Research On Node Deployment Based On Information Coverage In Wireless Sensor Networks

With the development of technology of wireless communication, sensor, embedded computing, distributed information processing, wireless sensor networks have made rapid development. Due to its potential applications in military, industrial, agricultural, bio-medical and many other prospects, wireless sensor networks have been gaining more and more attention. There are many problems to be solved in appling wireless sensor network, and one of them is coverage control. Furthermore, how to determine quantity and deployment positions of nodes, according to limited energy supply, to achieve the goal of maximizing the network lifetime has been one of the key issues of the coverage control.WSN coverage control not only means simple meaning of coverage, but also is necessarily related to specific aspects such as energy-saving communication, path planning, reliable communications and target locating. In accordance with different configuration types among nodes (i.e., whether the node needs to know its own location information), WSN coverage control is divided into two types: deterministic and random.Typical deterministic coverages include certainty regional/point coverage, grid-based target coverage. Certainty region/point coverage refers to the location information of WSN nodes being known in achieving the target area or target point coverage. Based on disk cover model, point coverage use the 0/1 coverage model to detect the target. Grid-based target coverage is used in the situation that the geographical environment being pre-determined, two-dimension (or three-dimension) grid are used to model the network, so as to select appropriate drid points for sensor nodes to implement regional/objectives coverage.This article studied the certainty point coverage and the grid-based coverage algorithm model and proposed a new algorithm of point coverage and target location for wireless sensor networks, which converts the sensor deployment problem into a constrained optimization problem. The algorithm utilizes greedy thought to get the optimal solutions while minimizing the total cost and ensuring the coverage requirement of the whole network system. Simulation results show that this algorithm is cost-effective and can greatly reduce the number of required sensors while meeting the requirement of information coverage. Based on the raised algorithm and the node energy calculation model,this article raised anotheralgorithm ,not only consider the maximization of the gain of the node,but also consider the energy consumption of the neighboring nodes .Simulation results show that this programme can decrease the energy consumption and the total price with the constraint.