IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

International Journal of Research in Information Technology (IJRIT)


ISSN 2001-5569

An Efficient Genetic Algorithm Based Optimal Route Selection Algorithm for WSN Mr. Sudeep Kumar Gupta1, Asst. Prof. Manoj Chouhan2 1

Research Scholar Information Technology/ RGTU Indore, Madhya Pradesh, India [email protected] 2

Asst. Prof. Information Technology/ RGTU Indore, Madhya Pradesh, India

Abstract Wireless sensor Network (WSN) is getting popular especially for applications where installation of the network infrastructure is not possible, such as military applications, remote sensing and disaster management. Despite the fact that WSN provides a great way of communicating without network infrastructure, but imposes some drawbacks and limitations (mainly on discovery of path and maintenance), which had to be corrected. This work presents a genetic algorithm based optimum route selection based technique to enhance the performance of WSN. The genetic algorithm basically works on the basis of natural selection concept which has proven its superiority for many heuristic search applications. The proposed technique is tested by simulating a mobile ad-hoc network using network simulator and MATLAB. Finally the results show that improves network performance.

Keywords: Wireless Sensor Network (WSN), Optimal Routing, Genetic Algorithm.

1. Introduction The Mobile Ad-hoc network is defined as an auto-configurable, infrastructure-less network of mobile devices that are connected through a wireless connection. Each device in a WSN is free to move independently in each direction, and hence will change ties with other devices frequently. Each move must forward regardless of its own use, and therefore, to be a router. Since there is no central control system and dynamic change in the position of nodes also makes it difficult to transfer the data to the network that is causing the highest packet drop rate, greater end-to-end delay, higher power consumption and extra charge etc. all of these complications are resolved by the routing protocols used, but because of the complicated structure rather than a single routing protocol can solve all the problems associated with it. Even if it is difficult to solve by using multiple protocols. Therefore, a different approach is needed for this problem. This work presents a genetic algorithm based optimal route selection approach to overcome it. Mobile factor is on a new approach in which instead of directly accessing a node in a program carried on the network and the program is running on the node in the network with the use of their resources and sends the requested information back to the home node. The rest of the paper arranged, as the second Section presents a brief review of the Literatures on the same topic. The third section presents the basic function of WSN routing protocol that is followed by the explanation of Genetic Algorithm in the fourth section. The fifth section explains the proposed algorithm and respective simulated the results and conclusion is presented in the sixth and seventh points respectively.

2. Literature Review This section presents an overview of some of the most important literatures are available in the same domain. First of all the details of DSR Protocol discretion provided by David b. Johnson et al [1] that have evaluated the operation of DSR through detailed simulation in a variety of styles of movement and communication, as well as through the implementation and considerable experimentation in a natural outdoor ad hoc networking testbed built in Pittsburgh, and have demonstrated the excellent performance of the Protocol. Another document for performance evaluation of routing protocols for WSNs presented by a. Ferreira et al [2], analyze the application of evolutionary graph theory in the construction of efficient routing protocols in realistic scenarios. VE Mujica et al [3] proposed a Mr. Sudeep Kumar Gupta,IJRIT


IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

smart mobile instrument called a NEURAL Agent and compared with normal DSR. This autonomous factor coordinates the collection of information, integrating, designing, programming and decision-making with other players through different sections that function asynchronously. Kenji Tei et al [5] showed a technical data recovery Site especially for WSN using mobile media. Will propose the Geographically Bound Mobile Agent (GBMA), which is a mobile agent migrates to be always located in a designated area. In Addition, to clarify cases where GBMA should be located and when the GBMA begins to migrate, we introduce two geographical zones: desired zone and the expected zone. In comparison with conventional methods with geocast or with a conventional mobile factor, the GBMA with these zones to recover data to a specific location can reduce the total number of messages. Hairong Qi et al [4] presents a method for the development of an optimal path for mobile agent to fulfill the task completion, while consuming minimal quantity of resources, including time and energy. Lei Liang et al [6] showed the algorithm based on agents and implements a protocol based on demand that provides efficient routing at the application level. A proof of concept implementation has been developed using Aglets [6], as well as the simulation for evaluation of performance.3. Dynamic Source Routing (DSR) Protocol.

3. Route Discovery in WSN using DSR For example, Figure 1 illustrates an example of Route Discovery, in which a node A is trying to discover a route to node E. to launch the Discovery Route, a broadcast a message REQUEST ROUTE as a single local broadcast packet, which is taken from (about) all the nodes currently within the transmission range of A. Every APPLICATION message ROUTE identifies the target and initiator Discovery Route, and also contains a unique request ID, which is defined by the founder of the APPLICATION. Each APPLICATION ROUTE also contains a business address file of each intermediate node through which this particular copy of the message REQUEST ROUTE has passed. This track record starts with a blank list from the organizer of the Discovery Route.

Fig.1: Route Discovery example: Node A is the initiator, and node E is the target When another node receives a REQUEST, if RD is the goal of Discovery Route, returns a message RESPONSE ROUTE to the initiator of the Discovery Route, giving a copy of the accumulated record route from APPLICATION ODOS; When the initiator receives this ROUTE reply, the caches this route in the Route Cache for use in sending subsequent packets for that destination. Although the DSR has many advantages that suffers from some disadvantages, such as the retention mechanism of routes is not locally repair a broken link. Stale route cache information might also lead to inconsistencies in the reconstruction phase of the route. The connection setup delay is higher than in the table-driven protocols. Even if the Protocol performs well in static and low-mobility environments, the efficiency drops rapidly with increased mobility. Also, significant growth of routing overhead is involved due to the source routing mechanism used in the DSR. This routing overhead is directly proportional to the length of the route.

4. Genetic Algorithm A simple Genetic algorithm is an iterative process, which maintains a stable population size P candidate solutions. During each iteration (generation) stage three genetic operators (reproduction, crossover and mutation) running to create new populations (offspring), and chromosomes of new populations were evaluated through the value of fitness that relates to the operating cost. Based on these genetic operators and evaluations, the best new populations of candidate solution is formed. With the above description, a simple genetic algorithm is given as follows [6]: 1. 2. 3. 4. 5.

Create a random population of binary string Calculate the suitability for each row in the population Create chords offspring through reproduction, crossover and mutation operation. Evaluate new strings and to calculate the suitability for each series (chromosome). If the search target is achieved, or allowable production is reached, return the best chromosome as a solution? Otherwise go to step 3.

Mr. Sudeep Kumar Gupta,IJRIT


IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

5. Proposed Algorithm In simple WSN routing protocol the route discovery is done by selection is done by selection of best route on the basis of coordinate distance. Here we are proposing an enhancement on WSN routing protocols for multi rate multi hops system using Genetic Algorithm which ensure the optimal route selection to get maximum throughput & minimum route maintenance and avoids congestion in our proposed method we are considering the multiple characteristics of nodes & than decide the route according to their scores on the basis of our developed formula explained below Objfun = Objective Function PFR = packet forwarding ratio MI = Mobility index MNH = maximum number of neighbors MBW= maximum bandwidth Then according to values we can calculate the following objective function for route selection  = (1 − ) / ( ∗ ) The definitions of terms used are PFR (packet forwarding ratio) = successful forwarded packets/total packets MI (Mobility index) = Non availability of node/total requirement to node. The index represents the mobility lesser the stability hence it reduces the selection chances of node for route. MNH (maximum numbers of neighbors) = maximum number of nodes attached at any node (With maximum bandwidth). MBW (Maximum Bandwidth) = Maximum Bandwidth of the node (Because of multi rate consideration) In our proposed method the route search is done according to algorithm below: Every node records the ID of nodes and other data such as MBW, MNH, MI and PFR rate of the nodes in the network. According to above explanation each node calculate the Objective function for each route & list them in descending order which helps it on selection of another route in case of failure of one route. The considerations taken during the simulation are: 1. 2. 3.

Each node maintains the table for their neighbors in the network. The table is used to store the required parameters of each node. Node have assigned maximum & minimum transmission rate randomly according to Gaussian description between 100 packets to 5 packets per seconds. 4. The total network area is considered 1km*1km. 5. Total number of nodes 50. 6. Mobility is assigned randomly. 7. Each node transmits at 10mw power & having Omni directional antenna. 8. Maximum number of route cache is 100. 9. transmission rate 9600 bps to 56000 bps 10. Packet size 1024 bits

6. Simulation Results After choosing parameters, the simulation is done for 60 minutes for a scenario. Then results were gathered. Mr. Sudeep Kumar Gupta,IJRIT


IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

In simulation scenario, 50 stations were configured to use these services randomly. In the simulation, we assumed that each traffic class has the equal portion of the total data traffic in terms of the average number of packets generated per unit time. The results obtained are as follows:

Fig.2: Route selection in normal WSN routing algorithm ([1, 18, 37, 45, 47, 7, 48, 33, 10, 16, 29, 2, 26, and 15]) route cost = 9.077.

Fig.3: Route selection in Proposed routing algorithm ([1, 37, 47, 4, 33, 16, 29, and 15]) route cost = 4.472.

Mr. Sudeep Kumar Gupta,IJRIT


IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

Fig.4: End to End Delay Comparison between Previous (Blue) and Proposed (Red)

Fig.5: End to End Delay Comparison between Previous (Blue) and Proposed (Red)

Fig.6: Route Discovery Time Comparison between Previous (Blue) and Proposed (Red) Mr. Sudeep Kumar Gupta,IJRIT


IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

Fig.7: Total Route Request Comparison between Previous (Blue) and Proposed (Red)

7. Conclusion The results obtained from simulation shows that proposed method provides efficient mechanism for service differentiation and hence provides quality of service to the WSN. However, this improvement comes at a cost of a little increase in route discovery time hence it can be taken as optimized solution for quality of service and the route discovery time up to certain extent.

References: [1] David B. Johnson David A. Maltz Josh Broch “DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks”, http://www.monarch.cs.rice.edu/monarch-papers/dsr-chapter00.pdf. [2] A. Ferreira · A. Goldman · J. Monteiro “Performance Evaluation of Routing Protocols for WSNs with Known Connectivity Patterns Using Evolving Graphs”, Wireless Networks Volume 16 Issue 3, April 2010 Pages 627 - 640 [3] V.E Mujica-V, Y. Rebahi, D. Sisalem, R. Popescu-Zeletin,“Performance Comparison of the Neuron RoutingAlgorithm for Mobile Ad Hoc Networks”, http://www.iptel.org/~dor/papers/Mujica0905_Performance.pdf [4] Hairong Qi and Feiyi Wang “Optimal Itinerary Analysis for Mobile Agents in Ad Hoc Wireless Sensor Networks”, University of Tennessee 2007. [5] Kenji Tei, Nobukazu Yoshioka, Yoshiaki Fukazawa, Shinichi Honiden “Using Mobile Agent for Location-Specific Data Retrieval in WSN” Intelligence in Communication Systems IFIP: The International Federation for Information Processing Volume 190, 2005, pp 157-168 [6] Lei Liang and Peter Graham “Assessment of a Mobile Agent Based Routing Protocol for Mobile Ad-hoc Networks” AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01 Pages 1-5. [7] David B. Johnson David A. Maltz Josh Broch “DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks”, In Ad Hoc Networking, edited by Charles E. Perkins, Chapter 5 2001. [8] Ichiro Satoh “Mobile Agents”, http://www.informatik.unirier.de/~ley/pers/hd/t/Tian:Ruya.html [9] Y. Rehabi and V. E. Mujica-V., “A reputation-based trust mechanism for ad hoc networks,” in Proceedings of the Tenth IEEE Symposium onComputers and Communications, Cartagena, SPAIN, june 2005. [10] D. Johnson and D. Maltz, “Dynamic source routing in ad hoc wireless networks,” in Mobile Computing, Imielinski and Korth, Eds.Kluwer Academic Publishers, 1996, vol. 353. [Online]. Available:citeseer.nj.nec.com/johnson96dynamic.html [11] U. Berkeley and U. ISI, “The network simulator ns-2,” 1998, available from http://www.isi.edu/nsnam/ns. Mr. Sudeep Kumar Gupta,IJRIT


IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 152-158

[12] Z. Hass and M. Pearlman, “The performance of query control schemes for the zone routing protocol,” in IEEE/ACM Transactions on network-ing, vol. 9 N4, Aug. 2001, pp. 427–438. [13] C. Perkins, E. Royer, S. Das, and M. Marine, “Performance comparison of two on-demand routing protocols for ad hoc networks,” in IEEE Personal Communications, vol. 8 N1, february 2001, pp. 16–28. [14] J. Broch, D. Maltz, D. Johnson, Y. Hu, and J. Jetcheva, “A performance comparison of multi-hop wireless ad hoc networks routing protocols,”in Fourth annual ACM/IEEE international conference on Mobile com-puting and networking. ACM Press, 1998, pp. 85–97. [15] C. Perkins and M. Royer, “Ad-hoc on-demand distance vector routing,”Proc. 2nd IEEE Wksp. Mobile Comp. Sys. and Apps., pp. 90–100, Feb.1999.

Mr. Sudeep Kumar Gupta,IJRIT


An Efficient Genetic Algorithm Based Optimal Route Selection ... - IJRIT

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