Energy Balance Strategy On Wireless Sensor Network

When the energy of the nodes are limited on wireless sensor network, especially the large-scale sensor nodes are deployed, it is a key factor that the network energy consumption police is to influence the network energy efficiency. And it is the one of the network energy consumption strategy that Energy-Balancing Consumption Strategy is to influence on enhancing the network coverage area and extending the network survival lifetime and improving the network energy efficiency. Currently it is still unsatisfactory that some Energy Balancing Consumption Strategy are based on the unidirectional energy driving force and the best node selected of the network nodes residual energy. These network energy consumption are unbalanced, and these network energy efficiency need to be improved. To optimizate above these issues, this paper build a virtual balacing forces which is "attractive force" and "resistance force". And this proposed Hybrid Strategy Particle Swarm Optimization Algorithm which combined with Standard Particle Swarm Optimization Algorithm and Mimicry Physics Optimization Algorithm. It applied this kind of the virtual balancing force and Energy-Balancing Consumption Strategy to this algorithm, which influenced the speed factor of this algorithm and sleep and wakeup & sleeping time of the nodes. Moreover, this implemented an Energy-Balancing Consumption Strategy which reach to balancing the network energy consumption and improving the whole network energy efficiency and coverage rate, and extending the network survival lifetime.Firstly, this has proposed an energy balancing consumption model that is based on an active virtual energy force. And it is also based on hierarchical trigeminal tree energy centroid of energy consumption model. The network residual energy gradually converged to a local energy center of gravity, and finally tended to the energy gravity center of the whole network. The higher is the energy distribution density of the nodes in the region, the more chance have these nodes waked up to extend the standby time. On the contrary, it is to shorter the standby time. By this way, this achieved to balance the energy consumption of the network.Secondly, this proposed the network topology based on neighbor Cell energy optimization. Referenced to the principle of physics Mimesis, this paper established two virtual forces: "attractive force" and "resistance force". With residual energy and distance ratio of node multiplied by the coefficient, and it calculated the virtual force and the center of the node energy change rate. This established virtual forces regulation. It balanced energy consumption between the work unit and the neighbor unit. It achieved energy balance consumption of dynamic neighbor network topology.And then, this established the relationship of the mapping between engineering requirement and algorithm model. Hybrid Strategy Particle Swarm Optimization Algorithm is applied to the network sleep and wakeup mechanism on the moving target tracking network. The speed of the virtual motion nodes is affected by a series of nodes virtual force on the basis of theoretical mechanics and Newton’s laws of motion. A virtual node moved along the energy distributed tree of the network. Velocity evolution equation is effected by a balance virtual force of the network nodes. Because the velocity of a virtual moving node is modified, this adjusted adaptively the standby time & the sleep time of these nodes. This implemented a network energy balancing consumption strategy. This combined the engineering concrete problem with theoretical solution, and evaluated the efficacy of this strategy with an algorithm performance.Finally, experimental results verified network energy balancing strategy which reached to balance the network nodes energy distributed through computing simulation based on MATLAB R2013 a tools and compared with other research data. This strategy achieved better energy efficiency than the hierarchical clustering nodes selected strategy and the best energy node selected strategy. With this strategy, the result is to optimize the problems existed in these energy balance strategy. It enhanced coverage area and extended the network survival lifetime, and improved the energy efficiency of the general network.