An ACOBased Fair Energy Usage Routing Algorithm for Wireless Sensor Networks
LIANG HuaWei1,2,3, CHEN WanMing1,2, LI Shuai1,2, MEI Tao2, MENG Max2,3
1.Department of Automation, University of Science and Technology of China, Hefei 230027 2.Center for Biomimetic Sensing and Control Research, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 3.Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
Abstract:How to make good use of the limited energy to maximize the network life span is an important problem in the study of the wireless sensor networks (WSN). The life of a WSN depends on the minimum of the residual energy of its nodes. A fair energy usage routing algorithm is proposed which uses the Ant Colony Optimization Algorithm (ACO) to balance the network energy distribution and extend the network life. The proposed algorithm utilizes the dynamic adaptability and optimization capabilities of the Ant Colony to get a treadoff between the shortest path and the fair energy usage. Simulation results show that the proposed algorithm is good at balancing the energy usage, and it effectively extends the span of the network life. The network life span using the ACObased fair energy usage routing algorithm is extended over 33% compared with the one using the shortest path optimization algorithm.
[1] Akyildiz I F, Su Welian, Sankarasubramaniam Y, et al. A Survey on Sensor Networks. IEEE Communications Magazine, 2002, 40(8): 102114 [2] Akkaya K, Younis M. A Survey on Routing Protocols for Wireless Sensor Networks. Ad Hoc Networks, 2005, 3(3): 325349 [3] Shah R C, Rabaey J M. Energy Aware Routing for Low Energy Ad Hoc Sensor Networks // Proc of the IEEE Wireless Communications and Networking Conference. New York, USA, 2002, Ⅰ: 350355 [4] Wang Z, Crowcroft J. Quality of Service Routing for Supporting Multimedia Applications. IEEE Journal on Selected Areas in Communications, 1996, 14(7): 12281234 [5] Dorigo M, Gambardella L M. Ant Colony System: a Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Trans on Evolutionary Computation, 1997, 1(1): 5366 [6] Bullnheimer B, Hartl R F, Strauss C. Applying the Ant System to the Vehicle Routing Problem // Voss S, Martello S, Osman I H, eds. MetaHeuristics: Advances and Trends in Local Search Paradigms for Optimization. Boston, USA: Kluwer Academics, 1998: 109120 [7] Gutjahr W J. A Generalized Convergence Result for the Graphbased Ant System Metaheuristic. Probability in the Engineering and Informational Sciences, 2003, 17(4): 545569 [8] Gutjahr W J. A GraphBased Ant System and Its Convergence. Future Generations Computer Systems, 2000, 16(9): 873888 [9] Gutjahr W J. ACO Algorithms with Guaranteed Convergence to the Optimal Solution. Information Processing Letters, 2002, 82(3): 145153 [10] Stutzle T, Dorigo M. A Short Convergence Proof for a Class of ACO Algorithms. IEEE Trans on Evolutionary Computation, 2002, 6(4): 358365 [11] Sim K M, Sun W H. Ant Colony Optimization for Routing and LoadBalancing: Survey and New Directions. IEEE Trans on Systems, Man, and Cybernetics, 2003, 33(5): 560572 [12] Gunes M, Spaniol O. AntRoutingAlgorithms for Mobile MultiHop AdHoc Networks // Proc of the International Workshop on Next Generation Network Technologies. Rousse, Bulgaria, 2002: 1024 [13] Liu Z, Kwiatkowska M Z, Constantinou C. A Swarm Intelligence Routing Algorithm for MANETs // Proc of the 3rd IASTED International Conference on Communications, Internet and Information Technology. St. Thomas, USA, 2004: 484489 [14] Liu Zhenyu, Kwiatkowska M Z, Constantinou C. A Biologically Inspired Congestion Control Routing Algorithm for MANETs // Proc of the 3rd International Conference on Pervasive Computing and Communications Workshops. Kauai Island, USA, 2005: 226231 [15] Hussein O, Saadawi T. Ant Routing Algorithm for Mobile AdHoc Networks (ARAMA) // Proc of the IEEE International Conference on Performance, Computing, and Communications. Phoenix, USA, 2003: 281290 [16] Bonabeau E, Dorigo M, Theraulaz G. Inspiration for Optimization from Social Insect Behavior. Nature, 2000, 406(6): 3942 [17] Intanagonwiwat C, Govindan R, Estrin D. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks // Proc of the 6th Annual ACM/IEEE Conference on Mobile Computing and Networking. Boston, USA, 2000: 5667