Volume 4, Issue 3, March 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Performance Evaluation of Optimized Routing Technique in Adhoc Networks Archna Rani [1], Dr. Manu Pratap Singh [2] Research Scholar [1], Dr. B.R. Ambedkar University, Agra [2] India Abstract: Many applications rely heavily on energy and thereby have a huge impact on number of times a packet is forwarded or sent across the network channel. It is very obvious that there is very low resource availability in such networks and applications and hence more copies and replications of a packet add significantly towards the energy and resource utilization in these networks. In real time systems, users have a particular deadline under which they need to communicate since the messages can be as urgent as 200 seconds or as unimportant as 2 to 3 days. To provide QoS in adhoc network with delay tolerant characteristic we take into account delay metric of the discovered links between source and destination in the route discovery process. This metric will be recorded inside the routing table, and it will be used to select the path that carries the lowest value of delay to use it as an active route between the source and destination. We develop prediction algorithm which predicts lowest cost path that satisfies a deadline. Then we propose a routing technique for Adhoc networks with quality of service that takes into account two important factors for messages of mobile nodes, deadline and cost and uses the prediction algorithm to make a more intelligent decision on which node to forward the message to. As many nodes in networks have high constraint on energy, so we will try to optimize on cost which is the number of transmissions on the network. Then we evaluate our proposed routing technique with existing protocols. Keywords: PORT, QoS. TCP/IP I. Introduction In Mobile Adhoc networks, a complete path does not exist from a source to a destination for most of the time or such a path is highly unstable and even may change or break soon after it has been discovered. This is due to constraints like node mobility, limited radio range etc. While mobile networks create many opportunities for computer communication, they also pose new challenges, for example frequent disconnections, limited bandwidth, long delays etc. Most of the networking protocols that are widely used like TCP/IP protocol were designed with wired networks in mind. Sometimes, there is no single-hop or multiple-hop route between some (or all) source/destination node pairs which might prevent some nodes from communicating with others and result in a disconnected network. If the disconnection last for a long duration of time, then it is not possible to deliver a packet from source to destination. It requires a new routing approach i.e. Store-Carry-and-Forward routing [3] rather than the traditional store-and-forward" routing paradigm, used in most current ad hoc routing approaches, in which messages are dropped if no route is found to reach a destination within a short period of time [4][5]. To deliver messages in a disconnected network or network which tolerate some delay [1], new quality of service routing approaches are proposed for delivering data. When QoS is considered, some techniques may be unsatisfactory or impractical due to the lack of resources and the excessive computation overhead. QoS routing usually involves two tasks: collecting and maintaining up-to-date state information about the network and finding feasible paths for a connection based on its QoS requirements. To support QoS, a service can be characterized by a set of measurable pre-specified service requirements such as minimum bandwidth, maximum delay, maximum delay variance and maximum packet loss rate. However many other metrics are also used to quantify QoS. To offer QoS routing, at least two parameters must be considered: minimum available bandwidth and maximum delay. Each node maintains a routing table to route data destined to the other nodes in the network. Most Adhoc networks with Delay Tolerant routing focus on whether or not to make a copy when meeting a node but not on planning a path, because path planning requires either knowledge of all the paths or prediction of the paths. Path planning is a more efficient way of routing since one can reduce on cost and yet achieve the reliability. But for high performance of path planning algorithm, we need high quality prediction and such high quality prediction might only be possible in semi-deterministic Adhoc Delay Tolerant Networks. Thus if a prediction scheme that can give the delay for the next contact for all the neighbors and also how often the neighbors meet, then it can find a more energy efficient routing solution and can make a decision on whether to forward to current neighbor or wait for a future contact that might arrive. The performance of the routing technique then will depend on the accuracy of the prediction algorithm used. So we are interested in predicting not only how often a given pair of nodes meets, but also the time at which they are likely to meet, given their past history of contacts. 2014, IJARCSSE All Rights Reserved Page 1492
II. Related Work Existing ad hoc routing techniques are unable to transmit between a source and destination that does not have a connected path. On the other hand in reality, there is never a connected path from source to destination. Recent research in this field includes Routing for such type of Networks [7] where messages are distributed within connected nodes in a set of message exchange sessions. Through node mobility, carriers contact other connected portions of network and message is spread to additional island of connected nodes. Given random exchange of data among replicas, all replicas will see all updates in a bounded amount of time. By giving random exchange of data among replicas, all updates will be seen by all replicas in a bounded amount of time. Routing is relatively simple because it requires no knowledge about the network. The disadvantage is that a huge amount of resources are consumed due to the large number of copies. This requires large amount of buffer space, bandwidth, and power. routing is a very simple and effective approach, but it does not consider the Constraints on the resource limitation. PROPHET, a Probabilistic Routing Protocol using History of Encounters and Transitivity, proposed by Lindgren et al. in [6] uses delivery predictability to determine a metric for contacts relative to successful delivery, such as delivery probability or delay. The operation of PROPHET is similar to that of Routing. When two nodes meet, they exchange summary vectors which in this case also contain the delivery predictability information stored at the nodes. This information is used to update the internal delivery predictability vector and then the information in the summary vector is used to decide which messages to request from the other node based on the forwarding strategy used. The basic assumption in the PROPHET is that mobility of nodes is not purely random, but it has a number of deterministic properties e.g. repeating behavior. The nodes that met each other in the past are more likely to meet in the future. Delivery predictability estimates the probability of the node A to be able to deliver a message to the destination node B. Similar to epidemic routing, whenever a node comes in to contact with other nodes in the network, they exchange summary vectors. The difference is that in the PROPHET method, the summary vectors also contain the delivery predictability values for destinations known by each node. Each node further requests messages it does not have and updates its internal delivery predictability vector to identify which node has greater delivery predictability to a given destination. Nodes update their delivery predictability metrics whenever meet each other. The delivery predictability favors nodes to meet frequently, and the fact that even if mobility is random, nodes that previously were close, probably have not moved far away from each other. III. Mobility Prediction Algorithm We assume that the mobility of nodes in a certain class of Adhoc Networks with Delay Tolerant characteristic is somewhat predictable and this is reflected in the contact pattern of nodes. We also assume that pattern of contact is somewhat periodic. This periodicity of behavior is captured by what we call a frame, which is the length of time after which the pattern repeats itself. Hence predicting how the nodes behave relies on predicting their behavior in the frame. For a contact pattern to be deterministic means, the frame is finite. Hence, a frame is the minimum (finite) amount of time after which the contact pattern repeats itself. The prediction problem can be stated as follows: Given a history of contact H i,j, which is an array size h l of tuples < t c,l c > (time of contact and length of contact) and time t, predict the delay before the next contact. We adopt the following prediction methodology developed by Mukundan Sridharan [2]. We divide time into frames of length f l and each frame into slots S n of fixed length S l. Next we find the average number of contacts per slot Pc i, j, given the history, which is simply the ratio of the number of contacts in time slot Sn and the number of frames of contact history data available, i.e., h l /f l. Given the average contacts Pc i, j and the time frame tf( i, j ), we calculate the maximum expected delay for the next contact for a probability threshold Pc thresh (for example, say 0.95), by summing up the individual contact probabilities in each slot, so the sum equals the threshold Pc thresh and finding the corresponding delays for the contacts. A natural threshold in our slot average case is 1.0 or a number very close to 1.0, which means that as the cumulative sum crosses 1.0 you are assured of at-least one contact before or by that time slot. Thus, a node i, for each node j it meets, predicts the maximum contact delays < C 1 j,c 2 j..c n j > with respect to a time frame tf( i, j), based on the contact history between the two nodes. IV. Proposed Routing Technique We have proposed routing technique for adhoc network with delay tolerant characteristic as QoS metric. The proposed approach has two phases path discovery phase and path maintenance phase. When a source node has to pass data to a destination node with QoS requirements, it starts with the path discovery phase. We used mobility prediction algorithm for intelligent path discovery. Once the path is found, the data transfer will take place. While data transmission is going on, it is also required to maintain the path to the destination. Steps of proposed routing technique are as follows: 1. Update history and contact predictions. /*Contact history of the nodes are updated and the contact prediction algorithm is run to get the expected contact delays*/ 2. Compute the available paths vector. /*compute the set of paths available from a node to the destination, through all of its available neighbors (excluding the current contact). Through each of the neighbors, multiple paths could be available*/ 2014, IJARCSSE All Rights Reserved Page 1493
3. Exchange available path information. /* Nodes exchange the paths with each other*/ 4. Update the path table with new information /* Node updates its routing table with the paths it received*/ 5. Compute the best path for each packet. /* Compute the lowest cost path that satisfies the deadline*/ 6. Forward packets. /* Forwards the data packets meant for (current contact) node j*/ V. METHODOLOGY Simulation scenario Due to the cost and difficulty of collecting traces of real world, the trace data will generally not be enough to run the amount of simulations for testing routing techniques. So it is good to build a synthetic mobility model. This model can generate an infinite number of trace data with the same statistical mobility properties as compared to real-world mobility model. The KU Campus environment is a synthetic trace that uses the Time Variant Community Model of Wei-Jen Hsu et. al. [8]. The model is based on two very essential things, Communities and Time Periods. Every node has a probability of going to each community at each time period. Also every node has an epoch (period) length and an epoch time which signifies the stationary time of the nodes at those communities. By setting all these parameters, one can model a simulation scenario which is closer to the human mobility in these environments. Performance Metrics The following metrics are used for the comparison of the protocols and proposed technique: 1. Number of Forwards or Cost This is the most important metric in this study and is the main metric to optimize. Number of forwards is defined as the total number of times a packet is sent on the network channel and is averaged over total number of packets created in the network. Number of Forwards = Total number of sends / (Total packets created per node*number of nodes) 2. Technique s Efficiency This is another important metric in this study and is defined as the total number of packets including data packets and control packets but not beacon(new) packets sent on the network channel divided by total number of data packets received at the base station (Good-put). Technique Efficiency = (Number of data packets sent + number of control packets sent)/ Total packets received at base station 3. Throughput Throughput shows the reliability of a technique which proves how successful the technique is and hence is defined as total number of packets received at the base station divided by total number of packets created in the network. Throughput = Total packets received at base station / (Total packets created per node* number of nodes) 4. Average Delay Delay is a metric which is optimized in most of the work done in routing. Here we combined this metric with a constraint value named as 'Deadline' that needs to be met. Average Delay is defined as the Total End to End Delay which is the time duration between creation of packet and when packet is received at the base station for all the packets created in the network averaged over number of packets created for all nodes. n Average Delay = i=1 ( Time packet i was received - Time packet i was created)/ (Total packets created per node * Number of nodes) VI. Analysis and Results In order to simulate existing protocols and our proposed optimized routing technique, we use NS 2 network simulator. The mobility of nodes is set by ns mobility traces, files generated for the above mentioned synthetic traces. There exists 1 base station and other mobile nodes that generate traffic to be delivered at the base station. At every node, packets are generated with size of 400 bytes at a constant interval and we plot the above important routing metrics with change in interval. We also change the number of nodes in the network and try to analyze the technique. We set a fixed buffer size of 200 packets for our technique. When a node meets, it exchanges the routing control information first and then starts sending data packets in order until all the packets are send or if the node goes out of communication range of the other node. Our technique is referred as Proposed Optimized Routing Technique (PORT) with two different versions with different deadlines 12 and 24 in the graphs. 2014, IJARCSSE All Rights Reserved Page 1494
Throughput No. of Forwards Efficiency 0.7 0.6 Efficiency Vs 0.5 0.4 0.3 0.2 0.1 0 0 0.05 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Traffic rate Figure 1: Efficiency vs. Our technique has the highest efficiency as can be seen in figure 1. Also efficiency decreases with lower deadlines which are expected since the delay constraints increase. The other techniques have a very low efficiency because of its high cost and higher technique overhead. 25 No. of Forwards Vs 20 15 10 5 0 0 0.05 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Figure 2: No. of forwards Vs As we can see from figure 2, our technique outperforms all the other techniques by a long margin in terms of cost which is number of forwards. Our technique with deadline 24 achieves the least cost since it is allowed to wait for a longer but low cost path compared to deadlines 12. is a single copy forwarding scheme and because of its average metric calculation, results in a high number of transmission thereby increasing cost. But it can also be seen that all the 2 versions of our technique with deadlines 12 and 24 still outperform and the other techniques. 1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 Throughput Vs 0 0.05 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 Figure 3: Throughput vs Figure 3 shows us the throughput with respect to rate of traffic. As can be seen that have the best throughput since it uses replication and also buffer mechanism policies and acknowledgment mechanism to reduce on buffer. 2014, IJARCSSE All Rights Reserved Page 1495
Average Delayt 7 6 5 4 3 2 1 Average Delay Vs Figure 4: Average Delay Vs Figure 4 shows the average delay for packets with respect to rate of traffic. As with throughput, the delay of the replication techniques is the best as expected. But it can be seen that our technique outperforms other single copy techniques and is also very close in terms of delay as compared to the replication techniques. Also it can be seen that if higher delay constraints are enforced, the delay reduces as deadline decreases. Thus the deadline parameter can be refined such that a particular delay can be achieved but with increase in cost. VII. Conclusion In this paper, we have developed a prediction algorithm in ad hoc networks with delay tolerant characteristic that takes nodes inter contact time history and gives future contact times for all pair of nodes in network. The availability of future contact times help in making routing decisions for routing techniques. We have analyzed the node mobility to better estimate node-to-node future contact statistics for improving message delivery. We have also designed an optimized routing technique that predicts the future nodes to reach the destination with quality of service under delay and buffer constraints of a network. For QoS, we have taken into account two factors for packets of mobile nodes- deadline i.e. time limit and cost i.e. the number of transmissions required for transfer of packet to the destination. Thus we have optimized routing technique that achieves equivalent reliability under delay and buffer constraints of a network. Simulation results are shown and the comparison of optimized routing technique with existing protocols is carried out. The result shows better performance in routing of data from source to destination. In Optimized routing technique, we will find paths to transmit data between any nodes in a semi-deterministic adhoc network with delay characteristics. It also tries to show the optimality of the technique with a low computational cost using Qos factors. After that it describes the synthetic traces used to validate the routing technique. It then describes briefly the other routing techniques used to compare with the proposed technique. Finally and also the validation of the proposed routing technique and References [1] Graham Williamson, Routing in Human Contact Networks, National University of Ireland, Dublin, 2010. [2] T. Spyropoulos, K. Psounis and C. Raghavendra. Spray and wait: an efficient routing scheme for intermittently connected mobile networks, Proceedings of the 2005 ACM SIGCOMM workshop on Delay tolerant networking (WDTN '05). ACM, New York, NY, USA, 252-259, 2005. [3] L. Song, D.F. Kotz., Evaluating Opportunistic Routing Protocols with large Realistic Contact Traces, Proceedings of the second workshop on Challenged networks (CHANTS), ACM,2007. [4] Abolhasan M., Wysocki T. and Dutkiewicz E, A review of routing protocols for mobile ad hoc networks, Ad Hoc Networks Elsevier Vol. 2 No.1, Pp. 1-22, 2004. [5] Mundur P., Lee S. and Seligman M, Routing for Data Delivery in Dynamic Networks, Military Communications Conference MILCOM, IEEE Washington, DC, 1-7, 2006. [6] A. Lindgren, A. Doria, and O. Schelen, Probabilistic routing in intermittently connected networks, In Proc. of the 1 st Int l. Wkshp. on Service Assurance with Partial and Intermittent Resources, pages 239 254, 2004. [7] Amin Vahdat and David Becker, Routing for Partially-Connected Ad Hoc Networks, Department of Computer Science Duke University Durham, NC 27708. July 2000. [8] Wei-Jen Hsu, Thrasyvoulos Spyropoulos, Konstantinos Psounis, and Ahmed Helmy, Modeling spatial and temporal dependencies of user mobility in wireless mobile networks, ACM Transaction 17(5): 1564-1577, 2009. [9] D. Johnson, D. Maltz, Dynamic source routing in ad hoc wireless networks, Mobile Computing, The Springer International Series in Engineering and Computer Science, Volume 353. 1996. 2014, IJARCSSE All Rights Reserved Page 1496