Research On Ant-Colony Based Routing Algorithm For WSN

Compared with the traditional computer network, wireless sensor network (WSN) has many characteristics such as energy-constrained, computing-limited, multi-hop communication, self-organized and dynamic-topology. Traditional routing mechanism does not suit for wireless sensor network. In some application, WSN requires its network nodes to self-organize into a reliable-connectivity network speedily, and provide stable packets, high throughput, and energy-efficient data transferring. The research of routing algorithm for WSN is becoming more and more important because of these technical difficulties.Based on the in-depth analysis of some existing routing protocols for WSN, An optimized ant-colony routing algorithm has been proposed. The main works of this thesis are as follows:1. According to the characteristics of wireless sensor networks, we designed and simulated the ant-colony routing algorithm by using the advantages of ant-colony algorithm, including easy to be realized, local work, integrating link quality into the computation of the pheromone concentration, supporting multi-path. The probability of being chosen as next hop is calculated by using the pheromone concentration.2. We proposed a optimized ant-colony routing algorithm According to the characteristics of WSN, such as energy-constrained and dynamic-topology. This strategy include a new probability model that can balance the energy consumption and the remaining energy of nodes. In this way, we can not only improve the life-time of WSN, but also get more optimal paths. Finally, we try to use the optimized ant-colony routing algorithm to solve the bridge structure monitoring problem by combining with the characteristics of bridge detection system.We proved that the ant-colony algorithm can be used to find the best route by simulation and analysis of the experimental results. We also provided a theoretical basis of applying ant-colony routing algorithm to the wireless sensor network of bridge structure monitoring system. Finally, we did a prospect of applying the ant-colony algorithm to the intelligent transportation system.