Research On Wireless Sensor Network Routing Protocol Base On Load Balancing

WSN (Wireless Sensor Network) is a hot topic in IT field during this century. It improves our ability to obtain the information, because it can provide the most direct, most effective and most objective physical information for mankind. In view of this, WSN has already got the widespread attention in military field, civil domain as well as academic field. Looking forward to the future, the wireless sensor network has the very broad prospect on market applications.Wireless Sensor Network consists of many sensor nodes whose capabilities, such as processing, storage and communications, are limited. The sensor nodes generally located in relatively harsh environment, so it is impossible to supply energy or substitute node for the network. The above problems have brought some inherent defects on wireless sensor networks, such as the limited energy supply, opened communication medium, fixed bandwidth and so on. According with the development of routing protocols in wireless sensor network, this paper analyzes some typical routing protocols and summarizes their advantages and defects.In summary, the utilization of energy is not sufficient. To solve this problem, this paper puts forward a load balancing dynamic routing algorithm based on ant colony optimization, BEACO.There are two improvements on this load balancing dynamic routing algorithm. The first improvement is that the choice of next hop node not only refers to the Pheromone on the path, but also refers to the residual energy of the next hop node, that is to view the choice of next hop as a combination planning issues based on the shortest path and minimum cost. The second improvement is that the update and volatilization of Pheromone are both reference to the energy of next hop node, that is to say the node with less energy will have smaller possibility to be selected as the next hop.This algorithm can guarantee the survival of the node with little energy, thus extending the life cycle of the network. In this paper, the simulation environment on the experiments is ns2.Compared with the traditional ant colony algorithm and other ant colony algorithm referred to energy, EEABR, the BEACO can effectively use the energy of each node in the network, and prolong the life cycle of the network. The algorithm BEACO does not limit the scale of the network, so it has broader applicability than other algorithms.