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Social relationship enhanced predicable routing in opportunistic network

Xingguang Xie, Yong Zhang, Chao Dai, Mei Song

School of Electronic Engineering, Beijing University of Posts and Telecommunications,Beijing, China starlightxie@gmail.com; yongzhang@bupt.edu.cn;

dcdc72@gmail.com; songm@bupt.edu.cn

Abstract-Routing is one of the most challenging problems in the opportunistic network owing to the occasion-connected mobile wireless environment. To overcome this weakness, many routing protocols have been put forward to solve it by exploiting the nodes’ mobility history. Meanwhile, to parallel the current trend of the social network, some of them design the solution by utilizing the social relationship characteristics from the real world. Nevertheless, few of these works could close the gap between the two totally different meanings and improve the efficiency of the whole network based on both. In this paper, we propose social relationship enhanced predicable routing (SREP) in the opportunistic network. The whole algorithm depends on this truth- the nodes in the opportunistic network only visits some defined place because of its necessary relationship with other people, thus we could adapt the semi-deterministic Markov process to model the behavior of the node. And we also introduce PageRank algorithm to quantify social degree of node. The simulation shows that SREP is an effective routing protocol in a specific scenario based on the human motion.

Keywords- Opportunistic Network; Social Network; PageRank;

Routing; Data Dissemination

I. Introduction The opportunistic network is a fundamental promising

evolution of the Delay Tolerant Network (DTN) [1]. Both of them exists the challenging mobile wireless environment, which an end to end link between the source and the destination does not always exist. In the other words, wireless links are short-lived and end to end connectivity turns to be sporadic [2]. Thus main researches [3] in the opportunistic network attempt to make full use of this typical mobility to transfer the message and guarantee the reliable of upper application. Moreover, when the opportunistic network attempts to close the gap between human and network behavior, it becomes the social network and tries to exploit the human’s mobility and gregarious nature [4]. In opportunistic network, the date transmission is multi-hop, with intermediate nodes acting as routers that forward the messages addressed to other nodes. Routing is a challenging problem because of the temporal scheduling element in a dynamic topology. To overcome this disadvantage, the “store, carry and forward” scheme is put forwarded by scholars, which is the basis of the current routing protocol in opportunistic network. So the nodes have to decide who the next hop is, and also when to forward, as they route packets to destinations in the store-and-carry way.

Researchers have designed numerous of routing protocols for opportunistic network. Generally speaking, these routing

protocols could be divided into three groups [5]. The first one is context-oblivious routing protocol, such as Epidemic [8] and Spray-and-Wait [9]. And they seldom make use of information about the status or behavior of the devices, users, environment and social relationship. The second one is partly context-aware, such as HCR [10], these protocols calculate the probability that a node can deliver a message to a particular destination deduced by some according node information. However, they assume a specific model for the context. And the environment must match these assumptions to guarantee the performance of routing. The final one is fully context-aware, which could use social information to improve the efficiency. In the other words, it fully considers the importance of social relationship. They may not be as efficient as partially context-aware protocols in the conditions for which the latter have been designed, but thanks to learning features, they are much more adaptive. The most famous one is HiBop [11], which exploits the similarity of the nodes and yields a better performance.

In this paper, we propose the social relationship enhanced predicable routing (SREP). Researchers have got the conclusion that the nodes in opportunistic network, especially social network [12], have a deterministic mobility behavior, and they usually just roam around some certain locations, which we call community in this paper. The community can represent the classroom, lab, library and apartment and some other places in school, for example. What’s more, the community in SREP represents the true geographical location instead of human assemblage. Every node has its own preference and frequency to commute between communities, or just is located at some position [13]. In other words, the movement of node is somewhat regular. Actually, we use the probability of node mobile in SREP to represent the real phenomena. Furthermore, we know that, in the real world, different people play various roles [14] and gain the distinguish popularity in distinct groups or communities. We think this difference can be an efficient mean to exploit the drive of message and information in the opportunistic network. It is not only a potentially optimal way to improve the message delivery ratio by controlling the selection of the contact, but also to meet the feature of the human society. Based on these two conclusions, we put forward the Social relationship enhanced predicable routing in opportunistic network, short for SREP. In SREP, the semi-Markov process model the probability of node mobile among communities. Then we use the PageRank [15] from Google to evaluate the social degree of node in distinct community, which embodies

2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.72

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2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.72

268

2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.72

268

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the popularity of one person. When the source node wants to transmit a message and encounters any other nodes meanwhile, it compares the totally social degree of these nodes from the current community, which is the function of the transition probability and the degree in every other community.

II. Related work

In the past, many routing and forwarding protocols for opportunistic network have been proposed. The limitation of the early routing is that they just randomly choose the relay and replicate a few of surplus message. These design thoughts are lack of the defined goal to pursue the constrained source consume or the optimal efficiency. The epidemic routing [7] just is the most representative routing of this category. Its performance reduces drastically owning to the channel contention and other resource limitations. Considering the drawback of epidemic routing, the Prophet [16] and spray-and-wait emerged. The former considers that the nodes in the network have repeatable mobility. Thus a node is a good relay for one destination, if it has the highest probability to encounter the destination. The latter one is a kind of multi-copy routing protocol that controls the flooding overhead [17] by limiting the quality of the copy in the Spray stage and then the message is delivered directly to the destination in the Wait stage. Both of them try to overcome the faults of the Epidemic routing.

After these primary routings, by the way, many of them just evolve from the routing in DTN, the new routing protocols start to focus on more information relative with node mobility and human behavior in real world to improve the delivery rate and the efficiency of the whole network. For example, the Spray and Focus [18] protocol is the innovation of the Spray and Wait. In SW, a node with a message will directly choose a node it encounters as a relay. However, the SF protocols will decide an optimal relay node based on utility computations in which each node keeps track of the time elapsed since the nodes last met. Thus the relay will have a higher probability to transmit the message to the destination than the nodes chose randomly. This algorithm yields a significant increase of the network performance. On the other hands, the routings based on the prediction have also improved. [19, 20, 21, 22] propose a routing algorithm that uses past frequency of contacts and history of contacts. In [23], the researchers propose a routing that uses the current position and trajectories of nodes to predict their future distance to the destination. This algorithm ignites a new meaning to design the routing in opportunistic network. Some further research has observed that a node has its own mobility schedule and its path is almost deterministic.

Another meaning to design routing is the utilization of the social information [24]. [9] proposed the HCR, which distinguishes the priority of the messages. The higher priority the message has, the more source, extra cost and overhead the message could utilize to improve its own efficiency. Another classic protocol HiBOp is a history based routing protocol for opportunistic network. The main idea of HiBOp forwarding is looking for nodes that increasing match with personal data of the destination. Current context is useful to evaluate whether a

node is an optimal relay based on its instantaneous fitness. More similarity between two nodes means higher probability to encounter the destination [25]. Finally, depended on social attributes of nodes and prediction of node mobility, PER employs a semi-Markov model to predict both the probability that are two nodes will be in contact and the probability distribution of the contact time. It gains more precise predictions and reflect the true situation of the node mobility in real world.

III. System algorithm

A. Basic consideration

In opportunistic network, the feature of node follows a semi-deterministic mobility. A community is an actual spot where many nodes could assemble, and represents a geographical zone of real world. Every node would have contact when they are in the same community. The mobility of node would be included in a social network. For example, the communities would be university, office building, accommodation and hospital, as shown in figure 1. The nodes are the human being with smart phone or PDA, and follow their typical paths.

As exists in the real world, every person in one community plays a different role, thus it will lead to their distinct status [24]. The higher status a node has, the better an optimal relay it would be. SREP is a single copy routing protocol for opportunistic network. The message carried by the node is transmitted to the destination. When a node has to transmit a message from its queue, it will computers a probability social degree for all nodes in contact with itself firstly. And then the node selects a next node with a highest probability social degree. If no node is available at this time, the message will not be forwarded. The formulation of the algorithm is detailed below.

Figure 1. scenario of SREP

B. Assumption

In this article, we attempt to pursue an effective routing in the opportunistic network, considering the social relationship, which embodies social status, would be a wise alternative to enhance the efficiency of the whole network, which has been proved by [12]. Furthermore, we make some assumptions for

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our future work to simplify the complicated conditions. It includes that all of nodes can forward the message successful in the community. The community can guarantee the nodes have enough band and time to establish a reliable and durable contact, when two nodes are in the same community simultaneously. On the contrast, even the node may encounter outside the community, they couldn’t exchange the message. In the network, each community has a unique community ID. And the whole network is composed by a few of different communities. Each node, such as the smart phone and PDA, just wanders among various communities to transmit the message, and they also are aware the community which they are located currently. Moreover, each node has its own unique social degree in every community.

C. Node mobility model

Sociologists have realized that the social network displays a high degree of transitivity substantially, and if two people share some common features, they will have higher probability to be acquainted. In physics world, this responding phenomenon is called “clustering”. Watts [26] have found that the real world networks exhibit strong clustering and network transition. And the node shuttles among those clustering groups. In this paper, we imitate the mobility pattern of a node in the real world with homogeneous semi-Markov process. From the above assumption, a state of Markov chain is represented by the community (C). Node movement among different communities has the Markov memoryless property, which means that the probability of movement of node m from one community to another community is independent of the early probability in another community. So the process of a node movement can be defined as a discrete-time Markov chain.

1 1 2 0

1 0

1

( | , , ,...., ,

, ,...., )

( | , )

m m m m m mij n n n n

n nm mn n n

p P X j X X X Xt t t

P X j X i t

� � �

�

�

� �

� � �

(1) mnX means that node m stay at the community n at time

nt . mijp is the transition probability of node m from

community i to community j. The whole process in our study is divided into time slot, it also a good index to investigate the efficient of this algorithm. In SREP, every node would have a matrix of transition probability:

{ }mm ijP p�

(2)

It reflects the probability that the current node shuffles among its potential communities. But it is impossible for one node to enter all of relative community. In other words, the node may deviate from its regular path. However, after a long period, this node will detect all of its potentially neighbors owning to the semi-random mobility, and transition probability of node could be calculated based on the number of encountering. Formula (3) shows an example of transition

probability matrix for node m which roams on campus. The four communities indicate apartment, lab, classroom and refectory. When the node stays at one community, it will stay there or just pick up one destination to go.

11 12 13 14

21 22 23 24

31 32 33 34

41 42 43 44

m m m m

m m m m

m m m m m

m m m m

p p p pp p p p

Pp p p pp p p p

� �� �� �� � �� �� � (3)

If node m is at the apartment now, it may move to the lab with the probability 21

mp , the classroom with the

probability 31mp , and the refectory with the probability

41mp (figure 2). However, it cannot guarantee that the node will

meet all of its neighbors in a certain time period, thus, it is impossible that the P would cover all the transition of the node

and the 1

1N

mij

jp

�

�� is just an approximation.

Figure 2. Markov model of node’s mobility

The transition matrix would update itself in every time slot when they meet any other nodes. When the period lasts long enough, the whole matrix of node m is slightly influenced by

the new encountering nodes, and 1

Nmij

jp

�� approximately is 1.

The probability mijp that node m moves from community i to

community j is:

ijmij

i

Np

N�

(4)

Here iN is the sum of transition from community i

without considering the next community. ijN is the number of transition from community i to community j during a long enough time period. Obviously, ij iN N� and 1ijP . By

keep storing iN and ijN , each node could generate and update its own P matrix over time.

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IV. Contact probability

A. Social degree

Social information is an available and powerful augment to improve the data routing in opportunistic network. The social well-connected nodes are suited to forward the message to the destination. Generally speaking, the first thing to use the social relation to enhance the routing protocol is to extract the social degree from some limited and abstract information. However, some research [12, 14] has proved that the node with a higher social degree is an optimal chosen relay to the destination. Thus it is necessary to design an algorithm to rank the node based their social relationship. In some article, a node’s centrality in the network is put forwarded to identify bridges. Centrality in graph theory and network analysis is a quantification of the relative importance of a vertex within the graph. The centrality of a node in the network is a measure of the structural importance of the node. A central node has more potential to connect to other nodes. Furthermore, some centrality measures, such as closeness and betweenness, have been designed. However, some relative theories also are used in some other realm, which rely on the similar situation. Page rank [27] is a very famous algorithm of Google to rank web page. This technology is adopted to examine the entire link structure of the internet and locates the most important pages. The PageRank algorithm is given by the following equation:

1 2

1 2

(1 )( ) *

( )( ) ( ) ...( ) ( ) ( )

n

n

dPR A dN

PR TPR T PR TC T C T C T

�� �

� �� � �� �

(5)

Where PR(A) is the PageRank of node A, and

1 2, ,...., nT T T are the pages, and ( )nPR T is the PageRank of

page Tn that links to page A. ( )nC T is the number of the

links connected to the page nT . N is the total number of all pages that link to the current page. The PageRank of a page T is constantly weighted by the number of outbound links

( )nC T of page nT . The more outbound links a page T has, the less it will contribute to the PageRank of page A. d is a damping factor which can be set between 0 and 1. It means the probability, at any step, that a person will continue browsing the page instead of open another pages randomly. Generally, it sets to 0.85.

The PageRank can illuminate a much innovative way to extract the social relationship of the nodes in the opportunistic network. A famous and popular person means he or she has a lot of friends, in the other words, many person may recognize he. Therefore, it is easier for this famous person to look for someone else. If we assume that every two relative people has a link to connect them. The popular node (person) will bear numerous of outbound link. Although a popular node means more powerful influence, it would contribute little to other individual node’s social degree from the connected node’s perspective. Its significant influence averages to all the relative nodes. When some nodes stay at the same community,

they probably have chances to meet each other and transmit message during time Ts. If one node meets other nodes, we think there is a social graph connection between nodes. Such social graph connection is based on explicit friendships or common interests. The social graph connection

( , )i i iG V E� as an undirected link with the vertex set V and edge set E. Actually, one community just would have only one Gi for all nodes belonging to it. Nevertheless, not all the node with the same community will just in the community meanwhile. Thus, it is necessary for nodes in one community to exchange their topology information during their contacts to form their own SocialRank value. In addition, we define that only if the contact time of two nodes is greater than a constant, the two nodes would have a success and available connection in the current community. We call this constant appointment time.

( )(1 )( ) *( )

j i

ji

N G j

Sr NdSr N dN C N�

� ��� � � �� �

�

(6)

The equation above shows the SocialRank algorithm.

1 2, ,...., nN N N are the nodes in the community. ( )iSr N is the SocialRank value of node. Gi is the set of neighbor node.

( )jC N is the number of a node’s contact with other nodes in the same community. d is the damping factor, too, which can control the social relationship based on different information about the social graph connection. We assume that only the neighbor in opportunistic network could impact the social degree of one person. The higher the value of d, the greater the algorithm influences on the social relation between the nodes. In our routing, we just define d that nodes have high social degree, which means that they could encounter each other in one community. Maybe they are friends, and they may also share one or multiple common interests. In our future work, we will exploit its impact, but in this study, it equals to 1.

Figure 3. an example of Social Rank

However, compared with the PageRank in internet, there is difference between these two similar algorithms. The web page could not move unless it is canceled, so at the same time, web graphic connection will seldom change. Nevertheless, the nodes in SEPR represent the human mobility, so they could

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move, gather and spread out with their own purposes. They could just arrive at some community only based on their own schedule. Not all of nodes included in one community will stay at the current community simultaneously. It means that not all of the node in one community will have a contact with all other node in the same community. Even though, this difference will not influence the social rank of the node. The SEPR only need to update its own value after the time slot. After an enough period, the social value is approximate the actual constant value.

B. Relay selection algorithm

In this section, we propose an algorithm to determine the relay for the better network performance. A social relationship enhanced delivery probability metric is designed. Every node in the same community has a unique social degree. We could conclude this based on two considerations below. It is common sense that every person plays various roles in the different place. For example, A, B, C are the classmates in a university. In the Gym, A would be the captain of basketball team of this college, and B just is a common number of this team, while the C is totally not interest in the basketball, and seldom attends some relative activities. It is easy to know that the A with ( )ASr N is the most popular in the Gym

and ( ) ( ) ( )L L LA B CSr N Sr N Sr N� � . In the Lab, B is the

leader of the research team who will operate the program, while A and C are ordinary lab numbers. Thus the B has the highest SocialRank value owning to his role, so

( ) ( ) ( )L L LB C ASr N Sr N Sr N� � . The some similar situation

may happen to other places. On the other hands, every node encountering in the same community may have various habits to visit this community in different time with distinct durations. This specific schedule could influence when and who one node will meet, and thus social degree.

If a node is very popular, it would be an optimal relay for the destination [12]. ( )m

ijQ t is the probability of node m at time t from state i to state j. Thus the prediction correction of social degree of node with other communities is:

( ) ( )* ( )m mij ijS t Q t Sr m�

(7)

( )mijS t indicates that the actual role of a node’s social

degree to the whole network depends on the probability that another node will meet it in all of relative communities. Only one node encounters a node with higher social degree, it will be chose and forwarded a message as a relay node. In this formula, it means the prediction correction of social degree of node m in the community i currently which may get to the community j. The functions of the prediction correction of social degree are defined as follows:

Function 1 (SREP1): the total prediction correction of social degree of a node with all communities at time t is:

1,

,

max( ( ))

max( ( )* ( ))

mij

i j l

mij

i j l

S S t

Q t Sr m�

�

�

�

�

� (8)

The above formula signifies that the future importance of node m in all other communities. In SREP, when node n meets other node in the community i. It will calculate the total prediction correction of social degree for all nodes, and select a node with a highest correction. The new relay node may stay in the community, and repeat the above the process. Furthermore, it would leave this community, then and wander to other community as it wishes. After that it would forward the message to another node, if it does not meet the destination node in the new community. The new chosen relay node has a highest social degree among the nodes in this community presently, thus it will have more probability to meet the destination node.

Function 2 (SREP2): the average prediction correction of social degree of node is defined below:

2 max( ( ( )))

max( ( ( )* ( )))

mij

mij

S arv S t

arv Q t Sr m

�

� (9)

The next community the node will visit in the future is not unique, so it is probable for the relay to enter a community in which the relay has a small social degree, and it may lead to reduction of chance which relay meets the destination and the current relay has to pick up another relay with higher social degree in that community to transmit. Thus it is necessary to evaluate the average prediction correction of social degree to offset the uncertainty of the node mobility.

Relay selection algorithm just bases on the above two functions. When a node wants to transmit a message, it will use the relay selection algorithm to choose an optimal relay. If the node does not encounter a better relay before it leaves this community. The message will be stored in the queue of message for the next transmission and carried out the community by it. When the node enters into a new community based on its schedule, the node will begin a new process to look for a better relay until the message is sent to the destination. SREP would make use of the two different but relative functions respectively. We will compare the two functions below.

V. Performance evaluation

In this section, we accomplish to evaluate the SREP algorithm and contrast it with some relative prevalent routing protocol. Our aim is to investigate whether the SREP could increase the delivery ratio and performance of the network in the specific environment which imitate the real world, compared with others protocol. ONE which we used in the study is a powerful simulation tool for the opportunistic network which contains a lot of mainstream routing protocol. It also enables to generate mobility traces, with the interface for the third part to develop new routing mobility and algorithms.

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In the simulation, we also improve a mobility model in ONE. In this mobility model, there are several predefined communities in the network. Nodes always ramble around and among these communities. Nodes would stay at node community or go to other community based on their own preference. In the model, the community lies in different spots in our scenario which associate with actually geographic zone. It does not rely on the nodes’ movement. Thus, even at some special time, all nodes may leave away from one particular community. This community would not disappear. And the factors about this community also are still stored in corresponding nodes. In addition, two nodes only could communicate when both of them stay at the same community meanwhile. In this model, we assume that each node has a probability (1- p) to visit the community from its current location, and visit any other communities with the probability P/c-1, c is the total quality of the community. P varies from 0 to 0.5 with s step of 0.05, and it also notifies that the node’s movement is a kind of semi-deterministic mobility.

In our case, we simulate scenario with 8 communities and 100 nodes. And the simulation is 4000m*4000m. Initially, nodes are uniformly distributed among the communities. SREP need spend 1.5 hours to form the transition probability matrix and social degree for every node. A good routing protocol in opportunistic network should have a maximum delivery ratio and minimum delivery delay. We compare the performance of SREP, Epidemic routing, Spray and Wait, and direct routing with the different TTL, and deviation degree.

Figure 3. Delivery ratio with different deviations

Figure 4. delivery delay with different deviations

influence of the deviation

Figure 3 and 4 records the delivery delay and delivery ratio under different deviations. Although the two SREP algorithms are designed to be fit for the social mobile network, their performance of delivery delay is better than other routing protocol except the Epidemic routing. However, with the increase of the deviation, the SREPs’ delay increases and delivery ratio reduces, which become closer to others routing protocol. It is because that the more deviation means nodes move more dynamically and irregularly, and it may cost more time to transmit the message. So the regular mobility will help improve the delivery ratio. However, if p (deviation) is set to some constancy, the performance of SREP is better than other routing protocol except the Epidemic routing. Overall, the efficiency of SREP algorithms is acceptable, when the randomness of the node deviation is lower. We also could conclude that deviation almost does not affect the performance of other routing protocol.

Figure 5. delivery ratio with different TTLs

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Figure 6. delivery delay with different TTLs

influence of the TTLs(time to live)

Figure 5 and figure 6 summaries the delivery ratio and delivery delay of all routing protocol under different TTLs. Obviously, the longer TTL is, the higher delivery ratio the routing protocol have. In this scenario, we set the deviation p= 0.3. As we talk above, when the TTL is small, all routing protocols have a bad performance on the delivery ratio and delay. And when the TTL is longer enough, the performance of every routing improve. We also could the SREP yields an excellent efficiency of this special opportunistic network, considering the TTL influence.

VI. Conclusion

In this paper, we combine the social attribute of node mobility of social network with the prediction based routing, and propose the SREP, Social relationship enhanced predicable routing in opportunistic network. We employ an adapted discrete Markov chain to model pattern node in social opportunistic network, and introduce a social rank algorithm to evaluate the importance of the human’ status. We also define two selection functions to help to choose an optimal relay node for message delivery. SREP makes full use of the feature of human society, and coincides the mobility of the human mobility. Therefore, it could improve the performance of opportunistic network owning to better meet the characteristic of social network. Simulation results show that SREP can yield the improvement of the delivery ratio and reduce the delivery delay in some defined scenario.

Our future work remains focus on the combination of the social degree and prediction routing. We will deeply emphasize the difference of the social degree in different community. The random variation of the social degree may be a better drive of delivering message. We also plan to enhance the performance SREP and evaluate it on our campus.

Acknowledge

This relative work is supported by National Natural Science Foundation of China under Grant NO. 61171097 and Chinese Universities Scientific Fund (No.2009RC0304).

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