Abstract
Shortest path (SP) routing problem for static network has been addressed well in the recent past using different intelligent optimization techniques such as artificial neural network, ant colony optimization, particle swarm optimization, genetic algorithms (GA) etc. However, advancements in the wireless communication result in more and more wireless mobile networks such as mobile ad hoc network, wireless mesh network, etc. for which static path routing algorithms will not work well due to the dynamic nature of the mobile networks whose environmental conditions change over time. In this paper, we present a new method to address the SP routing problem for dynamic wireless sensor networks using well known optimization technique called GA. In this method, different paths which are formed randomly by the nodes between source and destination are modeled as chromosomes in the GA. Then, these chromosomes are undergone various genetic process such as selection, crossover and mutation to get new chromosomes. Every time the topology changes, network parameters such as sent packets, received packets, transmission time and dropped packets are estimated for each path and the optimized route is selected using fuzzy based fitness function applied to each chromosomes.
Similar content being viewed by others
References
Abdeljaouad, I., & Karmouch, A. (2015). Monitoring IPTV quality of experience in overlay networks using utility functions. Journal of Network and Computer Applications, 54, 1–10.
Lee, S., Soak, S., Kim, K., Park, H., & Jeon, M. (2008). Statistical properties analysis of real world tournament selection in genetic algorithms. Applied Intelligence, 28(2), 195–205.
Ahn, C. W., & Ramakrishna, R. S. (2002). A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transactions on Evolutionary Computation, 6(6), 566–579.
Eryilmaz, A., & Srikant, R. (2006). Joint congestion control, routing and MAC for stability and fairness in wireless networks. IEEE Journal on Selected Areas in Communications, 24(8), 1514–1524.
Neely, M., Modiano, E., & Li, C. (2005). Fairness and optimal stochastic control for heterogeneous networks. In Proceedings of IEEE INFOCOM (Vol. 3, pp. 1723–1734). Miami, FL.
Mohemmed, A. W., Sahoo, N. C., & Geok, T. K. (2008). Solving shortest path problem using particle swarm optimization. Applied Soft Computing, 8(4), 1643–1653.
Tassiulas, L., & Ephremides, A. (2002). Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multi-hop radio networks. IEEE Transactions on Automatic Control, 37(12), 1936–1948.
Stolyar, A. (2005). Maximizing queueing network utility subject to stability: Greedy primal-dual algorithm. Queueing Systems, 50(4), 401–457.
Neely, M. (2006). Energy optimal control for time-varying wireless networks. IEEE Transactions on Information Theory, 52(7), 2915–2934.
Wu, X., & Srikant, R. (2006). Scheduling efficiency of distributed greedy scheduling algorithms in wireless networks. In Proceedings of IEEE INFOCOM (pp. 1–12).
Dimakis, A., & Walrand, J. (2006). Sufficient conditions for stability of longest queue first scheduling. Advances in Applied Probability, 505–521.
Yeh, E., & Berry, R. (2007). Throughput optimal control of wireless networks with two-hop cooperative relaying. In Proceedings of IEEE ISIT (pp. 351–355).
Yeh, E., & Berry, R. (2007). Throughput optimal control of cooperative relay networks. IEEE Transactions on Information Theory, 53(10), 3827–3833.
Jung, K., & Shah, D. (2007). Low delay scheduling in wireless network. In Proceedings of IEEE ISIT (pp. 1396–1400).
Gupta, A., Lin, X., & Srikant, R. (2007). Low-complexity distributed scheduling algorithms for wireless networks. In Proceedings of IEEE INFOCOM (pp. 1631–1639).
Lin, X., & Rasool, S. (2006). Constant-time distributed scheduling policies for ad hoc wireless networks. In Proceedings of IEEE CDC (pp. 1258–1263).
Granda, J. C., Nuño, P., García, D. F., & Suárez, F. J. (2015). Autonomic platform for synchronous e-training in dispersed organizations. Journal of Network and Systems Management, 23(1), 183–209.
Goleva, R., Stainov, R., Savov, A., & Draganov, P. (2015). Reliable platform for enhanced living environment. In Mobile Networks and Management (pp. 315–328). Springer International Publishing.
Eryilmaz, A., Ozdaglar, A., & Modiano, E. (2007). Polynomial complexity algorithms for full utilization of multi-hop wireless networks. In Proceedings of IEEE INFOCOM (pp. 499–507).
Sanghavi, S., Bui, L., & Srikant, R. (2007). Distributed link scheduling with constant overhead. In Proceedings of ACM SIGMETRICS, San Diego, CA (pp. 313–324).
Lin, L., Lin, X., & Shroff, N. (2007). Low-complexity and distributed energy minimization in multi-hop wireless networks. In Proceedings of IEEE INFOCOM (pp. 1685–1693).
Joo, C., Lin, X., & Shroff, N. B. (2008). Understanding the capacity region of the greedy maximal scheduling algorithm in multi-hop wireless networks. In Proceedings of IEEE INFOCOM, Phoenix (pp. 1103–1111).
Jiang, L., & Walrand, J. (2008). A distributed CSMA algorithm for throughput and utility maximization in wireless networks. In Proceedings of 46th Annual Allerton Conference Communication, Control, Computing (pp. 1511–1519).
Liu, J., Yi, Y., Proutiere, A., Chiang, M., & Poor, H. V. (2008). Maximizing utility via random access without message passing. Microsoft Research, Technical Report.
Ni, J., & Srikant, R. (2009). Distributed CSMA/CA algorithms for achieving maximum throughput in wireless networks. Technical Report.
Bui, L., Srikant, R., & Stolyar, A. L. (2008). Optimal resource allocation for multicast flows in multihop wireless networks. Philosophical Transactions of the Royal Society A, Mathematical, Physical and Engineering Science, 366(1872), 2059–2074.
Ying, L., Shakkottai, S., Reddy, A., & Liu, S. (2011). On combining shortest-path and back-pressure routing over multihop wireless networks. IEEE/ACM Transactions on Networking, 19(3), 841–854.
Fotiou, N., Katsaros, K. V., Xylomenos, G., & Polyzos, G. C. (2015). H-pastry: An inter-domain topology aware overlay for the support of name-resolution services in the future internet. Computer Communications, 62, 13–22.
Figueiredo, S., Jeon, S., Gomes, D., & Aguiar, R. L. (2015). D3 M: Multicast listener mobility support mechanisms over distributed mobility anchoring architectures. Journal of Network and Computer Applications, 53, 24–38.
Bui, L., Srikant, R., & Stolyar, A. L. (2009). Novel architectures and algorithms for delay reduction in back-pressure scheduling and routing. In Proceedings of IEEE INFOCOM (pp. 2936–2940).
Goldberg, D. E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex systems, 3(5), 493–530.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Senthil Kumar, K., Ramkumar, D. Combined Genetic and Fuzzy Approach for Shortest Path Routing Problem in Ad hoc Networks. Wireless Pers Commun 90, 609–623 (2016). https://doi.org/10.1007/s11277-015-3130-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-015-3130-7