Research On The Lifetime Prolonging Algorithms For Wireless Sensor Networks

Wireless sensor networks combine sensor technology, embedded computing technology, computer technology, wireless communication technology and distributed information processing technology. It can collaborate objects information in real-time by a variety of integrated micro-sensors monitoring, sensing and collecting. The information will be sent to the user terminals through the processing of embedded systems and self-organizing wireless communication networks, so that we can grasp the overall monitoring of the regional parameters.Because the distributed region is full of sensor nodes, the area is wide and the deployment environment is complex, charging or replacing the battery to supplement the cost of energy consumed by the node is even higher than relaying, so it is unrealistic. Therefore energy conservation is an important research goal in wireless sensor networks. The major research work and content of this article is mainly reflected in the following aspects:(1)The network lifetime was significantly affected by the unbalanced energy consumption. In this paper, a square grid energy model based on the probability of transmission of the algorithm was derived, we use the recursive method to find the probability. Sensor nodes data are transmitted directly to the base station in a certain probability or to the base station by the relay node multi-hop transmission, then we make the relevant calculations. In the matlab simulation, we analyzed the factors that affect the probability of a node, and then the grid side length and network layers of their impact on the probability, on this basis we conclude the optimal transmission distance and the optimal layers.(2) Through energy consumption analysis of two traditional transmission strategies and the probability of hybrid transmission strategy, we sum up the advantages and disadvantages of each transmission protocol, We proposed the hybrid energy balance transmission strategy which is selectively optimized combinations, and it improve the unbalanced energy consumption of the hybrid probability propagation strategy. The simulation results show that the method greatly balance the energy consumption of the entire network to extend the network lifetime.