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1 Available online at ScienceDirect Procedia Computer Science 57 (215 ) rd International Conference on Recent Trends in Computing 215 (ICRTC-215) Location Information and Inter-Contact based Routing approach for Delay Tolerant Networks Savita a*, Prof. D.K. Lobiyal b a* Research Scholar, Jawaharlal Nehru University, New Mehrauli Road, New Delhi 1167, India b Professor, Jawaharlal Nehru University, New Mehrauli Road, New Delhi 1167, India Abstract Delay tolerant networks (DTNs) are often susceptible to the problem of high delay. Quite often, the major reason behind the high delay in the network is lack of complete path from source to destination. One of the greatest challenges faced by DTN is to enable end to end communication in heterogeneous environment that possesses severe performance impairments. The DTN routing problem, amounts to a constrained optimization problem where edges may be unavailable for extended periods of time and storage constraints exists at each node. The proposed protocol aims at minimizing the energy required for communication by reducing several overheads. It determines the delivery probability through each of its neighbours and delivers copies of message accordingly. The proposed protocol directionalizes the delivery of packets according to the location information of the destination. This study will focus on directional dissemination of messages which results in forwarding small number of messages. 215 The The Authors. Published Published by Elsevier by Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Computing 215 (ICRTC-215) Computing 215 (ICRTC-215). Keywords: Delay Tolerant Networks (DTNs), Power Control, Oportunistic Nerworks, Overhead, 1. Introduction Wired and wireless devices are able to communicate directly with each other when they fall within each other transmission range. Delay Tolerant Network (DTN) is basically dealing with frequent disconnection and disruption in connectivity among nodes in the network. This causes high delay in transmitting data to the destination. DTN addresses the issues of disconnection arises in the heterogeneous networks. This issue underpins the popularity of the DTN among the research community. To maintain the high reliability of network in the presence of frequent topology changes attract researchers to explore the area. DTN have so many applications like Wildlife Monitoring, Village Networks, Military Applications, and disaster response networks etc. [1]-[3], which provides an extensive scope of research. The requirement of resource depends on types of application area. For example, in disaster response networks where resources/infrastructures are disrupted or destroyed. It becomes challenging to establish communication in such scenario. In this network battery power plays vital role in increasing the life time of the network. Hence, battery The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Computing 215 (ICRTC-215) doi:1.116/j.procs

2 1368 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) power needs to be used efficiently and for that an energy efficient routing protocol should be designed for communication. Therefore, energy efficient routing schemes where system performance is not reduced are still an open issue in DTN. As discuss in previous there is no end to-end connectivity in DTN. Factors such as link failures, discharged batteries may be the reason of frequent disconnection in DTN. Message routing and node mobility have a great impact on energy consumption. The proposed work is to incorporate location information with may further reduce the requirement of energy consumption in communication. This study focuses on directional dissemination of messages with delivery probability of each of its neighbours which may result in forwarding small number of copies of a message. Some experimental results show that power consumption is very less in sleep and power off states and in idle state consumption is almost as high as in receive state. Some research work that deals with power consumption only considers the energy consumed in data transmission and tried to reduce the number of packets in the network. Obviously it degrades the system performance. According to S. Yang et al.[4], although the transmission consumes more energy as compared to receiving and idle listening, idle listening mode also consumes lot of energy as aforementioned. Mobile nodes spend most of its time in an idle listening mode searching for potential neighbours in DTN. Therefore, energy used in other states than data transmission cannot be ignored. On the contrary, in DTN, most of the energy is consumed just for contact discovery [5]. Therefore, in order to use energy efficiently one may have to limit not only to transmission, but also number of nodes that are active each time. There are two ways to reduce energy consumption in DTN [5], one is known as reduce forwarding and other is reduce beaconing. Beaconing is a periodic signalling for the node discovery for message reception. The rest of the paper is organized as follows. Section 2 gives a brief explanation about related work and ongoing research in the area of DTN routing. Section 3 elaborates the proposed network model and the proposed algorithm is discussed in Section 4. Further, the simulation setup and results are discussed in Section 5. Finally, Section 6 concludes the work and gives future directions. 2. Related Work There has been a considerable amount of work in the area of DTN routing addressed in[1],[2],[6],[7],[8],[9],[1][11], and [12]. There exist many routing algorithms for DTN, but only some of them consider energy constraint. Some researches proposed framework for DTN based on its characteristics. S. Jain et al. [3]proposed number of algorithms depending on the amount of knowledge about the traffic demand and topology characteristics there are four knowledge oracles, based on given oracle availability. They designed zero knowledge, partial knowledge, and complete knowledge algorithms. If complete knowledge is available, the mobile node schedules the discovery of contact and save energy to the maximum extent[6]. Another type of protocols based on social feature. Social feature based routing algorithm [13]-[17] are based on positive and negative features such as community, centrality, similarity, friendship and selfishness. These properties are used to build a social graph which is the most popular way to study social relationships. It is a global mapping of nodes and their relationship. An edge in contact graph represents the past encounter between the nodes. S. Merugu et al. [12] proposed Space and Time routing protocol. This protocol minimizes end-to-end delivery delay. A. Vahdat et al.[18]designed epidemic routing protocol, which uses the flooding method when two nodes come into the range of each other. Spray and Wait [19] approach which is a combination of epidemic and direct algorithms. Instead of blindly forwarding message to all or some nodes, the forwarding decision according to history based routing is made on predictable fashion. One example of this type of approach is proposed by Anders L. et al. [2] called probabilistic-routing protocol (PROPHET). C. Becker et al.[21]proposed World Model Based Routing (WMBR) approach. WMBR uses world models for better selection of relaying nodes and to find destination location without the use of flooding. World model has location information about the user profiles, building charts road map s etc. The mobility of data mules is controlled in some in an important category of DTN routing schemes to deliver messages among other nodes.

3 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) Message ferrying [22]is an example of such protocols M.Y.S. Uddin et al. [6] proposed ICR. This protocol takes advantage of recurrent mobility and contact patterns which are naturally present in the network. In ICR, the major objective is to identify more reliable routes and control replication, and to forward the messages depending on the estimation of route delay and delivery probability. 3. Network Model Each node maintains a set of neighbours which it encounters. We use intercontact graph instead of encounter graph. In intercontact graph, vertex represents an encounter between two nodes and an edge represents a delay between two encounters. Each edge, ab ac maintained by two values ( (ab ac),( σ 2 ac)), where (ab ac) is the average delay elapsed on node a between meeting nodes b and c,and ),( σ 2 ac) is the corresponding delay variance. Path in intercontact graph is represented as contact node for example (ab d) is a path from node ab to d. Suppose d is the destination node. Path delay represents as δ (ab d) and path variance as σ 2 (ab d).these are sum of edge delays and variances respectively[6]. 4. Routing Algorithm The entry for destination d at vertex ab consists of the path delay and variance. This entry is different from typical (eg. wired network) network entry, where we have only one value per destination. In the proposed algorithm, both values are considered as a parameter summary of the delay distribution. The delay distribution is used to determine the message delivery probability. Message independent path cost is defined as (1) This cost shows favour towards those paths with has less delay and delay variance. Each vertex ab in intercontact graph associated with two routing tables, one at node a and other at node b. When contact between two nodes a and b occur, they update each other s routing table for vertex ab. Considers a node b that comes in contact with node a. Node b recomputed its optimal paths to each of the destination and share it with node a. For every neighbour l N j (N j is set of neighbours of b) the mean, delay, variance and cost are first computed. (2) (3) (4) Then the optimal cost is found as: = arg (5) Node b now send delay l * and var l * to node a as a mean delay and variance of the destination w via b(optimal path). Node a update its routing table with d(ab d)=delay l and σ 2 (ab d) = var l. Similarly, node a updates routing table of node b [6].

4 137 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) When node a meets node other than destination, it forwards some copies to the encountered nodes. Suppose node a has a message with L copies to convey to w, and it comes in contact with its neighbour b. The node update time-to-live (TTL) field of the message. Later, it determines the delivery probability P c through each of its neighbours c, as follows; P c = P{< delay TTL /delay>} Where ф(.) is the Corresponding Distribution Function of normal distribution. Further, neighbours are logically arranged by delivery probability P c in descending order. Each neighbour c is allocated P c * L copies and these allotments are subtracted from L. This continues until all neighbours have been considered or L runs out [6]. We used directional delivery of packets towards the destination. It finds the location of the destination and then transfers the copies of the message to those who are in the direction of the destination. The incorporation of location information in the above approach and further reduces the energy consumption in communication. Our proposed scheme focused on directional forwarding of messages which results in a forwarding small number of messages. (6) Fig. 1 Location Calculation [28] As shown in Fig. 1 according to the location of the destination, calculation of area (Request zone) is possible[27]. The nodes present in this area have more chances of delivery. Therefore, in our approach we only forward the copies from the nodes which fall this area. This reduces the number of copies in the network and also the communication energy. 5. Simulation Setup and Results We used simulator THE ONE. This is a simulator written in Java developed for DTN. This section presents various experiments conducted to establish the efficiency and effectiveness of proposed algorithm. Here, we have evaluated the performance of the proposed Intercontact Routing using Location Information with other

5 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) protocols such as epidemic, spray-and-wait and. The comparative analysis between the proposed protocol and the existing protocols with similar simulation parameters are considered. We have evaluated different protocols with Map Based Movement model (MBM) because it captures the recurrent patterns efficiently. MBM maps data of roads and streets. In all simulation, a warm up period of 1 seconds is used. In our evaluation we focused on overhead because high overhead shows the high energy consumption. We use the (45m, 34m) area and the creation interval is seconds. It means creation of a new message in every 25 to 35 seconds. The time to live (TTL) of the messages created by the host group is 3 minutes and the size of the messages is in between 5KB- 1MB. We used Bluetooth interface for all nodes and the transmission speed is 25kbps. Delivery probability No of Nodes epidemic (a) overhead epidemic No of Nodes (b)

6 1372 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) Fig. 2. Varying Number of nodes (a) Delivery probability (b) overhead Delivery probability Buffer Size (a) epidemic 12 1 overhead epidemic snw prophet Buffer Size (b) Fig. 3. Varying Buffer Size (a) Delivery probability (b) overhead

7 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) Delivery probability No.of Initial Copies epidemic Overhead (a) No. of Initial Copies Epidemic (b) Fig. 4. Varying No. of initial copies (a) Delivery probability (b) overhead We perform three group of simulation with varying node density, buffer size and number of initial copies of messages as shown in Fig. 2, 3, 4 respectively. First, we discuss the comparative results of different protocol with varying node density. For this simulation we use the buffer size of 5M, that means buffer have a capacity to store 5 messages at a time. Number of initial copies for messages is 35. From the results, it is visible that there has been decrease in the overheads of our protocol except prophet in case of number of nodes 1, 2, 3, 4, 6 and 7 as shown in Fig. 2(b), which means for dense networks the proposed protocol performs better than the all other protocols. This happens due to the less number of copies in the network for messages. The proposed protocol sent messages only to better nodes. Fig. 2(a) shows delivery probability versus number of nodes. From the results, it is visible that proposed protocol has equal or reduced delivery probability. Second simulation presents the results with varying buffer size. This simulation we use 1 nodes and 35 initial copies for message. From the result it is visible that there has been decrease in the overhead of our protocol in all the cases as shown in Fig. 3(b). Fig. 3(a) shows delivery ratio versus buffer size. The proposed protocol has equal or reduced delivery probability with the same reasons as discussed above. Third simulation presents the results with varying number of copies. In this simulation we use 1 nodes and 5M buffer capacity. From the result, it is visible that there has been decrease in the overheads of our protocol except prophet in case of number of initial copies 15 and 7 as shown in Fig. 4(b). Fig. 4(a) shows delivery ratio

8 1374 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) versus buffer size. The proposed protocol has equal or reduced delivery probability the same reasons as discussed above. 6. Conclusion In spite of extensive research in the domain of Delay tolerant network in academics, it still faces several challenges. The key challenging issue among them is the requirement of energy. Some routing protocols focused on minimizing of energy. The work proposed in this paper aims at improving the energy for DTNs by reducing overhead. It determines the delivery probability through each of its neighbours and delivers copies of message to each of its neighbours accordingly incorporating location information. For the simulation purpose, we have used THE ONE simulator which is meant for opportunistic networks. We have evaluated the performance of protocols by taking delivery probability and overhead as statistical measures. It is observed that proposed protocol is reduced overhead in comparison to existing routing protocols. Further, we also have given attention to various other important factors like delivery ratio. As suggested by M.Y.S. Uddin et al.[6], this work can further be extended to propose a solution where network itself obtains number of copies by learning from the network. Other location based schemes can also be explored that may provide better direction of delivery towards the destination and therefore, may further reduce the communication energy consumption. Further, emphasis can be given on improvement of other important metrics to enhance delivery probability of proposed protocol. References [1] S. Jain, K. Fall, R. Patra, Routing in a Delay Tolerant Network, ACM SIGCOMM, 24. [2] A.E.Al Fagih and H.S Hassanein, Routing schemes for Delay-Tolerant Network- An Applications Perspective Technical Report , no. December 21, 212. [3] M.Y.S.Uddin, H.Ahmadi, T. Abdelzaher, Intercontact Routing for Energy Constrained Disaster Response Networks, IEEE, TMC,213. [4] S.Yang, C.K.Yeo, F.B.S.Lee, Cooperative Duty Cycling For Energy-Efficient Contact Discovery in Pocket Switched Networks, IEEE, TVT, 213. [5] E.Altman, A.P.Azad, T.Başar, Combined Optimal Control of Activation and Transmission in Delay-Tolerant Networks, IEEE/ACM, TNET, 213. [6] Y. S. Uddin, N. G. Ave, D. M. Nicol, and R. H. Kravets, A Post-Disaster Mobility Model for Delay Tolerant Networking, pp , 29. [7] Z. and D. S. Zhang, Routing in Intermittently Connected Mobile Adhoc Networks and Delay Tolerant Networks: Overview and Challenges, pp , 26. [8] P. Puri, A Survey Paper on Routing in Delay-tolerant Networks, pp , 213. [9] J.Shen, S.Moh, I.Chung Routing protocols in Delay-Tolerant Networks: A comparative survey, ITC-CSCC, 28. [1] E.P.C. Jones, L. Li, J.K.Schmidtke, P.A.S.Ward, Practical Routing in Delay-Tolerant Networks, IEEE Transactions on Mobile Computing, 27. [11] Z.Zhang, Routing in intermittently connected Mobile Adhoc Networks and Delay-Tolerant Networks: Overview and Challenges, IEEE CS&T, 26. [12] S. Merugu, M. Ammar, E. Zegura Routing in Space and Time in Networks with Predictable Mobility, GIT&CC, 24. [13] D. W. McMillian, D. M. Chavis, Sense of Community: A Definition and Theory, Journal of Community Psychology, vol. 14,no.1,pp.6-23, [14] P. V. Marsden, Egocentric and Sociocentric Measure of Network Centrality, Social Networks, vol. 56, no.4, pp , 25. [15] E. M. Daly, M. Haahr, Social Network Analysis for Routing in Disconnected Delay-Tolerant Manets, in MobiHoc 7 Proc. 8 th ACM International Symposium on Mobile Adhoc Networking and Computing, 27. [16] M. McPherson, L. Smith-Lovin, J.M. Cook, Birds of a Feather: Homophily in Social Networks, Annual Review of Sociology, Vol.27, pp , 21. [17] P. Hui., K. Xu, V. O. Li, J. Crowcroft, V. Latora, P. Lio, Selfishness, Altruism and message Spreding in mobile Social Networks, in Proc. 1 st IEEE International Workshop on Network Science for Communication Networks, April 24,29.

9 Savita and D.K. Lobiyal / Procedia Computer Science 57 ( 215 ) [18] A. Vahdat, D. Becker, Epidemic Routing for Partially Connected Ad Hoc Networks, Tech. Rep., Department of Computer Science, Duke University, NC, 2. [19] Thrasyvoulos,K.Psounis, C.S.Raghavendra, Spray and Wait: An Efficient Routing Scheme for Intermittently Connected Mobile Networks, ACM SIGCOMM, 25. [2] A. Lindgren, A. Doria, O. Schelen, Probabilistic routing in intermittently connected networks, ACM SIGMOBILE CCR, 23. [21] C.Becker, G.Schiele, New Mechanisms for Routing in Ad Hoc Networks through world models, 4th Plenary Cabernet Wksp., Pisa, Italy, 21. [22] W. Zhao, M.Ammar,E. Zegura, A Message Ferrying Approach for Data Delivery in Sparse Mobile Ad Hoc Networks, MobiHoc 4, ACM, 24. [23] P. Hui, J. Crowcroft, How Small Labels Create Big Improvements, in International Workshop on Intermittently Connected Mobile Adhoc Networks in Conjunction with IEEE PerCom 27, March 19-23, 27. [24] P. Hui, J. Crowcroft, E.Yonek, Bubble Rap: Social Based Forwarding in Delay Tolerant Networks, in Proc. 9 th ACM International Symposium on Mobile Adhoc Networking and Computing, May, 28. [25] W. Gao, Q Li, B. Zhao, G. Cao, Multicasting in Delay Tolerant Networks: A Social Network Perspective Networks, in MobiHoc 9: Proc. 1 th ACM International Symposium on Mobile Adhoc Networking and Computing, 29. [26] Y. Zhang, J. Zhao, Social Network Analysis on Data Diffusion in Delay Tolerant Networks, in MobiHoc 9: Proc. 1 th ACM International Symposium on Mobile Adhoc Networking and Computing, 29. [27] E. Bulut, B.K. Szymanski, Friendship Based routing in Delay Tolerant Mobile Social Networks, in Proc. IEEE Global Telecommunication Conference., Dec, 21. [28] Y. Ko and N. H. Vaidya, Location-Aided Routing ( LAR ) in mobile ad hoc networks, vol. 6, pp , 2. [29] A. Mei, J. Stefa, Give2get: Forwarding in Social Mobile wireless Networks of Selfish Individuals, in ICDCS 1 Proc. 21 IEEE 3 th International Conference on Communications and Networking in China, Aug, 28. [3] Y. Wang, W.S. Yang, J. Wu, Analysis of a Hypercube Based Social Feature Multipath Routing in Delay Tolerant Networks, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 9, 213. [31] J. Wu, Y. Wang, Hypercube Based Multipath Social Feature Routing in Human Contact Networks, IEEE Transactions on Computers, vol. 63, no. 2, 214. [32] Z. Li, H. Shen, SEDUM: Exploiting Social Networks in Utility Based Distributed Routing for DTNs, IEEE Transactions on Computers, vol. 62, no. 1, 213. [33] F. Li, C. Zhang, Z. Gao, L. Zhao, Y. Wang, Social Feature Enhanced Group Based Routing for Wireless Delay Tolerant Networks, in Proc.8 th IEEE International Conference on Mobile Adhoc and Sensor Networks, 213. [34] A. Keränen, T. Kärkkäinen, and J. Ott, Simulating Mobility and DTNs with the ONE (Invited Paper), J. Commun., vol. 5, no. 2, pp , Feb. 21. [35] A. Keränen, J. Ott, and T. Kärkkäinen, The ONE simulator for DTN protocol evaluation, Proc. Second Int. ICST Conf. Simul. Tools Tech., 29.

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