BEEINFO: Interest-Based Forwarding Using Artiﬁcial Bee...

1188 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 3, MARCH 2015

BEEINFO: Interest-Based Forwarding UsingArtificial Bee Colony for Socially Aware Networking

Feng Xia, Senior Member, IEEE, Li Liu, Jie Li, Ahmedin Mohammed Ahmed, Student Member, IEEE,Laurence Tianruo Yang, Member, IEEE, and Jianhua Ma

Abstract—Socially aware networking (SAN) provides a promis-ing paradigm for routing and forwarding data packets by exploit-ing social properties of involved entities, for example, in vehicularsocial networks (VSNs). The mobility of individuals often featuressome regularity in location and time, particularly in vehicular en-vironments. However, individuals’ learning capability and aware-ness to the dynamic environments have not been well exploredin the literature. Inspired by the artificial bee colony, we presentBEEINFO, which is a set of interest-based forwarding schemesfor SAN, which consists of BEEINFO-D, BEEINFO-S, andBEEINFO-D&S. BEEINFO adopts the food foraging behavior ofbees to detect the environment information and to optimize theforwarding procedure. BEEINFO takes advantage of individuals’perceiving and learning capability to gather information of densityand social ties. BEEINFO-D, BEEINFO-S, and BEEINFO-D&Sare distinct from each other according to different utilizationof density and social ties. This enhances the adaptability to dy-namic environments. Additionally, BEEINFO performs messagescheduling and buffer management to improve the forwardingperformance. Extensive simulations have been conducted to com-pare BEEINFO with two representative protocols, i.e., PRoPHETand Epidemic. The results illustrate that BEEINFO outperformsPRoPHET and Epidemic with higher message delivery ratio, lessoverhead, and fewer hop counts.

Index Terms—Artificial bee colony, interest, routing and for-warding, socially aware networking (SAN), vehicle social networks(VSNs).

I. INTRODUCTION

THE popularity of mobile devices (e.g., smartphones,tablets, vehicle onboard equipment, etc.) makes them the

main body of future communications and services. Peoplespend a great amount of time surfing the Internet, following

Manuscript received May 2, 2013; revised September 21, 2013; acceptedJanuary 26, 2014. Date of publication February 10, 2014; date of current ver-sion March 10, 2015. This work was supported in part by the National NaturalScience Foundation of China under Grant 60903153 and Grant 61203165, bythe Liaoning Provincial Natural Science Foundation of China under Grant201202032, and by the Fundamental Research Funds for the Central Univer-sities under Grant DUT12JR10. The review of this paper was coordinated byDr. J. D. Panagiotis Papadimitratos.

F. Xia, J. Li, and A. M. Ahmed are with the School of Software, DalianUniversity of Technology, Dalian 116620, China (e-mail: [email protected]).

L. Liu is with the School of Software, Dalian University of Technology,Dalian 116620, China, and also with Shandong Jiaotong University, Jinan250357, China.

L. T. Yang is with the School of Computer Science and Technology,Huazhong University of Science and Technology, Wuhan 430074, China, andalso with the Department of Computer Science, St. Francis Xavier University,Antigonish, NS B2G 2W5, Canada (e-mail: [email protected]).

J. Ma is with the Faculty of Computer and Information Sciences, HoseiUniversity, Tokyo 102-8160, Japan (e-mail: [email protected]).

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

Digital Object Identifier 10.1109/TVT.2014.2305192

microblogs, sharing information, and utilizing applications inmobile environments. Due to the inseparable relationship be-tween a mobile device and its carrier/user, social-based relation-ships and mobility characters of the carriers have been exploitedrecently in many research fields, such as delay-tolerant net-works (DTNs) [1], opportunistic networks (OppNets) [2], andvehicular ad hoc networks [3]. We call this emerging network-ing paradigm socially aware networking (SAN), which takesadvantage of the mobile device carriers’ social relationships/properties and utilizes them as the main design force in, forexample, mobile (ad hoc) social networks.

SAN works similarly to DTNs and OppNets. On one hand,they all lack end-to-end routes from the source node to thedestination node due to the dynamic topology induced bythe mobility of nodes. Thus, they generally utilize meetingopportunities to implement transmission in multihop mode. Onthe other hand, they all adopt a store–carry–forward paradigmin pairwise fashion to provide communications between mobiledevices with the absence of the infrastructure, relying on short-range communication technologies such as Wi-Fi and Blue-tooth. Therefore, to predict the meeting opportunity is a keyproblem for them with the assumption that the mobility processis ergodic and stationary. However, the significant differencebetween SAN and DTNs (or OppNets) is that SAN takesthe social properties into consideration to solve problems inrouting, forwarding, and information dissemination.

Social relationships are relatively stable, and they vary lessfrequently than the transmission link between mobile nodes,which can be leveraged to enable efficient message transmis-sion [4]. This constitutes the basic idea behind SAN. Conti andKumar [5] identify two social levels in the opportunistic envi-ronment by embedding the social relationships in the electronicworld: electronic and virtual social networks.

Fig. 1 shows how the two social levels map with each otherin the context of SAN. Mobile devices form electronic socialnetworks when they are close enough to communicate, andtheir spatiotemporal properties determine their relationships.A typical instance of electronic social networks is vehicularsocial networks (VSNs) [6], [7]. Moreover, individuals usuallyhave regular mobility patterns, making the mobile devicescome into VSNs in a regular fashion [8]. For example, anindividual usually drives with fixed routes between his homeand workplace. Meanwhile, the objects carrying mobile devicesconstruct the virtual social networks based on their inherent so-cial properties/relations. Generally, electronic social networkschange rapidly due to the mobility of mobile devices. However,people’s relationships do not change much over time, and this

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XIA et al.: BEEINFO 1189

Fig. 1. Vehicular social networks and virtual social networks.

makes virtual social networks relatively stable and of greatvalue to conduct research on social computing.

Vehicular ad hoc networks have attracted a lot of atten-tion in recent years, for satisfying the demand of vehicle-to-vehicle, vehicle-to-roadside, or vehicle-to-infrastructurecommunications [9]. Individuals and vehicles are bonded witheach other. First, people move with heavy reliance on vehicles,and they usually go to office or go back home by car or by bus.Second, vehicles can expand the people’s mobility range andprovide special and temporary mobile transmission networks.For instance, people on a bus can communicate with each otherthrough their portable communication devices, which helpsform a temporary mobile ad hoc network. Private cars representpeople’s long-distance movement orbit, whereas buses followfixed routes. Due to the long and regular movement property,vehicles are superior for perceiving the requirement and changefor different data in different environments. Obviously, vehiclescan effectively participate in routing and data disseminationin SAN, such as news dissemination application. However,the communication between vehicles and portable devices isignored in recent research.

Virtual social community and social tie are the most usu-ally used concepts in SAN. Community is inspired from thegregarious property of society, in which mobile nodes contactfrequently, whereas social tie indicates the relationship strengthamong nodes. Mobile nodes can be departed into differentcommunities according to contact frequencies. The communitymembers can meet more often than others out of the communitycan. Consequently, when a source node generates a message foranother node, it can select an appropriate forwarder to deliverthe message to a destination community where the destinationnode belongs. This phase is called inter-community forwarding.Afterward, the intracommunity forwarding phase starts in thedestination community. In this phase, the central node is usedfor its highest centrality value, meaning that it is able to en-counter more nodes. Except for the central node, social tie canhelp in estimating the strength of social relationships amongnodes. Specifically, the stronger the tie connecting two nodes,the more similar they are [10].

These concepts improve the delivery efficiency of social-based routing protocols. However, there are still some difficul-ties to overcome. The first one is how to conduct communityconstruction and detection in SAN, due to the absence of a

central control mechanism, let alone the changing networktopology. Additionally, in this transient environment, a commu-nity construction algorithm usually runs continuously, inducinghigh cost. The second problem is to predict future encountersand calculate social relationships with less resource consump-tion because, in SAN, a mobile node needs to collect, store,and process a large number of information, covering contacthistory, personal information, and operation records. However,it is very challenging for mobile nodes with limited resources.Meanwhile, a significant amount of information needs to beexchanged. This may cause a serious congestion problem,which will potentially impair the performance.

In summary, the key to resolve these challenges facing SANis to improve the adaptability to dynamic environments. It isessential to find an appropriate method to detect the changes ofenvironment context and to adapt to them in a timely fashion.Fortunately, similar problems have been well solved in biolog-ical systems by the use of swarm intelligence [11]. Swarm in-telligence studies the complex natural behaviors in their socialactivities, including cooperation and interaction with the envi-ronment. Biological systems such as ant colony [12], bee colony[13], and fish schooling [14] have shown excellent applicabilityin algorithm design. These algorithms often feature highadaptability and learning capability to dynamic environments,which can be used to solve the aforementioned problems.

In this paper, we take into account social properties andmobility regularities of human beings and vehicles and presenta set of routing algorithms named BEEINFO, which standsfor artificial BEE-colony-inspired INterest-based FOrwarding,under the framework of SAN. BEEINFO serves its purpose bymaking full use of individuals’ learning capability and mutualcooperation to be aware of the changing environment dynami-cally, which is inspired by the artificial bee colony algorithm.

A previous version of BEEINFO has been proposed in [15].This paper differs from our previous work in that 1) the originalBEEINFO is extended from a single scheme to a set of algo-rithms, including BEEINFO-D, BEEINFO-S, and BEEINFO-D&S for diverse situations; 2) the algorithms of environmentawareness and forwarding strategy are improved; and 3) in thispaper, diverse movement models are adopted, and extensivesimulation experiments are conducted in vehicular environmentto examine the performance of BEEINFO.

BEEINFO is novel in several aspects. First, it enhancesthe adaptability to dynamic environments through exploitingswarm intelligence. Second, it classifies communities based onpersonal interests into specified categories. This eliminates thecost of community detection and construction. Furthermore,nodes exchange their interest information (i.e., their exact in-terests) when they are in transmission range, to predict the en-vironment information and social tie. The interest informationis small enough to reduce the congestion. In addition, mobilenodes process limited information, which saves resources, in-cluding buffer and energy.

The remainder of this paper is organized as follows.Section II reviews related work on socially aware routing andforwarding protocols and on the artificial bee colony algorithmand its applications. We describe BEEINFO’s system modelin Section III and the forwarding process in Section IV. In


Section V, we present the simulation environment in detail, andthe performance of BEEINFO, PRoPHET [16], and Epidemic[17] is qualitatively evaluated via extensive simulations.Finally, Section VI concludes this paper and illustrates thefuture work.

II. RELATED WORK

An increasing number of research efforts have been made inthe emerging field of SAN, where two kinds of social prop-erties, i.e., community and behavior regularity, are often used.Some of these works lay a solid foundation for our BEEINFO.In addition, swarm intelligence, particularly the artificial beecolony algorithm, provides an intriguing method to enhance theperformance of BEEINFO.

A. Socially Aware Routing and Forwarding

It is generally understood that community members canmeet more often than others out of the community. Individualsconstitute a community according to common interests or acommonality relationship. LABEL [18] and BUBBLE RAP[19] are two well-known routing protocols based on commu-nity. LABEL, which is very simple, delivers the messagesonly to the members in the destination community. However,BUBBLE RAP is more complex than LABEL, based on so-cially aware overlay. It uses community and central nodesto construct socially aware overlay and enhances the routingperformance effectively. The drawback lies in that the cost toconstruct and maintain a socially aware overlay is high.

In addition, LocalCom [20] and Gently [21] take inter-community routings into consideration. LocalCom presents ametric named similarity to construct the neighboring graph,which considers the encounter frequency, encounter length, andseparation period in the encounter history. It uses the neighbor-ing graph for detecting the community, the similarity for routingin intracommunity, and controlled flooding routing for inter-community communication based on gateways. Gently is basedon the context-aware adaptive routing (CAR) protocol [22]and LABEL. It uses CAR-like routing when no nodes of thedestination community are in reach. When the message carriermeets a node of the destination community, Gently adopts aLABEL-based strategy. Then, in the destination community,CAR-like routing is used again to deliver the message to thedestination.

Unlike community, behavior regularity focuses more on in-dividuals. The basic idea behind is that human beings often haverepeated mobility patterns. For instance, many people usuallyfollow similar daily mobility patterns from Monday to Friday.The regular encounter history is beneficial to predict the futureencounter probability. SimBet [23], SimBetTS [24], and HiBOp[25] are typical routing protocols of this kind. SimBet is basedon the utility function, which exploits betweenness centralityand similarity to the destination node using an ego network. Inaddition to betweenness and similarity, SimBetTS takes socialtie strength into consideration. HiBOp automatically learnsand represents context information, i.e., the users’ behaviorand social relations, and exploits this knowledge to drive theforwarding process.

These protocols predict the encounter probability using his-tory records, but they ignore the mobile nodes’ group identities.In contrast, BEEINFO combines these properties. Specifically,BEEINFO recognizes different communities based on the mo-bile nodes’ interests, and each node can perceive the densitiesof different communities by which it passes. Additionally,our scheme can use the history and present information topredict the future pattern as mobile nodes usually have similarmovement trajectory in a period.

B. Swarm Intelligence

Biological society can perform complex tasks, such as re-source management and task allocation, social differentiation,and synchronization (or desynchronization), by just relyingon the cooperation among social individuals (e.g., insects).Surprisingly, the law governing the biological system is a smallnumber of simple generic rules, with no external controllingentity. Several typical algorithms have set good examples onhow swarm intelligence help tackle problems in various fields.The artificial bee colony algorithm is one of them.

Mohan and Baskaran [12] surveyed the recent researchand implementation of ant colony optimization and proposedSMACO, an improved ant colony optimization model to solvenetwork routing problems. The simulation results showed thatSMACO can reduce packet losses and response time. To studyhow individuals integrate information for movement guidance,Katz et al. [14] studied the trajectories of golden shinersswimming in diverse shoals. The interactions observed couldhelp explain how the dramatic changes in position and velocityenable animals to stay cohesive and amplify significant socialinformation. In [26], Kachroudi et al. used an online particleswarm optimization algorithm to design a predictive decisionsupport system for energy-based driving, in terms of batteryautonomy, driving comfort indexes, and travel time.

The artificial bee colony algorithm imitates the bees’ be-haviors to search for nectar sources, which tends to attain theoptimal division of labor in bees. Theoretically, the richer andcloser a food source, the more bees would be sent. The scoutbees search the nectar in a random direction and gather infor-mation in the meantime. The search results are called PATCH,involving food density in an area and the rough direction. Whenscout bees return to the hive, they perform a characteristic dancecalled the waggle dance to project the PATCH. Then, accordingto the PATCH, a proper number of scout bees are assigned towork as follower bees on the best patch sites because they arethe ones who found these sites.

Some routing protocols inspired by artificial bee colony havebeen proposed recently. BeeAdHoc [27] is a mobile ad hocnetwork routing algorithm in which an artificial bee colonyshows its value in designing three parts for each node: pack-ing floor, entrance, and dance floor. The packing floor andthe entrance provide an interface to the transport layer andthe medium-access-control layer, respectively. The dance floormakes routing decisions. This protocol uses the three layersto filter and select the best router. Karaboga and Akay [13]used the artificial bee colony algorithm to solve optimizationproblem, and it has been proven that the artificial bee colony


converges well and computes quickly. Rahmatizadeh et al.[28] proposed an ant–bee routing (ABR) protocol to solve theadaptive routing problem by use of social insect metaphors. InABR, the ant is responsible for searching the food (destination).Once the destination is searched, an artificial bee is assigned toone of the destination.

In contrast to previous works, we pay more attention tothe awareness capability to the environment (nectars) of bees.BEEINFO combines the social properties and takes into ac-count awareness to the environment inspired by artificial beecolony algorithm. To the best of our knowledge, BEEINFO andits previous version are the first efforts that take the environmentfactor as the motive to solve the routing protocol problemin SAN.

III. OVERVIEW OF BEEINFO

A. System Model

In this paper, we consider an application scenario that is closeto reality. We consider three kinds of mobile object: pedestrians,cars, and buses. Each object is equipped with a mobile device(for a pedestrian) or a vehicle device (for a car or a bus). Thedevice on a vehicle is controlled by the driver, and we omitthe influence of passengers. They communicate with each otherthrough wireless interfaces, e.g., Bluetooth or Wi-Fi. We referto these devices as nodes in this paper. The application aims ateffective routing through these mobile nodes.

The nodes related to pedestrians and cars have more objectiveproperties that indicate their owners’ interests. When peopleaccess the Internet or share data, they focus more on informa-tion they are interested in. People’s interests change in a longtimescale, whereas they are stable in a relatively short time. Forinstance, people may be interested in entertainment in daily life,but during the Olympic Games, they perhaps pay more attentionto sports.

People (e.g., classmates or colleagues) with the same inter-ests usually get together to talk about and share their interests.They contact frequently and form a community. These interest-based communities are bonded to regions and differ from eachother on interests. For instance, students in school usually shareno common interests with workers in factories.

Additionally, a large volume of data are generated every day,which is usually labeled with diverse tags to represent theircategories. For example, the news in popular web sites such asCNN and Yahoo! are often categorized into politics, sports,weathers, entertainments, etc. Since the category structures ofdata in different web sites are similar and individuals are usedto the categories, we use data categories to represent people’sinterests. A community is related to one category. To a specifiedcommunity, a person belongs to it if he is interested in thecategory. If he has several interests, he belongs to and affectseveral communities simultaneously. With no doubt, multipleinterests are more complex than a single interest. Thus, weassume that a node only has one interest.

When people move along a route regularly (e.g., go to workfrom Monday to Friday by car), they often pass by severalrelatively fixed locations at similar time points. In vehicularenvironments, the mobility features more regularities, and the

Fig. 2. Bees’ awareness capability.

information can be delivered in a wider area. For instance, buseshave fixed movement routes. These advantages have been takenin quite a lot of research efforts in VSNs [3], [29].

Accordingly, we make the following assumptions in thispaper.

• Nodes are fully cooperative, and there are no misbehaviornodes.

• Three kinds of mobile nodes (pedestrians, cars, and buses)are involved in application scenario, which is a VSN.

• Each node has one interest, and nodes (mobile devices)with the same interest belong to the same community.

• Nodes follow their regular mobility patterns, respectively.

B. Community Based on Interest

Many recent works such as LABEL [18] and SPOON [30]use interest as the (major) measure to construct community, andthey have proven the effectiveness. In SPOON, a node’s interestis extracted through analyzing the files stored in their disksusing the document clustering technique. In fact, the node’sinterest information can often be obtained through decisionanalysis of the history operations of the mobile devices suchas searching and downloading records. In this paper, we assigninterests to nodes, which is similar to LABEL. The number ofinterests is limited, in contrast to the large number of mobilenodes. Consequently, BEEINFO uses little storage to storethe interest-related information, which saves the scarce bufferresource.

C. Inspiration From Artificial Bee Colony

Nodes may pass different communities; therefore, they canserve as postmen for the passing communities if there is extrastorage space. For inter-community, BEEINFO imitates a beesearching process to perceive node’s influence to a differentcommunity. In the artificial bee colony algorithm, bees searchnectars and bring the biggest density nectar back to the hive.Bees are capable of being aware of nectar densities. As Fig. 2shows, a scout bee goes through three nectar sources (SourcesA, B, and C) with different densities. It records every sourceID and its density and then compares the densities to get themaximum.


Fig. 3. Imitation of bees’ awareness capability in VSNs.

BEEINFO takes advantage of individuals’ awareness andlearning capability by imitating bees’ behaviors. Mobile nodesperceive and record information (e.g., densities) of passingcommunities. In the transmission range, nodes exchange theirinterest information. According to the exchanged interest in-formation, every node records or updates the densities ofcommunities. The density information indicates the number ofnodes belonging to a community. The higher the density is,the more nodes the community has. Every node stores a listof passing communities and their densities, which provides aguideline to select better forwarders for the inter-communityphase. Additionally, mobile nodes do not need to search theexact density values but to perceive their passed communitydensities. Therefore, there is no extra cost.

Fig. 3 shows how bees’ awareness capability is imitated inVSNs as an application of BEEINFO. We assume that threedifferent communities exist: the supermarket community, theschool community, and the hospital community. These repre-sent different data categories. The bus passes the supermarketand the school, whereas the car passes all the three spots.The arrows in diverse colors represent the routes of the busand the car separately. Apparently, the bus and the car bothpass the supermarket community and the school community.However, they get different densities for the two communities.The bus obtains bigger density than the car in the supermarketcommunity, with less density in the school community. There-fore, if there is a message to be delivered to the supermarketcommunity, the bus is the better forwarder. Similarly, the car isthe better one to deliver a message to the school community.

For intracommunity, we consider the relationship betweennodes in the same community, which is social tie. The moretimes that two nodes with the same interest meet, the highertheir social tie is. When data are delivered to the destinationcommunity, we need to select intermediate nodes that meetthe destination frequently. It is similar to community densityas they both apply a process of analyzing records, computing,and predicting (see Section V). The difference is that social tieis for intracommunity and its awareness object is nodes withinthe same community, whereas density is for inter-communityprocess and its object is nodes’ interests.

Fig. 4. Components of BEEINFO.

BEEINFO is based on perceiving densities of communitiesfor inter-community and social tie of nodes for intracommunity.We present three routing algorithms according to differentawareness factors.

• BEEINFO-D: BEEINFO-D adopts community density asmain evidence to select forwarders in inter-communityforwarding. For intracommunity forwarding, it directlyforwards the data to the destination node with no help fromothers as the direct forwarding strategy.

• BEEINFO-S: BEEINFO-S mainly adopts social tie infor-mation to perform intracommunity forwarding. For inter-community forwarding, we choose LABEL as it selectsforwarders only from nodes in the same community witha destination node. Thus, BEEINFO-S forwards messagesto nodes that have the same interest with the destinationnode for inter-community forwarding.

• BEEINFO-D&S: BEEINFO-D&S combines density andthe social tie for the whole forwarding process. To beexact, density is for inter-community phase, and the socialtie is for intracommunity forwarding.

D. Components of BEEINFO

Fig. 4 shows the major components of BEEINFO. Mobilenodes are divided into different communities based on interests,and those with the same interest usually exchange messagesfrequently. We need not to detect communities but to selectforwarders according to information obtained from the envi-ronment. According to nodes’ interests, environment awarenesscan perceive the number of passing nodes with different in-terests in a period of time, which we name density. That is,the bigger the density, the more nodes can be met. Providingthat individuals usually have repeated mobility patterns, thedensity information can be used to select forwarders. In onecommunity, its members will be in contact frequently. Thus,BEEINFO maintains social tie between nodes according to thecontact records. The social tie information is utilized to selectthe forwarders in the intracommunity forwarding process. Thus,we design social tie awareness to collect social tie informationin the community from contact history. Based on the densityand social tie information, the forwarding strategy takes differ-ent strategies to select the forwarders under inter-communityand intracommunity conditions. BEEINFO also incorporates


message scheduling and buffer management to further enhancethe forwarding efficiency. As shown in Fig. 4, BEEINFO-Dand BEEINFO-S consist of four components, whereasBEEINFO-D&S utilizes all of the components. The three al-gorithms adopt similar theory and strategy in the componentsof forwarding strategy, message scheduling, and buffer man-agement. The difference is that BEEINFO-D and BEEINFO-Somit the social tie awareness and environment awareness,respectively. Consequently, we describe the implementationof BEEINFO-D&S to represent the forwarding process ofBEEINFO in the following for simplicity.

IV. FORWARDING PROCESS OF BEEINFO

BEEINFO-D&S consists of five components (as shown inFig. 4) to gather environment and social tie information andemploys various information to make decisions.

When nodes are in their transmission ranges, they commu-nicate with each other in a pairwise fashion. The forward-ing process of BEEINFO-D&S is designed as follows. First,they exchange their interest and message list. Second, if theyhave different interests, they update the density informationfor different interests (i.e., different communities). Otherwise,they update the density for their own community and socialtie information for each other. Third, depending on the des-tination’s interest of message, nodes exchange density infor-mation or social tie information. BEEINFO-D&S chooses thebetter forwarder and decides the messages to be delivered byexamining the density or social tie information. Finally, thealgorithm orders the messages according to message schedulingand buffer management, and then, the transmission starts.

When mobile nodes move around, they gather three kinds ofimportant information.

1) Mobile nodes detect the environment context informationand discover current communities, according to theirinterests. For example, if some nodes in a certain areaare interested in A, we say that these nodes belong tocommunity A at present. Due to the nodes’ mobility,the same area may belong to another community; there-fore, the detection of communities is only determined bymobile nodes’ interests, instead of time and space. Thiscommunity detection method can adapt quickly to thechange of environment and needs no maintenance of thecommunity information, which is adaptive to the dynamictopology of the network.

2) While moving, mobile nodes gather information aboutnearby nodes and calculate the densities of differentcommunities by analyzing the information. To the samecommunity, different nodes may have different densitiessince they differ from each other on the number of nodesthat they encounter. To a certain node, the larger densityof a community it has, the more nodes it encounters, andthe more suitable it is to be a forwarder. In an inter-community scenario, the density information can help se-lect better forwarders to deliver the messages efficiently.

3) In intracommunity, social tie information must be main-tained among the contact nodes. A node’s social tie

TABLE IDEFINITION OF SYMBOLS

indicates the social proximity to another node. If node Ahas higher social tie with node B, it suggests that node Ahas more opportunities to meet node B in the community.When B is the destination of a message, A will be a betterchoice as a forwarder. In a word, the social tie is of greatvalue for selecting forwarders.

Mobile nodes can update and maintain the above informationautomatically during their movement and operation, with no ex-ternal control or intervention from any central mechanism. Thischaracteristic makes BEEINFO-D&S effective and flexible inthe absence of infrastructure. The forwarding protocol is basedon the environment and social tie information. To help describethe details, we define some symbols, as shown in Table I.

A. Environment and Social Tie Awareness

When two nodes (SN and IN ) connect to each other, theyupdate and maintain the density or social tie information andselect forwarders according to these information. The informa-tion update and maintenance have no relation with messagesbut only involves SN and IN . If Is is the same as Ii, SN andIN are in the same community, and they should update theirsocial tie information and the density of their own community.Otherwise, SN and IN are in different communities, and theyupdate their density, respectively, for inter-community forward-ing. Algorithm 1 gives the pseudocode of the environmentand social tie information update and maintenance in detail.Notably, it is described from SN ’s perspective. Moreover, thecorresponding equations are described later.

Algorithm 1 Pseudocode for environment and social tieinformation awareness

1: for all nodes INs connected to node SN do2: //In time window T :3: if Is == Ii then4: //Update contact information5: if SN has the social tie record of IN then6: Update social tie information with (3);7: else8: Initiate the social tie to IN in SN ;9: end if

10: Update the intracommunity density with (1);11: else12: //Update inter-community contact information13: if SN has density record of IN then14: Update density information with (1);


15: else16: Initiate the density of IN in SN ;17: end if18: end if19: //When time is multiple of T20: Predict density information with (2);21: Predict present social tie with (4);22: end for

1) Density of Community: In social networks, the degreeof sociability among nodes is a key factor determining thenumber of nodes that a node can encounter [31] for furthercalculation on the successful delivery ratio. Thus, we can usethe centrality of a node in community to measure the densityof this community. The most useful centrality measures aredegree centrality (DC), betweenness centrality, and closenesscentrality. In BEEINFO-D&S, we use degree centrality, i.e.,the number of nodes n that a node encounters over a timewindow T to measure the density of community, as in (1) inthe following:

Densityi(t) =DCi(t) =

T∑

t=0

n (1)

Densityi(t+Δt) =α× Densityi(t−Δt)

+ (1 − α)× Densityi(t). (2)

In addition, considering the influence of the past and presentinformation simultaneously, we produce the density predictionas in (2), which is similar to [31]. α is community densityprediction factor, which indicates the influence ratio of pastinformation and present information.

2) Intracommunity Social Tie of Nodes: When a message isunder delivery to the destination community, the social tie be-tween nodes determines the efficiency. This is because social tieis constructed along with the contact process, and it representsthe direct contact probability (or the social proximity) betweentwo nodes. It can help to find the destination node directly. Notethat the social tie information is limited among nodes in thesame community. We use (3), shown below, to measure thesocial tie over a time window T , and (4), also shown below,to combine the past and present information to predict futuresocial tie, which is also from [31]:

SoTiei, j(t) = (λi, j × di, j(t)) /T (3)

SoTiei, j(t+Δt) =β × SoTiei, j(t−Δt)

+ (1 − β)× SoTiei, j(t). (4)

In (3), λi, j is the contact frequency (i.e., the times i and jcontact over time window T ), and di, j is the contact durationof i and j in time window T . β in (4) is a social-tie predictionfactor, similar to α in (2).

Finally, an evaporation process is necessary for the scout bees“to search” previous density of nectar source and social tie,

using the following separately:

Densitynew =Densityold × γk (5)

SoTienew = SoTieold × γk (6)

where γ is an evaporation factor, and k is the time intervaltoward the last update.

B. Forwarding Strategy

The forwarding strategy is the core of BEEINFO-D&S. Itpredicts the future encountered density or social tie to choosethe best forwarder. The destination information (i.e., ID andinterest) is stored in the message header, and it is easy forBEEINFO-D&S to obtain the information. According to theinterest of DN , SN , and IN , BEEINFO-D&S classifies theenvironment context into inter-community and intracommu-nity and then takes different measures. Algorithm 2 gives thepseudocode of the forwarding strategy, and we describe all thepossible situations to explain the algorithm.

Algorithm 2 Pseudocode of forwarding strategy

1: Given message M in the buffer of a node;2: for all IN do3: if IN is DN then4: Deliver M from SN to IN ;5: else6: if Ii == Id then7: // IN belongs to destination community8: if Is == Id then9: // SN belongs to destination community

10: if SoTie(SN, DN) < SoTie(IN, DN)then

11: Deliver M from SN to IN ;12: end if13: else14: Deliver M from SN to IN ;15: end if16: else if Is! = Id then17: //Ii! = Id and Is! = Id18: if density of SN < density of IN for Id then19: Deliver M from SN to IN ;20: end if21: end if22: end if23: end for

1) Is == Id and Ii == Id: Under this condition, DN ,SN , and IN share the same interest, meaning they are all inthe same community. In intracommunity, the social tie will beutilized to decide the better forwarder. If either SN or IN hascontacted DN , they must maintain the social tie about DN ,respectively. Thus, the node that has the higher social tie willbe selected as the better forwarder. If neither of them has thesocial tie record to DN , SN will stop the forwarding processto wait for a better forwarder.


2) Is == Id and Ii! = Id: DN and SN still share thesame interest, whereas IN has the different interest. Thus,DN and SN are in same community, and IN is an outsidenode. This situation suggests that we need a node in the samecommunity to perform intracommunity forwarding; therefore,IN is not suitable to be a forwarder.

3) Is! = Id and Ii == Id: IN and DN have the sameinterest, whereas SN has the different interest. Thus, IN andDN are in same community, but SN does not belong to them.The forwarding strategy will select IN as a forwarder and themessage will be forwarded from SN to IN .

4) Is! = Id and Is == Ii: In this case, SN and IN havethe same interest, whereas DN ’s interest differs from the oth-ers’. It indicates that SN and IN belong to the same commu-nity but not to the destination community. It is inter-communityforwarding. The density information will be utilized to selectthe forwarder. The node with the higher density to DN will bechosen as forwarder among SN and IN .

5) Is! = Id and Ii! = Id and Is! = Ii: SN , IN , and DNshare no common interests at all; therefore, they are from dif-ferent communities. It is also an inter-community environment.Thus, BEEINFO-D&S performs the same procedure as the lastcondition to choose a forwarder.

BEEINFO-D ignores the collection of social tie information.Thus, its forwarding strategy involves Conditions 4 and 5.BEEINFO-S takes no consideration on density informationof communities. Hence, its forwarding strategy consists ofConditions 1 and 3.

When the destination node receives the message, it broad-casts a response message to notify the nodes that still maintainsthe message to discard it. The response messages are moreefficient for nearby nodes, which have higher probabilities tostore the data. Thus, the response messages are controlled byshort time to live (TTL). The response messages can optimizemessage discarding in buffer management.

C. Message Scheduling and Buffer Management

Mobile nodes are restricted to the scarce resource such aspower, buffer size, and contact time, which have a large effecton the routing and forwarding efficiency. In BEEINFO, we per-form message scheduling and buffer management to improvetransmit rate, to reduce buffer replacement, to lower drop rate,and to save resources. The message scheduling strategy decidesin what order to transmit the messages between nodes withthe least time cost. It ensures that the messages with higheropportunities can be successfully delivered. The buffer man-agement algorithm decides which messages can be discardedwhen the buffer reaches its capacity and when new messagesrequire a buffer. The message scheduling strategy and the bufferreplacement algorithm share the similar principle as they bothrequire excluding or discarding the messages that are expiredor delivered successfully without influencing those messagesin transmission. The difference between them only lies in thesequence. As a result, we choose to describe them together here,and the details are as follows.

When IN is selected as a forwarder and the relevant mes-sages are waiting to be delivered, the relation between the DN

and IN is the major force to measure the success rate. Weassume that there are a set of messages to be transmitted. Themessage scheduling strategy will order the messages accordingto the following priority rules.

1) Since intracommunity transmission has the highest prior-ity, the messages will be transmitted first if they satisfyId == Ii. For the messages in the same condition, thesocial tie between DN and IN will be taken into consid-eration. The messages with higher social tie to DN willhave higher priority. If the social ties are equal, the newerone will be transmitted first.

2) For messages that do not satisfy the condition Id == Ii,it suits the inter-community transmission. We considerthe density of different interests in IN . Because INhas different densities to different communities, the mes-sages will be reordered according to density values. Formessages that have equal value, the newer one will betransmitted first.

The buffer replacement algorithm only relies on SN and themessages. We mainly consider the relationship between DNand SN . The principle of the buffer replacement algorithm justhas the reverse order with the scheduling sequence, as follows.

1) First, the messages that will be sent out of SN ’s commu-nity are replaced. That is the inter-community forward-ing. For these messages, the densities of different DNsin SN further decide the replaced sequence. Messageswith lower density will be discarded first. If the densitiesare equal, the older one will be replaced.

2) Second, we consider the messages in which the Id isequal to Is, and the message with the lowest social tieof DN in SN will be discarded first. If the social ties areequal to each other, the older one will be replaced.

V. PERFORMANCE EVALUATION

A. Simulation Setup

Here, we describe the simulation experiments for evaluatingthe performance of BEEINFO (BEEINFO-D, BEEINFO-S, andBEEINFO-D&S). Additionally, we compare them to Epidemicand PRoPHET in community-based mobility environment. Theexperiments are carried out using the Opportunistic NetworkEnvironment (ONE) Simulator [32]. The ONE simulator isspecifically designed for DTN routing, and it allows creatingscenarios upon different synthetic movement models and real-world traces.

Our main objective is to examine whether BEEINFO per-forms better in terms of delivery ratio, overhead, latency, andhop count, when compared with the other protocols. We con-sider points-of-interest (POIs) movement model, which is sim-ilar to the community-based model. POIs contain several POIareas which indicate interest-based communities, and nodesmove and contact according to predefined probabilities. EachPOI represents a group of nodes sharing the same interest, i.e.,in the same community. In each POI scenario, nodes at theoriginal community meet each other with a high probability andmove to other POIs with a low probability.


TABLE IISIMULATION PARAMETERS

There are four groups of nodes in our experiments, includingtwo pedestrian groups, one car group, and one bus group.The pedestrian groups and the car group are all made up of40 nodes, whereas there are six nodes in the bus group. Cars arerequired to run on the road, whereas buses adopt the MapRoute-Movement model that has a fixed movement route. Pedestriansand cars utilize the shortest-path Map-based movement modelthat uses Dijkstra’s algorithm to find the shortest path betweentwo POIs.

We compare BEEINFO against PRoPHET and Epidemic,which are two representative and popular protocols in the field.The PRoPHET routing is prediction based, i.e., it predicts andselects the forwarders through history encounter records, thesame as BEEINFO. The difference between BEEINFO andPRoPHET is that the former predict upon the density of com-munities and social ties to the nodes with same interest, whereasPRoPHET is based on nodes’ mutual contact history records.BEEINFO takes environmental and personal information intoconsideration, whereas PRoPHET only considers the personalinformation. Epidemic adopts a simple flooding method thatcopies the messages to every encounter that has yet to receivea copy. Without the constraint on buffer, Epidemic can getthe best performance. However, it is not feasible in mobilenetworks. Epidemic usually produces several copies and bringsheavy overhead. BEEINFO selects appropriate forwarders, andit can effectively reduce the number of copies.

The simulation parameters are summarized in Table II. Themobile nodes are set with a buffer of 10 MB and a message TTLof 600 min generally. The wireless transmission applies twokinds of Bluetooth interface: one with a communication rangeof 10 m and a transmission speed of 2 Mb/s for pedestriansand car nodes and the other one with a communication range of1000 m and a transmission speed of 10 Mb/s for bus nodes.Pedestrians and cars generate message events each 50–90 s.Bus nodes are only responsible for transmission. The size ofa message is 0.5–1 MB. The simulation duration is 400 000 s,with 5000 s warming up. We implement experiments from threeviewpoints: different buffer sizes (10–50 MB), different mes-sage TTLs (600–3600 min), and different lengths of simulationtime (50 000–600 000 s).

In each experiment, we compare the protocols based on thefollowing four criteria:

• message delivery ratio, which is the ratio of successfullydelivered messages to the total number of unique messagescreated (with the redundant messages excluded) in a givenperiod;

• overhead, which is the ratio of relayed messages (de-livered messages excluded) and delivered messages, re-flecting the ratio of message replicas propagated into thenetwork;

• average latency, which is the average time between thetime a message is generated and the time it is deliveredsuccessfully (including buffering delays);

• average hop count, which is the average hop counts whenmessages are received successfully.

B. Simulation Results and Analysis

We evaluate the performance of the protocols over differentbuffer sizes, message TTLs, and simulation periods. We ran30 times for each experiment, and the results given representthe average values. The results of simulation experiments areshown in Figs. 5–7. Each figure contains four subfigures (a)–(d)showing comparisons of the three protocols for the message de-livery ratio, overhead, average latency, and average hop counts,respectively.

The data in Fig. 5 show that, the higher the buffer size,the more messages are delivered to their destinations, the lessoverhead is generated, and the fewer hop counts are neededby the protocols. Three BEEINFO algorithms provide the bestresults in terms of message delivery, overhead, and hop countsfor all the buffer sizes.

In BEEINFO algorithms, BEEINFO-D achieves the bestperformance, followed by BEEINFO-S and BEEINFO-D&S.BEEINFO-S and BEEINFO-D&S achieve similar efficiency.Specifically, when the buffer size is 50 MB, BEEINFO-D deliv-ers 74.77% messages (compared with 72.63% for BEEINFO-Sand 72% for BEEINFO-D&S) with message overhead of183.7 (compared with 157 for BEEINFO-S and 205 forBEEINFO-D&S) and hop count of less than 1.85 (2.13 forBEEINFO-S and 2.22 for BEEINFO-D&S). By contrast,Epidemic and PRoPHET obtain worse performance with 64.3%and 56.4% in delivery ratio, 318 and 356 in overhead, and 3.1and 2.6 in hop count, respectively.

Nevertheless, three BEEINFO algorithms generate higheraverage latency, and in particular, BEEINFO-D gets the highest.Taking a buffer size of 20 MB as an example, the valueof BEEINFO-D’s latency is 7521, which is nearly twice ofPRoPHET (3895) and Epidemic (3953). The average latencyof BEEINFO-S (5633) and BEEINFO-D&S (5625) are lowerthan BEEINFO-D, but they are still about 0.4 times higherthan PRoPHET and Epidemic. However, this is acceptable inmany delay-tolerant applications. Moreover, when the buffersize is more than 20 MB, the latencies or BEEINFO algorithmsstart decreasing, whereas those of Epidemic and PRoPHET areincreasing. The reason is that PRoPHET and Epidemic generatemany redundant messages, which causes more buffer replace-ment operations. In contrast, BEEINFO effectively controls the


Fig. 5. Performance over buffer size.

Fig. 6. Performance over message’s TTL.

number of the redundancy, resulting in the decline of averagelatency.

Fig. 6 shows the performance of the protocols with varyingTTLs (using 10 MB of buffer size and 400 000 s of simulationtime). As the TTL increases, the delivery ratio of messagesof all protocols decrease. When the TTL increases to 3600,all protocols’ delivery ratios are very close. For the overhead,BEEINFO-D keeps the most stable and the least. For theaverage latency, BEEINFO-D decreases dramatically from thepeak of 7735 to 5977, whereas the others change a little. As forhop count, all algorithms keep it stable.

When a message’s TTL increases, it can be stored longerin the buffer, which may improve the success delivery rate

of messages in buffer. However, it decreases the deliveryopportunities of messages in air and increases the frequencyof buffer replacement. As a result, more duplicated messagesare generated, which causes the decline in delivery rate andincrease in overhead. As a whole, BEEINFO achieves higherdelivery ratio, lower overhead, and fewer hop counts comparedwith PRoPHET and Epidemic in TTL experiments, similar towhat is shown in Fig. 5.

Fig. 7 presents the performance of the protocols in changingsimulation time (with a buffer size of 10 MB and a messageTTL of 600 min). With the simulation time ascending from50 000 to 600 000 s, BEEINFO gathers increasing environmentand social tie information. Thus, it can predict more accurately


Fig. 7. Performance over simulation time.

and make better choices. Moreover, more time is available fordelivery. Delivery ratio, overhead, and latency of all algorithmsare increasing more or less through time. When simulationtime is over 400 000 s, they are more stable. The deliveryratios of BEEINFO algorithms climb to their peaks rapidlyand are then kept stable over the simulation time. In addi-tion to delivery ratio, the other three metrics experience thesame trend of climbing to the highest value and are keptstable with the increasing simulation time. Furthermore, thesubfigures show the same result as Figs. 5 and 6 do, whichis that BEEINFO outperforms PRoPHET and Epidemic indelivery ratio, overhead, and hop count, along with the highestaverage latency. For the algorithms in BEEINFO, BEEINFO-Dis superior to BEEINFO-S and BEEINFO-D&S on deliveryratio, overhead, and hop count. The defect of BEEINFO-D isthe highest latency. The reason lies in the direct forwardingstrategy in intracommunity. BEEINFO-S and BEEINFO-D&Sstill achieve similar performance.

VI. CONCLUSION

In this paper, we have proposed a set of interest-basedforwarding schemes inspired by artificial bee colony, namelyBEEINFO, in the context of SAN. It consists of three differentprotocols: BEEINFO-D, BEEINFO-S, and BEEINFO-D&S.From a conceptual perspective, BEEINFO takes advantage ofboth biologically inspired networking and SAN. It is charac-terized with strong adaptability to the dynamic environmentby fully harnessing the cooperation of individuals, makingit suitable to mobile (social) networks such as VSNs. BEE-INFO perceives densities of passing communities and socialtie according to interests, instead of the change of individuals’interests. Mobile nodes maintain this information for distinctaims: density for inter-community forwarding and the social tiefor the intracommunity process. More precisely, BEEINFO-D

adopts density information inter-community forwarding.BEEINFO-S utilizes social tie for intracommunity forwarding.In addition, BEEINFO-D&S use both density and social tieinformation. Additionally, BEEINFO introduces the messagescheduling strategy and the buffer management algorithm toimprove the delivery rate.

We have conducted extensive simulations on BEEINFO andcompare their performance against PRoPHET and Epidemic.The results have shown that BEEINFO outperforms PRoPHETand Epidemic with higher delivery ratio, less overhead, andfewer hop counts. The major drawback of BEEINFO is theaverage latency in some cases. For the algorithms in BEEINFO,BEEINFO-D achieves better performance than BEEINFO-Sand BEEINFO-D&S on delivery ratio, overhead, and hop countwith the longest average latency.

In reality, the data’s categories might be leveled, and relation-ships between categories are complex. We intend to considerthe nodes with multiple interests and their relationships asfuture work. Moreover, the interests and social tie informationare private information, and security problems may arise. Thus,the privacy issue is another focus of our future work. Further-more, we plan to simulate and analyze them in more realisticscenarios, e.g., with real traces. Finally, it is quite necessary tofind a way to lower the latency of BEEINFO as well.

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Feng Xia (M’07–SM’12) received the B.E. andPh.D. degrees from Zhejiang University, Hangzhou,China, in 2001 and 2006, respectively.

He is currently an Associate Professor and a Ph.D.Supervisor with the School of Software, DalianUniversity of Technology, Dalian, China. He is theauthor/coauthor of one book and over 150 scientificpapers in international journals and conferences. Hisresearch interests include social computing, mobilecomputing, and cyber-physical systems.

Dr. Xia is a Senior Member of the IEEE ComputerSociety and the IEEE Systems, Man, and Cybernetics Society, and a mem-ber of the Association for Computing Machinery (ACM) and ACM SpecialInterest Group on Mobility of Systems, Users, Data, and Computing (ACMSIGMOBILE). He serves as a (Guest) Editor for several international journalsand serves as a General Chair, a Program Committee Chair, a Workshop Chair,a Publicity Chair, or Program Committee Member of a number of conferences.

Li Liu received the B.S. and M.S. degrees in com-puter science and technology from Shandong Uni-versity of Science and Technology, Qingdao, China,in 2001 and 2004, respectively. She is currentlyworking toward the Ph.D. degree with the School ofSoftware, Dalian University of Technology, Dalian,China.

Since 2004, she has been with Shandong Jiao-tong University, Jinan, China. Her research interestsinclude opportunistic networks, socially aware net-working, and mobile social networks.

Jie Li received the Bachelor’s degree in networkengineering from Dalian University of Technology,Dalian, China, in 2011. He is currently workingtoward the Master’s degree from the Mobile andSocial Computing Laboratory, School of Software,Dalian University of Technology.

His research interests include mobile social net-works, socially aware networking, and ad hocnetworks.

Ahmedin Mohammed Ahmed (S’13) received theB.S. degree in computer science from Bahirdar Uni-versity, Bahirdar, Ethiopia, in 2006 and the Mas-ter’s degree in software engineering from ChongqingUniversity, Chongqing, China, in 2011. He is cur-rently working toward the Ph.D. degree with theMobile and Social Computing Laboratory, School ofSoftware, Dalian University of Technology, Dalian,China.

From 2006 to 2011, he was a Lecturer of computerscience with Wollo University, Dessie, Ethiopia. His

research interests include mobile and social computing, ad hoc social networks,mobile data management, middleware design, and smart meeting.


Laurence Tianruo Yang (M’97) received the B.E.degree in computer science and technology fromTsinghua University, Beijing, China, and the Ph.D.degree in computer science from the University ofVictoria, Victoria, BC, Canada.

He is currently a Professor with the Schoolof Computer Science and Technology, HuazhongUniversity of Science and Technology, Wuhan,China, and with the Department of Computer Sci-ence, St. Francis Xavier University, Antigonish, NS,Canada. His research has been supported by the Na-

tional Sciences and Engineering Research Council and the Canada Foundationfor Innovation. His research interests include parallel and distributed computingand embedded and ubiquitous/pervasive computing.

Jianhua Ma received the B.S. and M.S. degreesfrom the National University of Defense Technology,Changsha, China, in 1982 and 1985, respectively,and the Ph.D. degree from Xidian University, Xi’an,China, in 1990.

He had 15 years of working experience withthe National University of Defense Technology,Changsha, China; Xidian University, Xi’an, China;and the University of Aizu, Aizuwakamatsu, Japan.He is currently a Professor with the Faculty of Com-puter and Information Sciences, Hosei University,

Tokyo, Japan. He is the author of over 200 papers and the Editor of over20 books/proceedings and over 20 journal special issues. His research interestsinclude multimedia, networks, ubiquitous computing, social computing, andcyber intelligence.

Dr. Ma is a Founder of the IEEE International Conference on UbiquitousIntelligence and Computing, the IEEE Conference on Cyber, Physical, andSocial Computing, and the IEEE Conference on Internet of Things.

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