| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系所 === 102 === Wireless mesh network is a communication network consisting of mesh routers and mesh clients. The main characteristic of this network is the ability to maintain an active network connection path between each mesh client pair. When a mesh router fails, an alternative path will be created to ensure communication is not interrupted. Real world usage patterns indicate clustering effects amongst clients of mesh clients and through mesh routers, can connect and communicate with each other directly, forming a social network. Thus, the purpose of this research is to develop social-based PSO algorithms for social-aware dynamic router node placement in wireless mesh networks. The concept of social-aware refers to routers that communicate with each other to sense movements by clusters of clients in order to provide them with internet access services. The role of the router node placement problem is to maintain up to date router information to map with the latest network topology as well as connectivity rates to maximize the number of clients serviced by the mesh router.
Prior research that involve using particle swarm optimization method in solving the router node placement problem results in many clients that cannot receive network services. The main cause of this issue is the result of routers not being placed at optimal locations and moving clusters of clients. Therefore, we devise a new particle swarm optimization method: based on a Social-based PSO Algorithm with an additional vector based mechanism to improve upon prior research that are based on particle swarm optimization methods. The vector based mechanism utilizes the property that enables router to router communications to continuously make rapid adjustments in response to movements by clusters of clients so that routers that have reached their maximum limit can enhance the network performance by offloading clients to other routers. The experiment was conducted on networks of various sizes, where clients are divided into two or three groups. Cluster of clients move according to planned paths and their initial positions are uniformly distributed. Next our Social-based PSO Algorithm is compared with prior particle swarm optimization methods. Experimental results show that the use of the Social-based PSO Algorithm in a dynamic scenario can effectively reduce the number of clients not able to receive the service, so that the overall network topology coverage and connectivity rates are greater using this algorithm.
|
|---|
