Research On Rounting Algotithm Based Ant_Colony_Optimization For WSN

With the rapid development of MEMS-based sensor technology,embedded computing systems, low-power wireless communications have enabled the development of relatively inexpensive and low-powerwireless micro-sensors, Hundreds or thousands of these micro-sensors can form self-organized network—WSN(Wireless Sensor Network) which can play an important role in a variety of commercial and military applications, such as environmental monitoring, Industry monitoring and security systems.Routing in sensor networks is very challenging due to several characteristics that distinguish it from contemporary communication and wireless Ad hoc networks. These characteristics, such as no global address scheme, dynamic topology, extraordinarily limited resource, constrain the development of routing in WSN. Due to such differences, many new algorithms have been proposed for routing data in sensor networks these years. Some routing protocols proposed by oversea researchers are analyzed and compared in the paper and on the basis of assimilating the excellent idea among them, an improved routing algorithm, Ant-Colony Based Routing Algorithm of WSN, is proposed.Using the characteristics of ant colony optimization algorithm—simply easy to be realized, local work, integrating the jump number into the Pheromone and supporting multiple paths, designed wireless sensor network routing algorithm based on the ant colony optimization algorithm. Ant Colony Optimization methods of self-organization, dynamic and multi-path make it particularly suited to the characteristics of wireless sensor networks for routing. On the basis of directed diffusion algorithm, this paper presented an Ant Colony Optimization routing algorithm of wireless sensor network (ARAWSN). The routing algorithm is a low cost, adaptability, and multi-path support, and other features. Simulation analysis showed that compared to directed diffusion algorithm, at the aspect of average delay in the transmission this algorithm is similar, but the average energy consumption in the network has been significantly improved. In addition, ARAWSN algorithm also has high reliability and adaptability, etc. Also, through adjusting pheromone concentration and the value of the partial inspiration weight, this algorithm optimizes the quality of the integrated routing, achieves network congestion control and energy balance, etc. in order to meet the needs of various applications. After the routing algorithm simulation tests, demonstrate that this algorithm basically achieves a wireless sensor network routing algorithm design goals.