International Journal of Computer Engineering and Applications, Volume XII, Special Issue, August 18, ISSN

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1 International Journal of Computer Engineering and Applications, ANALYZING IMPACT OF FACTORS ON ROUTING DECISIONS IN OPPORTUNISTIC MOBILE NETWORKS Sonam Kumari 1, Dr. Itu Snigdh 2 Department of Computer Science and Technology, Birla Institute of Technology, Mesra, Ranchi, India. 1. aa.sonamkumari@gmail.com 2. itusnigdh@bitmesra.ac.in Abstract With the proliferation of sensor networks and mobile ad hoc networks, we have a plethora of intelligence equipped smart devices that are heterogeneous in nature and need to communicate frequently over the network. Also, for such devices, the basic requirement is supporting mobility and ubiquitous computing. Opportunistic (self-organizing) networking (OMNs) may be considered as a remarkable concept and the first step towards realizing these requirements. Current literature suggests a set of forwarding protocols evaluated on the basis of mobility, contacts and traffic patterns and reliability concerns. These constraints increase the burden of successful communication and affect the energy reserve of the participating nodes. Thus, the efficiency of the forwarding mechanism or the routing scheme and ad hoc connections is related to how intelligently the node decides the forwarding candidate. This article analyzes the impact of social behavior, node remaining energy, and node speed and delivery probability on opportunistic networks. It also designs a framework that can be adapted to predict the connectivity and message delivery reliability and thus work towards improving it. The framework essentially considers a type II fuzzy rule-based intelligent system that chooses the best candidate for data delivery dependent on a belief value. Index Terms Contact duration, Inter-contact time, Opportunistic Mobile Network (OMN), Relay node. I. INTRODUCTION The opportunistic network [1] is the evolution of mobile ad hoc networks (MANET) where nodes are wirelessly connected. However, contrary to MANETs the connections between the nodes are not stable and break as quickly as discovered. The communication in opportunistic network takes place on the establishment of opportunistic contacts among mobile nodes as an end to end path does not exist. Sonam Kumari, Dr. Itu Snigdh 1

2 ANALYZING IMPACT OF FACTORS ON ROUTING DECISIONS IN OPPORTUNISTIC MOBILE NETWORKS Since no fixed connection between two nodes exists, the routes build here are highly dynamic and so we cannot use traditional routing strategies in opportunistic network efficiently. Moreover, opportunistic network works in an environment where lengthy delays and error rate are tolerant. There are several types of research going on the opportunistic network as in near future the number of mobile devices equipped with Wi-Fi, Bluetooth, sensors, camera etc is going to increase rapidly. The advantage of this situation is that we have a number of available mobile devices in an environment and hence the contact opportunities would likely increase which is very useful in establishing an opportunistic network. An OMN consists of a source node which generates the message forwards the message to its intermediate nodes such that the intermediate node brings the message closer to the destination node. The forwarding paradigm used in OMN is based on store-carry-forwarding. As the link performance is highly variable the node stores the message on its buffer until it comes in contact with the next hop and forwards the message if it discovers the intermediate node. and knowledge-based. Some of the commonly used approaches are enumerated as direct transmission [8], Location-based approach [7], Knowledge-based [7], context-aware routing (CAR) strategy [4], MaxProp [10] and Shortest expected path routing (SEPR) [12]. B. Flooding-Based Approach A flooding-based routing protocol is a multiple copy based strategy. Significant works include Epidemic routing [6] which uses the epidemic algorithm [11] and the Spray and Wait scheme [9] containing two phases i) spray phase and ii) wait phase. In spray phase, N number of copies is generated by the node and is spread randomly to its neighbors present at one hop count distance. In wait phase, if the destination was not found in spread phase the nodes will perform direct transmission. Another wellknown algorithm is PROPHET [13] based on a prediction routing protocol using history of encounters and transitivity. Table I illustrates the comparisons between different routing protocols of OMN. The comparative analysis has been based on parameters like buffer management, estimation of forwarding probability, reliability, and energy efficiency. II. EXISTING ROUTING PROTOCOLS A number of routing protocols have been proposed till now to deal with the highly dynamic environment of the opportunistic mobile network. The two main categories in which we can divide routing protocols are a) forwarding based approach and b) flooding based approach. Forwarding based approach is a single copy approach in which only one copy of the message is created and forwarded to the destination node using intermediate nodes. But in flooding based approach multiple copies of the message is created and broadcasted to its neighbors. The major drawback of forwarding based approach is the latency delay and the low delivery ratio but the buffer required is decreased as well as the traffic is reduced and hence the required is less as compared with flooding based approach. And the major drawback of flooding based approach is that the network traffic increases and hence the requirement increase, as well as the buffer space required, is more than forwarding based approach but the delivery latency decreases. A. Forwarding Based Approach The forwarding based approach can be classified into three main categories: direct transmission, location-based III. FACTORS AFFECTING BELIEF In an opportunistic mobile network, the key problem is to choose the appropriate relay nodes to transfer the message in multi-hop scenario, such that the delivery latency is decreased, and reliability is increased. The impact of social behavior, node remaining energy, node speed and delivery probability on opportunistic networks is analyzed in this section. Based on the estimation of these parameters, belief is been computed for each node TABLE I COMPARISON BETWEEN ROUTING PROTOCOLS Protocols Direct transmission Buffer Managem ent Estimation of Forwarding Probability Reliability Infinite No reliable, may incur long delays MoVe Infinite Yes, using the motion vector CAR Infinite Yes, using kalman filter MaxProb Yes, no of hop counts Yes, estimating the delivery likelihood Energy efficiency than epidemic than epidemic than epidemic if buffer size is small than CAR(higher delivery rate) than an epidemic Bandwidth than CAR Sonam Kumari, Dr. Itu Snigdh 2

3 SEPR International Journal of Computer Engineering and Applications, Removes packet with smaller EPL Yes PROPHET Infinite Yes, using delivery probability vector than epidemic(almost 35 percent) than an epidemic and we choose the best candidate node for data delivery dependent on a belief value. Figure.1briefly explains the prediction mechanism. In the figure, S represents the source node and D, the destination node. All nodes including the source and destination are constantly moving with variable speeds and belong to a particular reference region momentarily as shown by the irregular boundaries. Every node stores the frequently encountered nodes statistics namely, the frequency of encounter, the number of messages delivered to it, the duration time when they were in contact and then computes a belief value according to these parameters. This status table is frequently updated with every message delivery. A. SOCIAL REFERENCE than epidemic One of the characteristics of an OMN is that it is tightly (almost 50 percent) than an epidemic Fig. 1. Schematic diagram of OMNs forwarding technique based on Belief and NRE value. The figure depicts that initially, node 2 and node 3 is in the transmission range of node S. Source node stores an updated table of belief and node remaining energy (NRE) parameter values of 2 and 3 based on past experiences. As we see that the belief and NRE value of node 2 is more than node 3, the source node forwards the packet to node 2. Similarly, node 2 will transmit the packet to node 4 since the belief and NRE value of node 4 is greater than node 1. Now, since node 4 comes in the transmission range of destination node D, it will directly transmit the packet to D. The article proposes computation of a factor belief before forwarding data packets to the relay nodes. The belief is further computed on the basic environmental conditions and is stored and updated by each node. coupled with human social behavior. Social behavior in this article is computed based on the value of betweenness centrality [5]. Betweenness centrality of a node is the ratio between the numbers of shortest path that passes through that node to the total number of the shortest path. Betweenness(ѵ) = s ѵ t σ st σst (ѵ) (1) Where, σst is the total number of shortest paths from node s to node t and σs(ѵ) is the number of those paths that pass through ѵ. B. DELIVERY PROBABILITY The two factors, contact duration (CT) [2] and intercontact time (ICT) [2], are important parameters in determining the capacity of an opportunistic network. We infer that the delivery ratio (DR) is directly proportional to contact duration and indirectly proportional to inter-contact time. Contact duration is the time duration during which the devices are in contact with each other. Intercontact time is the time interval after which the two devices will come in contact with each other. Contact Duration DR α (2) Inter Contact Time C. NODE REMAINING ENERGY Node remaining energy (NRE) parameter depends on the number of packets it transmits and receives and the inter-probe time [3]. As a node decreases in power, the likeliness of the node suffering downtime increases. Inter probe is the time interval after which the device will do neighbor discovery again. Here, B is the movement speed at which the device is traveling in meter per second. R is the transmission radius of the device in the meter. The variable α lies within the range 0 < α 1. The parameter α specifies how close devices should be to each other in relation to a maximum uniform transmission radius before being classified as neighbors. 1 NRE α (3) No of packets received and transmitted ESend(k, d) = Eelec k + εamp k dij 2 (4) ERecv(k) = Eelec k (5) Where Eelec is the coefficient of radio frequency, εamp is the amplification coefficient of sending the device and dij is the data transmission radius of the node. 1 Sonam Kumari, Dr. Itu Snigdh 3

4 ANALYZING IMPACT OF FACTORS ON ROUTING DECISIONS IN OPPORTUNISTIC MOBILE NETWORKS Inter probe time (τ) = (6) αfs 2B fs = (7) R D. NODE SPEED Since the mobile devices are carried by the user, the node speed parameter is a stable value and depends on the mode of transportation of the user. In this article the speed of a walking person is assumed to vary from 0.5 to 1.5 meter per second, the speed of person traveling by a vehicle is assumed to vary from 8.3 to 30 meters per second and of a cyclist is assumed to vary from 4.2 to 11 meters per second. IV. PROPOSED SYSTEM MODEL Fig. 2. Proposed system model based on BBN V. IMPACT OF PARAMETERS ON BELIEF USING FIS Since the parameters considered in this article does not have a certain value, fuzzy inference system is used to deal with this uncertainty. Using a type II fuzzy rule-based intelligent system belief value is computed in this section. The impact of delivery ratio, node remaining energy and social reference on belief is shown in the Fig. 4, Fig. 5 and Fig. 6. Cases Fig. 5. The impact of social reference on the belief TABLE II THE IMPACT OF DELIVERY RATIO ON BELIEF Social Reference Node remaining energy Description Case-1 Low Low Belief is low Case-2 Low Medium Belief is medium when Case-3 Low High Belief is medium when Case-4 Medium Low Belief is medium when Case-5 Medium Medium Belief is medium Case -6 Medium High Belief is medium Sonam Kumari, Dr. Itu Snigdh 4

5 International Journal of Computer Engineering and Applications, Case -7 High Low Belief is medium Case 8 High Medium Belief is medium initially and high probability increases Case 9 High High Belief is high TABLE III THE IMPACT OF NODE REMAINING ENERGY ON BELIEF Cases Social Delivery Description reference Probability Case-1 Low Low Belief is low Case-2 Low Medium Belief is low Case-3 Low High Belief is medium when Case-4 Medium Low Belief is medium when Case-5 Medium Medium Belief is medium when Case -6 Medium High Belief is medium Case -7 High Low Belief is high when Case-8 High Medium Belief is high when Case-9 High High Belief is high when THE Cases Node Remaining Energy TABLE IV SOCIAL REFE ON Delivery Ratio BELIEF Description Case-1 Low Low Belief is low Case-2 Low Medium Belief is low Case-3 Low High Belief is medium Case-4 Medium Low Belief is medium Case-5 Medium Medium Belief is medium Case -6 Medium High Belief is high Case -7 High Low Belief is high Case-8 High Medium Belief is high Case-9 High High Belief is high VI. CONCLUSION In the article, the impact of social reference, node remaining energy and delivery probability on belief has been analyzed based on which intermediate node is selected for data transmission. Table II gives the description of different cases of social reference and node remaining energy and its impact on belief is illustrated when the delivery ratio is varied. Similarly, Table III describes different cases of social reference and delivery ratio and its impact on belief when node remaining energy is varied and Table IV describes all the cases of delivery ratio and node remaining energy and its impact on belief when social reference is varied. From the graph, we can say that the influence of social reference on belief value is less than the other parameters. Thus the node whose node remaining energy and delivery ratio is more becomes the eligible candidate for data delivery. REFERENCES [1] Conti, Marco, Silvia Giordano, Martin May, and Andrea Passarella. (2010, Sep). From opportunistic networks to opportunistic computing. IEEE Communications Magazine. [Online]. 48(9),pp Available: [2] Pelusi, Luciana, Andrea Passarella, and Marco Conti. (2006, Nov). Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Communications Magazine. [Online]. 44(11), pp Available: [3] M. Orlinski, N. Filer. (2014,Aug). Neighbour discovery in opportunistic networks. Ad hoc Networks. [Online]. 25, pp Available: [4] Musolesi, Mirco, and Cecilia Mascolo.(2009 Feb). CAR: Context-aware adaptive routing for delay-tolerant mobile networks. IEEE Trans Mobile Computing [Online]. 8(2), pp Available: [5] Liu, Tong, Yanmin Zhu, Ruobing Jiang, and Bo Li.,"A sociality-aware approach to computing backbone in mobile opportunistic networks." in GLOBECOM, 2015, pp [6] A. Vahdat and D. Becker, Epidemic routing for partially Connected ad hoc networks, In Duke University, Technical Report CS ,2000. [7] J. LeBrun, C.-N. Chuah, D. Ghosal, and M. Zhang, Knowledgebased opportunistic forwarding in vehicular wireless ad hoc networks, in VTC, 2005, pp [8] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, Singlecopy routing in intermittently connected mobile networks, In IEEE SECON,2004, pp [9] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, Spray and wait: An efficient routing scheme for intermittently connected mobile networks, In Proceeding of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking WDTN 05, pp , Sonam Kumari, Dr. Itu Snigdh 5

6 ANALYZING IMPACT OF FACTORS ON ROUTING DECISIONS IN OPPORTUNISTIC MOBILE NETWORKS [10] Burgess, John, Brian Gallagher, David Jensen, and Brian Neil Levine. "Maxprop: Routing for vehicle-based disruption- tolerant networks.",in INFOCOM,2006, pp [11] Demers, Alan J., Daniel H. Greene, Carl H. Hauser, Irish, John Larson, Scott Shenker, Howard E. Sturgis, Daniel Wes Swinehart and C. Douglas B. Terry. Epidemic Algorithms for Replicated Database Maintenance. in PODC, 1987, pp [12] Tan, Kun, Qian Zhang, and Wenwu Zhu. "Shortest path routing in partially connected ad hoc networks." in GLOBECOM'03,, 2003, pp [13] Lindgren, Anders, Avri Doria, and Olov Schelen. "Probabilistic routing in intermittently connected networks." in Service Assurance with Partial and Intermittent Resources, 2004, pp Sonam Kumari, Dr. Itu Snigdh 6

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