics to make a forwarding decision. Proposed protocol aims to reduce End-to-End delay and improve packet delivery ratio in VSNs. Additionally, SARP overcomes the shortcoming of topology based routing and optimum local situation of geographically based routing protocols by considering the global and local community acquaintance of nodes. We performed extensive simulations under constant node density with different mobility speed and constant speed with varying node density to study the effect of node mobility speed and density on end-to-end delay and packet delivery ratio. The simulation results show that SARP outperforms GPSR by 22% and 26% in terms of end-to-end delay and packet delivery ratio respectively. Also, SARP outperforms AOVD in terms of end-to-end delay. © 2017 Elsevier B.V. All rights reserved. 1. Introduction Vehicular Ad hoc Network (VANET) is a unique form of Ad hoc networks, created by vehicles with restricted mobility on roads and streets with data processing and wireless communica- tion capabilities. These vehicles either communicate directly or through Road Side Units (RSUs) utilizing different communication infrastructures, including 3G/4G, WiFi, etc. Network resources are used to access and obtain data from other networks. VANETs have the capabilities to be deployed in different operational environ- ments for a diverse range of applications. In other words, VANETs are sparse Ad hoc networks formed by vehicles to communicate * Corresponding author. E-mail address: [email protected] (T. Qiu). opportunistically on contact. Due to its easy to deploy nature and diverse possible range of applications in a pervasive environment, VANETs have attracted the attention of not only academicians but also from industrial leaders. Typical applications of VANETs include intelligent traffic control systems, collision warnings, active navi- gation systems, and passenger entertainment/comfort services. Quite recently, the research community in the field of com- munication and technology has been greatly attracted by Social Network Analysis (SNA) and its applications to design new algo- rithms and protocols for socially aware networking, such as Mobile Social Networks (MSNs), Vehicular Social Networks (VSNs), and Internet of Things (IoT). The motivation behind the inheritance of social networks in communication networks is that all entities have interdependencies which relate them to each other in one http://dx.doi.org/10.1016/j.future.2017.07.059 0167-739X/© 2017 Elsevier B.V. All rights reserved.

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Page 1: Social acquaintance based routing in Vehicular Social Networksthealphalab.org/papers/Social Acquaintance Based...Pleasecitethisarticleinpressas:A.Rahim,etal.,SocialacquaintancebasedroutinginVehicularSocialNetworks,FutureGenerationComputerSystems(2017),

Please cite this article in press as: A. Rahim, et al., Social acquaintance based routing in Vehicular Social Networks, Future Generation Computer Systems (2017),http://dx.doi.org/10.1016/j.future.2017.07.059.

Future Generation Computer Systems ( ) –

Contents lists available at ScienceDirect

Future Generation Computer Systems

journal homepage: www.elsevier.com/locate/fgcs

Social acquaintance based routing in Vehicular Social NetworksAzizur Rahim a, Tie Qiu a,*, Zhaolong Ning a, Jinzhong Wang a,b, Noor Ullah a, Amr Tolba c,d,Feng Xia a

a School of Software, Dalian University of Technology, Dalian 116620, Chinab Department of Sport Information Technology, Shenyang Sport University, Shenyang 110102, Chinac Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabiad Mathematics Department, Faculty of Science, Menoufia University, Shebin El-Kom 32511, Egypt

h i g h l i g h t s

• This paper presents a novel protocol based on social acquaintance for data forwarding in VSNs.• Social feature metrics are considered for decision-making.• Reduced End-to-End delay and improve packet delivery ratio.• Simulations were performed to evaluate the proposed protocol under different scenarios.

a r t i c l e i n f o

Article history:Received 16 March 2017Received in revised form 3 July 2017Accepted 23 July 2017Available online xxxx

Keywords:Vehicular Social NetworksCyber-Physical systemsInternet of ThingsIntelligent transport systemsCommunication architectureReliable data delivery

a b s t r a c t

The concept of Internet of Things (IoT) provides us the opportunity to interconnect different objects withthe communication and processing capabilities for a diverse range of applications. Recently, VehicularSocial Networks (VSNs) have been introduced through the combination of relevant concepts from twoprimary disciplines, i.e., social networks and Vehicular Ad hoc Networks (VANETs). Inspired from thesocial acquaintance in our daily life, we present a Social Acquaintance based Routing Protocol (SARP) forVSNs, which collectively consider three social feature metrics to make a forwarding decision. Proposedprotocol aims to reduce End-to-End delay and improve packet delivery ratio in VSNs. Additionally, SARPovercomes the shortcoming of topology based routing and optimum local situation of geographicallybased routing protocols by considering the global and local community acquaintance of nodes. Weperformed extensive simulations under constant node density with differentmobility speed and constantspeed with varying node density to study the effect of node mobility speed and density on end-to-enddelay and packet delivery ratio. The simulation results show that SARP outperforms GPSR by 22% and26% in terms of end-to-end delay and packet delivery ratio respectively. Also, SARP outperforms AOVD interms of end-to-end delay.

© 2017 Elsevier B.V. All rights reserved.

1. Introduction

Vehicular Ad hoc Network (VANET) is a unique form of Adhoc networks, created by vehicles with restricted mobility onroads and streets with data processing and wireless communica-tion capabilities. These vehicles either communicate directly orthrough Road Side Units (RSUs) utilizing different communicationinfrastructures, including 3G/4G, WiFi, etc. Network resources areused to access and obtain data from other networks. VANETs havethe capabilities to be deployed in different operational environ-ments for a diverse range of applications. In other words, VANETsare sparse Ad hoc networks formed by vehicles to communicate

* Corresponding author.E-mail address: [email protected] (T. Qiu).

opportunistically on contact. Due to its easy to deploy nature anddiverse possible range of applications in a pervasive environment,VANETs have attracted the attention of not only academicians butalso from industrial leaders. Typical applications of VANETs includeintelligent traffic control systems, collision warnings, active navi-gation systems, and passenger entertainment/comfort services.

Quite recently, the research community in the field of com-munication and technology has been greatly attracted by SocialNetwork Analysis (SNA) and its applications to design new algo-rithms and protocols for socially aware networking, such asMobileSocial Networks (MSNs), Vehicular Social Networks (VSNs), andInternet of Things (IoT). The motivation behind the inheritanceof social networks in communication networks is that all entitieshave interdependencies which relate them to each other in one

http://dx.doi.org/10.1016/j.future.2017.07.0590167-739X/© 2017 Elsevier B.V. All rights reserved.

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Please cite this article in press as: A. Rahim, et al., Social acquaintance based routing in Vehicular Social Networks, Future Generation Computer Systems (2017),http://dx.doi.org/10.1016/j.future.2017.07.059.

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way or another. These interdependencies include physical con-tact, mutual interest, similarity, group participation, and muchmore. SNA not only helps us to define these interdependenciesbut can also be exploited to correlate them. Social networking isthe grouping of entities based on their social interdependencies toimprove the effectiveness and efficiency of network services [1,2].Some algorithms and protocols have been proposed so far, basedon the social interaction of entities (nodes, devices, people, andsystems), which has shown that social interactions of nodes canbe exploited to achieve high efficiency and effectiveness withinmobile communication systems [3].

The inheritance of social networks has a lot of potentials whichcan be exploited in a vehicular environment. Vehicular mobilityand density are two of the main factors that significantly influ-ence the communication in the vehicular environment. Mobilitydirections and speed limitations on public roads restrict vehicularmobility. However, vehicular density is affected by the number ofvehiclesmoving on a specific routewithin a particular period of theday/week. During rush hour, traffic jams result in higher vehiculardensity. Density variations during different times of day/weekcharacterize vehicular dynamic network topology, making datasharing and communication a challenging task in the vehicularenvironment. Besides, vehicular mobility depends on drivers’ in-terests, behaviors, and routine. For examples, on weekdays, thecommuters often repeat the same path at the same time to andfrom the same destinations, such as work offices, universities,schools, etc. Similarly, different destinations, including parks, cin-emas, shopping malls, etc., are the most visited destinations forcommuters onweekends. These vehicles are controlled by humansexhibiting some social behaviors. However, unlike Mobile SocialNetworks (MSNs), vehicular mobility is restricted to roads andstreets. These vehicles communicate on opportunistic encounterwithin each other’s communication range.

In a vehicular environment, commuters encounter other vehi-cles/passengers on their trajectories with a similar profile movingon the same street and facing the same traffic conditions. Thesecommuters can share valuable information along the roads andmay include traffic information, personal information, and vehicleinformation. Socially-aware techniques are applied to exploit thesocial similarity of nodes to improve data delivery services andconnectivity in a vehicular environment. Inheritance of socialnetworks into vehicular networks has been identified as one ofthe most efficient solutions for a diverse range of applications.These applications can be mainly divided into four categories; (a)Safety-based applications, (b) Convenience-based Applications, (c)Comfort-based applications, and (d) Entertainment-based applica-tions.

After homes and offices, regular citizens spend considerabletime in vehicles based on their daily schedules. Different fromMSNs, where human beings interact with each other using theirsmart devices, network entities in VSNs are heterogeneous in-cluding OBUs, RSUs, as well as drivers’ and passengers’ smartdevices. As shown in Fig. 1, VSNs incorporate relevant features andconcepts from two different fields namely VANETs and social net-works. VANETs provide the underlying communication networkinfrastructure, whereas social networks contribute to the socialknowledge of entities. VSNs can be deployed in different scenariossuch as urban and highway, where it can be either centralizedor distributed in nature. Based on the underlying communicationarchitecture, three types of communication relations are found inVSNs: human-to-human, humans andmachines, andmachines-to-machines. Similarly, unique characteristics of VSNs and particularapplications environment distinguish VSNs from traditional MSNsand VANETs.

Recently, many efforts have been made to propose different al-gorithms and architectures that incorporate the concepts of social

networks to support a variety of mobile social applications [4–7].However, these solutions have mainly focused on Delay TolerantNetworks (DTNs) andMSNs. Quite recently, a fewworks addressedthe incorporation of social networks in vehicular networks [8–16].It has been demonstrated that the incorporation of social networksinto vehicular networks positively influence the services and com-munication. However, some technical challenges arise that needto be addressed. Dynamic network topology and high vehicularmobilitymake application development inVSNs a challenging task.Similarly, opportunistic and short-term communication contactsin VSNs demand for efforts to develop new algorithms to accom-modate different applications. Data dissemination, routing, mo-bility modeling, simulation, privacy, and security are other issueswhich need to be considered.

Social relationships are relatively stronger and stable as com-pared to communication links between mobile nodes, which canbe exploited to enhance data transmission [17]. However, somequestions arise considering the vehicular environment. Does it hassocial properties? Is there any permanence of social behaviors invehicular mobility? How effective is to examine social behaviorin the vehicular environment? Some works exist in literature toanswer these questions [18–20]. Authors in [19] analyzed realdata sets and found that vehicular mobility shows small worldphenomenon. Similarly, the study also indicates the existence ofcommunities with similar interests. Social ties and social commu-nity are widely used concepts to enhance data dissemination insocially-aware networks. Community members are expected tocommunicate more frequently as compared to other nodes out ofthe same community. However, for inter-community communica-tion, global knowledge of popularity of nodes is required. Similarly,for intra-community data dissemination and forwarding, social tiescan be exploited to enhance data delivery.

Improving QoS in VSNs is one of the challenging tasks dueto higher mobility and dynamic network topology. Informationsharing and prediction based algorithms in VSNs is vulnerable topacket drop and privacy attacks. Similarly, resources utilizationand traffic congestion are also the key factors to consider whiledeveloping algorithms/protocols for VSNs. In short, to overcomethese challenges efforts are needed with appropriate mechanismto deal with highly dynamic nature of VSNs and intermittent con-nectivity to enhance data delivery.

In this paper, we consider social characteristics of nodes inthe vehicular environment and propose a novel protocol for datadissemination in VSNs named as SARP (Social-Acquaintance basedRouting Protocol). This protocol exploits the social metrics in afully distributed manner using characteristics of Ad hoc and delaytolerant networks. Thisworkdiffers fromexistingworks as follows.First, community acquaintance is defined to quantify the globaland local importance of intermediate nodes. Second, communityacquaintance is jointly considered with node centrality and ac-tiveness with minimum possible overhead to select the next relaynode. Third, we propose a new protocol (SARP) to enhance datadelivery in VSNs with minimized end-to-end delay and improvedpacket delivery ratio. Finally, extensive simulations are performedto evaluate the performance of proposed protocol in VSNs.

The rest of the paper is organized as follows. Related work onsocially-aware routing and dissemination protocols are presentedin Section 2 with motivation for our proposed model. In Section 3,we provide an overview of proposed protocol and social featureswhich are considered to develop the forwarding decision. Sec-tion 4 describes the simulation setup. Before concluding remarksin Section 6 with future work, simulation results are presented inSection 5.

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Please cite this article in press as: A. Rahim, et al., Social acquaintance based routing in Vehicular Social Networks, Future Generation Computer Systems (2017),http://dx.doi.org/10.1016/j.future.2017.07.059.

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Fig. 1. Vehicular social networks.

2. Related works and motivation

Data dissemination in VSNs is one of the main challenges dueto highly dynamic nature and intermittent connectivity in thevehicular environment. Unlike wireless sensor networks, wheresensor nodes directly transmit sensed data to the base station [21],in VSNs, an end-to-end path does not exist, and data is forwardedin store-carry-and-forward fashion. Different features and param-eters may be considered to enhance data dissemination in VSNs.In [22] authors have profoundly studied different data dissemina-tion approaches in VSNs. In the study, it is shown that social behav-iors and mobility pattern of nodes1 are being exploited to designcontent dissemination protocols. Similarly, inextricable propertiesof mobiles devices and their users (i.e., their mobility patterns, in-terdependent social behaviors) are being utilized in other commu-nication networks, such as Pocket Switched Networks (PSNs) [23],Delay Tolerant Networks (DTNs) [24], Socially Aware networking(SAN) [25], and Opportunistic Networks (OppNets) [26].

SAN provides a ground to inherit social properties of nodesinto the vehicular environment, where a group of individualshaving common interests may share information along the roads.Socially based protocols help to identify socially-similar nodesbased on common interest, community affiliation, similar route,and destination. Stability of social ties and less frequent variationin social relationships are the key initiatives to inherit social prop-erties in the vehicular environment to enhance data dissemination.Recently, nodes’ social properties are extensively analyzed andconsidered by the research community to design new routingprotocols for socially-aware networks [25,27–29].

Quite recently, Xia et al. in [25] have presented a novel interest-based forwarding scheme for socially aware networks. The pro-posed protocol, BEEINFO (Artificial BEE Colony inspired INterest-based FOrwarding), exploits food foraging behavior of bees torecord information of different communities passing through.Bees’ awareness capability has been introduced in VSNs consider-ing three distinct areas, i.e., shopping mall, hospital, and school.Vehicles along their routes collect community information andestimate community density. Community density is defined asthe number of nodes in a particular community. Individuals withsimilar and shared interests build communities, and it is under-stood that members of the same community meet more oftenthan members out of the community. LABEL [30] is one of thewell-known routing protocols based on community concepts to

1 With the term ‘‘nodes’’ we mean vehicles and human beings participating indata dissemination in VSNs

delivermessages only to themember nodes of destination commu-nity. Similarly, following the same idea, BUBBLE RAP [31] enhancerouting performance considering central nodes with communitydetection, resulting in high cost for maintenance and constructionof socio-aware overlay.

To reduce end-to-end delay and achieve higher delivery ratiois a challenging goal to be achieved in VSNs. Gu et al. in [32]proposed a socially-aware routing protocol with fuzzy logic toachieve this aim. This fuzzy logic algorithm not only depends ontraditional greedy approach but also exploits the social behaviorsof nodes, i.e., centrality. Cunha et al. in [33] considered the dailyvariation of traffic flow and social ties among vehicles to proposea data dissemination protocols in vehicular networks. The socialmetrics considered by authors include clustering coefficient andnode degree to select the best node for data dissemination in thevehicular environment. Besides, some other works in the liter-ature [34–39] combine community awareness with other socialmetrics, i.e., similarity, node centrality, and betweenness to en-hance inter-community and intra-community data delivery. Theseprotocols consider historical encounter of nodes to predict thecontact probability for data forwarding.

Apart from social network analysis, based on underlying com-munication architecture of VSNs, the traditional performancemet-rics including delivery ratio, bandwidth usage, and data deliverydelay affect the QoS of VSNs. However, some applications, i.e., traf-fic, safety, and emergency information dissemination require shortdata delivery delay. On the other side, some applications demandhigher data delivery as compared to reduced end to end delaysuch as entertainment applications. However, exploring the effi-cient modeling of socially aware metrics in VSNs is a challengingtask. Efforts are still needed to exploit application-oriented socialmetrics to design data dissemination protocols for VSNs.

3. Overview of SARP

Reliable and efficient data dissemination in the vehicular en-vironment is one of the most extensively studied issues in litera-ture. Higher mobility and dynamic network topology are the keyfactors making data dissemination in the vehicular environmentas one of the challenging tasks and have attracted the researchcommunity. Traditional routing protocols for VANETs, MANETs,and other large-scale networks often use the geographical infor-mation or network topology information for making a routingdecision [40,41]. Routing protocols based on network topologycannot be applied to VSNs due to its dynamic network topology.

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Please cite this article in press as: A. Rahim, et al., Social acquaintance based routing in Vehicular Social Networks, Future Generation Computer Systems (2017),http://dx.doi.org/10.1016/j.future.2017.07.059.

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Similarly, routing protocols based on geographical informationcan be helpful in highway scenarios but might end up in localoptimum in urban areas. On the other hand, the social relation-ships among nodes in the vehicular environment are relativelystronger and stable as compared to node mobility. In this paper,we propose a multi-dimensional routing protocol, which exploitsthe nodes’ social characteristics such as community acquaintance,node centrality, and activeness to route amessage from a source todestination in VSNs. Nodes belong to different communities basedon their interest and geographical location. Compared to existingsocial-aware routing protocols, SARP not only considers the cur-rent values of parameters but also examines the historical valuesfor decision making which makes it more flexible and reliable.Similarly, global importance of relay nodes is considered to over-come the local optimum problem of geographically based routingprotocols.

We consider an application scenario for information sharingand develop amechanism to calculate a priority value of next relaynode. The message is forwarded in store-and-carry fashion from asource node to the destination node within the same or differentcommunity. People with the same interests or from the same com-munity usually meet more often and have the greater probabilitytomeet in future as compared to othermembers of communities orwith different interests.We consider three research groups (GroupA, Group B, and Group C) with different research direction withmutual collaboration, where the members of the same researchgroup have greater interaction and probability to meet as com-pared to members of other research groups. If a member of GroupA wants to deliver a file to someone in the same research group,he/she may directly hand over the desired file personally or candeliver the file to a personwhohas greater community acquaintanceas compared to him/her.Why do not we consider node centrality todeliver themessage/file asmost of the previous work do? A personor node can have higher node centrality, but it may not be sufficientto deliver a message to the destination node as compared to theone who has greater interaction within the same community atthe same time. Similarly, does it guaranty that a message will bedelivered to the destination for sure by choosing a relay node withhigh community acquaintance and node centrality? To increase theprobability of message delivery, we also consider node activeness.Consequently, a person who has higher community acquaintance,the high degree of centrality andmost active in the community canbe themost suitable relay candidate to deliver amessage. Similarly,a person who is active and has more connection within Group Abut no or less acquaintance with members of Group B or Group Ccannot be a suitable relay for information delivery fromGroup A toGroup B or Group C. The global community acquaintance is requiredto route a message from source to destination community duringinter community communication.

As shown in Fig. 2, if the source node s wants to send a packetto a destination node d outside community A, it can choose one ofthe nodes i, j, and k as a relay node. Degree centrality for node i, j,and k is the same (i.e., 5); however, the neighbors of j remain thesame while the neighbor list of node j and k include some transitnodes. This indicates that node i and k aremore active as comparedto node j. Besides, the interaction of node i is limited to membersof community A; however, node k has an interaction with nodesfrom community C, whichmay increase the probability of messagedelivery towards destination community. In other words, node khas higher global community acquaintance as compared to node iand j. Following this, we consider these three social featuremetricsto define priority value for data forwarding to choose next relaynode. The following subsections describe how we estimate thesesocial feature metrics.

3.1. Community acquaintance

Community acquaintance is the global and local measurementof node’s popularity in a network. Higher global community ac-quaintance increases the probability of inter-community packetforwarding, and higher local community acquaintance increasesthe probability of intra-community forwarding. In our proposedmethod we use the following equation to estimate nodes’ commu-nity acquaintance.

CAcqni = αCAcqlocalni + (1− α)CAcqglobalni . (1)

Where α is a control variable, and the values are used in ourproposed model are 1 and 0 for intra-community and inter-community packet forwarding respectively. Global community ac-quaintance is required for inter-community forwarding, but oncea packet reaches destination community, the local communityacquaintance is considered for packet forwarding. Node encounteris measured when two nodes are within the communication rangeof each other, and a hellomessage is successfully exchanged.

CAcqlocalni =N∑j=1

Encounterni,nj (2)

Equation 2 is used to calculate the local community acquaintanceof node ni. Where Encounterni,nj = 1, if a hello message is success-fully exchanged between ni and nj and ni and nj belong to the samecommunity.N is the total number of nodes in the same community.

Similarly, Eq. (3) is used to calculate the global communityacquaintance of node ni. Where Encounterni,nk = 1, if a hellomessage is successfully exchanged between ni and nk and ni andnk do not belong to the same community. M is the total numberof nodes across all communities (total number of nodes in thenetwork).

CAcqglobalni =M∑

k=1

Encounterni,nk (3)

Individualswith similar and shared interests build communities. Inthis paper, we assign community numbers to nodes based on theirinterest and mobility similarity, which is similar to LABEL [30].Initially, community acquaintance of node ni (global and local)remains 1 and increases once a node encounters other nodes.Values of global and local community acquaintance depend uponthe number of communities and number of members in eachcommunity respectively. For example, in a network topology with100nodes, four communities, and 25members in each community,the highest possible value for global and local community acquain-tance can be 4 and 25 respectively.When a node encounters a nodefrom the same community, excluding repetition, local communityacquaintance is increased, but global community acquaintanceremains the same. On the hand, if a node encounters a node fromthe different community then its global acquaintance increases.

3.2. Social activeness

The network topology of VSNs is highly dynamic, and nodes’neighbor list frequently alterswith time. As a general concept fromsocial life, a person who meets more new people in his/her dailyroutine is considered to be more socially active as compared toone who keeps his/her interaction to a group of limited people.Following this concept, the social activeness of node ni at time tcan be calculated as

SActni (t) = 1−Nni (t −△t) ∩ Nni (t)Nni (t −△t) ∪ Nni (t)

(4)

where Nni (t) and Nni (t − △t) represent the number of currentneighbors of node ni at time t and previous number of neighbors

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Please cite this article in press as: A. Rahim, et al., Social acquaintance based routing in Vehicular Social Networks, Future Generation Computer Systems (2017),http://dx.doi.org/10.1016/j.future.2017.07.059.

A. Rahim et al. / Future Generation Computer Systems ( ) – 5

Fig. 2. Community acquaintance.

of node ni at time t −△t respectively. Value of△t is not constant;it depends upon the current and previous value of t at which thevalue SActni (t) is calculated.

SActni ← βSActni (t −△t)+ (1− β)SActni (t) (5)

where β is a smoothing factor to consider the current value ofSActni at time t and the previous value of SActni at time t − △t .In our proposed method, the value of β is set to 0.5 to considerthe equal significance of current and the previous value of SActni .A greater value for SActni indicates that ni is more active in thenetwork probability to meet new members is high. Consequently,increases the probability of packet delivery.

3.3. Degree centrality

Centrality is the relative measurement of nodes’ importance ina social network and can be measured with different methods ob-served from social network analysis. However, we consider degreecentrality of a node to measure its capability of direct links withits neighbors in VSNs. A Higher value of degree centrality of nodesindicates the stronger capability of interaction with other nodes inVSNs and increases the probability of packet delivery. Gu et al. in[32] consider a similar concept of centrality and node activeness;however, our consideration and use of these metrics are entirelydifferent. For a node ni, degree centrality at time t is calculated as

DCni (t) =N∑

k=1

Eni,nj (6)

Where Eni,nj = 1 if there exists a direct communication linkbetween nodes ni and nj. Similar to social activeness of a node,degree centrality is periodically updated with a smoothing factorβ = 0.5 so that the current and previous value of DCni are equallyweighted.

DCni (t)← βDCni (t)+ (1− β)DCni (t −△t) (7)

3.4. SARP

Nodeswithin their communication range communicate in pair-wise fashion and can exchange data packets. Nodes on the en-counterwith other nodes update their neighbor list and count theirdirect contacts with other nodes to update values of SAct and DC .Thus, nodes on the encounter with other nodes also keep track

of their community acquaintance (CAcq). Our proposed protocol,SARP, collectively combines the above three social metrics to cal-culate the priority value using the following equation.

Priorityni = 1−1

CAcqni + SActni + DCni (t)(8)

If node ni encounters node nj, it compares the Priority values,and if the Priority of node nj is greater than node ni, node ni willforward the message to node nj otherwise it will buffer and carrythe message until a new node with higher Priority is encountered.

4. Simulation setup

In this section, we present the simulation setup to evaluate theperformance of SARP. We used VanetMobiSim to generate vehic-ular mobility traces and widely-adopted network simulator NS2to assess the performance of our proposed protocol. We compareour proposed protocol with twowidely accepted routing protocolsfor VANETs and MANETs, i.e., GPSR and AODV. For mobility tracesgeneration, we considered, and area of 2000× 3000 m2 area nearXinghai Square2 as shown in Fig. 3. Other parameters that we usedfor our simulation are illustrated in Table 1. For implementationand simulation, we made the following assumption in this paper.

• All nodes are fully cooperative and cooperate in data for-warding;• Nodes are categorized into different communities based on

interest and mobility pattern.

5. Results and discussion

In this paper, we investigate the average End-to-end Delay andPacket delivery ratio with variation in node density with constantspeed and with constant node density with varying speed. Thissection presents the performance analysis of SARP in comparisonwith AODV and GPSR.

2 Xinghai Square is a city square in Dalian City, Liaoning Province, China. https://en.wikipedia.org/wiki/Xinghai_Square

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Fig. 3. Simulation area.

Table 1Simulation parameters.

Parameter Value

Network simulator ns-2.34Simulation time 300 sNumber of nodes 100, 150, 200, 250, 300Simulation area 2000× 3000 m2

Trip generation Random trip generationTraffic source/destination RandomPackets generation rate 5 PacketsCBR interval 0.20 sMAC protocol IEEE 802.11pSpeed of vehicles 30–70 KM/HTransmission range 80 m

5.1. End-to-end delay

The average end-to-end delay is one of the important factors tocompare the performance of any routing protocol in communica-tion networks. The average end-to-end delay is the measurementof all possible delays from a source node to destination node,i.e., buffering, propagation, transmission, and retransmission de-lay. End-to-end delay is the time taken by data packets deliveredsuccessfully from a source node to destination node in a network.Average end-to-end delay can be calculated as the mean of end-to-end delays of all successfully delivered packets. We calculatedthe end-to-end delay as the time difference of the time at whichpacket was transmitted at the source and the time at which it wasdelivered at destination. In our analysis, we analyzed end-to-enddelay for different node density and different node speed.

5.1.1. End-to-end delay vs. number of nodesFig. 4 shows the average end-to-end delay vs. the number of

vehicles with constant node speed. We studied the end-to-end de-lay of our proposed protocol in comparison with AODV and GPSR.The result shows that end-to-end delay of AODV is much higherwith the lower number of nodes in the network as compared toGPSR and SARP. The reasons for higher end-to-end delay of AODVare the frequent breaking of links and re-establishment of newconnections. However, with increasing number of nodes, end-to-end delay is reduced, but with further increase in the number ofnodes, end-to-end delay starts increasing due to increased routingoverhead. On the other side, end-to-end delay of GPSR is lowerthan AODV. However, end-to-end delay increases with increasedpacket overhead caused by hello beacons in dense networks. End-to-end delay of SARP is greater thanGPSRwith lower node density;however, it decreases with increasing node density. With increas-ing node density, the probability of nodes encounters increases.

Fig. 4. End-to-end delay vs. number of nodes.

Consequently, the probability of data delivery increases resultingin lower end-to-end delay. Thus, SARP outperforms GPSR andAODV in the dense network with minimized end-to-end delay.

5.1.2. End-to-end delay vs. speedFig. 5 shows the end-to-end delay vs. node speed with a con-

stant number of nodes. We kept the number of nodes consistent toanalyze the effect of node mobility on end-to-end delay. At lowerspeed with constant node density, SARP and GPSR outperformAODV. However, with increasing node speed end-to-end delay ofAODV starts decreasing but with the further increasing, it startsrising again. This degradation is caused by broken link with highnode mobility. On the other hand, end-to-end delay of GPSR in-creases with increase node speed. To compare with AODV andGPSR, SARP outperforms concerning end-to-end delay. Initially,the probability to of node encounters increases with increasingnode speed. Consequently, end-to-end delay starts decreasing.However, a further increase in speed results in increased end-to-end but still outperforms both AODV and GPSR. The reason for thisincrease is the link failure and re-establishment of links at highspeed.

5.2. Packet delivery ratio

Packet delivery ratio is another important factor to measurethe performance of protocols in networks. Packet delivery ratio

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Fig. 5. End-to-end delay vs. node speed.

depends on various parameters set for simulation which includespacket size, transmission range, the number of nodes, and nodemobility. Packet delivery ratio is the ratio of the number of packetssuccessfully delivered to a destination node to the number of pack-ets sent by a source node. The performance is considered to betterif the packet delivery ratio is high. In our analysis, we comparedthe performance of SARP in terms of packet delivery ratio withdifferent node density and varying node speed.

5.2.1. Packet delivery ratio vs. node densityPacket delivery ratio of SARP in comparison with AODV and

GPSR with respect to the number of nodes under constant speed isshown in Fig. 6. It is shown in the figure thatwith lownode density,packet delivery ratio of SARP is better than GPSR but lower thanAODV; however, packet delivery ratio of AODV starts decreasingwith increasing number of nodes while packet delivery ratio SARPincreaseswith increasing number of nodes. As the number of nodesincreases, the probability of node encounter increases resulting inincreased probability of relay nodes with a high priority value. Onthe other hand, packet delivery ratio of AODV and GPSR decreaseswith increasing number of nodes as routing overhead increases indense networks.

5.2.2. Packet delivery ratio vs. node speedFig. 7 shows the effect of varying speed on packet delivery ratio

with constant nodedensity. As shown in the figure, increasingnodespeed has reversed effect on packet delivery ratio; however, SARPstill outperforms AODV and GPSR. Initially, with the increase inspeed performance of all protocols increases in terms of packetdelivery ratio, however, with further increase in speed, the per-formance of AODV and GPSR starts immediately degrading whileSARP shows better performance in terms of packet delivery ratio.Link failure at high speed causes packet loss due to which AODVperformance is degraded. Similarly, very short contact durationof nodes also results in packet loss degrading the performanceof GPSR and SARP. In Case if GPSR, sometimes packets may beforwarded in the wrong direction that leads to low data deliveryratio.

5.3. Impact of community acquaintance

The three social metrics we considered for our proposed modelare independent. However, all these social metrics show the im-portance of a node in the network and play an important role

Fig. 6. Packet delivery ratio vs. number of nodes.

Fig. 7. Packet delivery ratio vs. node speed.

in decision-making procedure to select an intermediate node fordata delivery. We investigated the importance of community ac-quaintance in the network and performed some simulation to seethe impact of community acquaintance on end-to-end delay andpacket delivery ratio. We considered only degree centrality andsocial activeness for decision making (SARP-DC).

As shown in Fig. 8, SARP-DC performs better than AODV andGPSR in terms of end-to-end delay. However, its end-to-end delayis greater than SARP. In this case, the possible reason for this delayis that data packets are forwarded to nodes with higher degreecentrality but low or no community acquaintance. On the otherside, in SARP data packets are sent to the node with possiblecommunity acquaintance resulting in lower end-to-end delay.

Community acquaintance does not only help to improve theperformance in terms of end-to-end delay but also increases theprobability of packet delivery. As it is shown in Fig. 9, the packetdelivery ratio of SARP-DC is degraded as compared to AODV, GPSRand SARP. Some of the packets are forwarded to nodes with low orno community acquaintance and are dropped as the TTL expiresbefore reaching the destination community. Consequently, fromthis analysis, it is concluded that community acquaintance not only

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Fig. 8. End-to-end delay with degree centrality.

Fig. 9. Packet delivery with degree centrality.

helps to improve performance in terms of end-to-end delay butalso data delivery ratio.

6. Conclusion

VSNs being a bridge between vehicular networks and socialnetworks have attracted the research community due to its diverserange of applications. In this paper, we have presented a novelrouting protocol exploiting social feature metrics, opportunisticencounters and mobility pattern for collaborative data deliveryin VSNs. Traditional routing protocols based on network topologydo not suit VSNs due to its highly dynamic nature. Similarly,geographically based routing protocols may result in the localoptimum. The proposed protocol considers the opportunistic en-counters of nodeswith social featuremetrics to quantify the globaland local community acquaintance of nodes for selection of relaynodes.

We used VanetMobiSim formobility trace generation consider-ing a local area of 2000 × 3000 m2. Extensive simulations wereperformed using vehicular mobility traces using NS2 to analyzethe effect of node density and speed of vehicles on end-to-end

delay and packet delivery ratio. We compared the performance ofour proposed protocol, SARP, with two widely accepted routingprotocols, i.e., AODV and GPSR. Results have shown that overallSARP outperforms AODV and GPSR in terms of packet deliveryratio and end-to-end delay. However, packet delivery ratio of SARPdecreasing with increasing speed but still outperforms AODV andGPSR.

The mobility pattern of real data sets may not be the sameas generated by mobility generation tools. We intend to considerreal data sets of vehicular mobility to analyze the performanceas a future work. Besides, emergency warning systems in VSNsdemand reduced end-to-end delay and to prioritize and scheduleemergency messages. Thus, we will focus on packet scheduling inVSNs. Furthermore, to enhance packet data delivery and reduceend-to-end delay we will focus on socially-aware multi-casting inVSNs with real vehicular mobility traces.

Acknowledgments

The authors extend their appreciation to the InternationalScientific Partnership Program ISPP at King Saud University forfunding this research work through ISPP#0078. Also, this workis supported by National Natural Science Foundation of China(61572106, 61502075 and 61672131), Dalian Science and Technol-ogy Planning Project (2015A11GX015 and 2015R054).

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Azizur Rahim received the B.Sc. degree from Universityof Engineering and Technology, Peshawar and MS degreein Electrical Engineering from COMSATS, Islamabad. He iscurrently a PhD scholar under the supervision of Prof. FengXia at The Alpha Lab, School of Software, Dalian Univer-sity of Technology, Dalian, China. His research interestsinclude Mobile and Social Computing, Ad hoc Networks,VANETs, Mobile Social Networks, Vehicular Social Net-works.

Tie Qiu received B.Sc from Inner Mongolia University ofTechnology,M.Sc and Ph.D fromDalianUniversity of Tech-nology (DUT), China, in 2003, 2005 and 2012, respectively.He is currently Associate Professor at School of Software,Dalian University of Technology. He was a visiting profes-sor at Electrical and Computer Engineering at Iowa StateUniversity in U.S. (January 2014–January 2015). He servesas an Associate Editor of IEEE Access Journal, Computers& Electrical Engineering (Elsevier Journal) and Human-centric Computing and Information Sciences (SpringerJournal), an Editorial Board Member of Ad hoc Networks

(Elsevier Journal) and International Journal on Ad hoc Networking Systems, a GuestEditor of Future Generation Computer Systems (Elsevier Journal). He serves asGeneral Chair, PC Chair, Workshop Chair, Publicity Chair, Publication Chair or TPCMember of a number of conferences. He has authored/coauthored 8 books, over60 scientific papers in International Journals and Conference Proceedings. He hascontributed to the development of 4 copyrighted software systems and invented15 patents. He is a senior member of China Computer Federation (CCF) and a SeniorMember of IEEE and ACM.

Zhaolong Ning received the M.Sc. and Ph.D. degrees fromNortheastern University, China, in 2011 and 2014, respec-tively. He was a Research Fellow at Kyushu University,Japan, from 2013 to 2014. He is currently an AssistantProfessor at the Dalian University of Technology. His cur-rent research interests include cloud computing, networkoptimization, and social network. He has authored over 40papers in the above areas. He is a member of the ACM.

Jinzhong Wang received the B.S. degree in computer ed-ucation from Anshan Normal University, Anshan, China,in 2002, and the M.Sc. degree in computer applicationtechnology from Liaoning University, Shenyang, China,in 2005. Since 2005, he has been with Shenyang SportUniversity, Shenyang, China. He is currently pursuing thePh.D. degreewith theAlpha Lab, School of Software, DalianUniversity of Technology, Dalian, China. His current re-search interests include the analysis of big trafic data andmobility pattern prediction by trajectory data.

Noor Ullah received his Bachelors and Masters degrees inElectrical Engineering with major in Communication andElectronics from University of Engineering and Technol-ogy Peshawar, Pakistan. He is currently a Ph.D. candidatein Software Engineering, at School of Software, DalianUniversity of Technology, China. His research interestsinclude, but not limited to Vehicular Socials Networks andIntelligent Transportation System design.

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Amr Tolba received the MSc and Ph.D. degrees from Fac-ulty of Science, Menoufia University, Egypt, in 2002 and2006 respectively. He is currently anAssociate Professor atFaculty of Science, Menoufia University, Egypt. He is nowon leave from Menoufia Univesity to Computer ScienceDepartment, Community College, King Saud University(KSU), Saudi Arabia. Dr Tolba serves as a Technical Pro-gram Committee Member in several conferences. He hasauthored/coauthored over 30 scientific papers in Inter-national Journals and Conference Proceedings. His mainresearch interests include Social-Aware Network, Internet

of Things, Intelligent Systems, Big Data, Recommender Systems, and Cloud Comput-ing.

Feng Xia received the B.Sc. and Ph.D. degrees from Zhe-jiang University, Hangzhou, China. He was a Research Fel-low at Queensland University of Technology, Australia. Heis currently a Full Professor in School of Software, DalianUniversity of Technology, China. He is the (Guest) Editor ofseveral International Journals. He serves as General Chair,PC Chair, Workshop Chair, or Publicity Chair of a numberof conferences. Dr. Xia has published 2 books and over200 scientific papers in International Journals and Con-ferences. His research interests include Social Computing,Computational Social Science, big data, and mobile social

networks. He is a Senior Member of IEEE and ACM.