A Method of Improving the Coverage of Mobile Sensor Networks

Abstract

Provided is a method for improving mobile sensor network coverage rate. A mobile sensor network that the method is based on comprises a plurality of heterogeneous mobile sensor nodes, wherein each mobile sensor node has a specific perception radius and a communication radius and can obtain position of itself. The method comprises: meshing the continuous mobile sensor network monitor area and distributing each mobile sensor node to the meshed monitor area randomly so that initial position of the each mobile sensor node is obtained; calculating coverage rate of the mobile sensor network; determining strategy of improving mobile sensor network coverage rate by employing coverage hole-directed distributed differential evolution algorithm (CHDDE), that is, determining the position where each sensor node is to move; and carrying out the movement operation for each mobile sensor node. The method is more comprehensive and practical with the rate of convergence and system energy consumption taken into account; besides, based on distributed computation of node, and through coverage hole-directed distributed differential evolution algorithm, the new position of the node is calculated without foreknowing position information of all nodes, so that calculating speed is quickened and communication cost is saved, node energy consumption is saved, and network lifetime is prolonged.

Description

technical field [0001] The invention relates to the technical field of mobile sensor networks, in particular to a method for providing coverage of mobile sensor networks. Background technique [0002] The basic role of wireless sensor networks (WirelessSensorNetworks, WSNs) is to monitor the environment, discover and locate targets in the environment. Due to the low price of sensor nodes, they can be widely used in digital battlefield monitoring, national defense security, medical care, environmental monitoring and other fields. Each sensor node has a sensing range and a communication range, and the node can perceive the environmental information within the sensing range and communicate with other nodes within the communication range. The coverage of deployed nodes to the monitoring area is an important index to evaluate the performance of wireless sensor networks. The position change of nodes affects coverage, communication energy consumption and information processing. ...

AI Technical Summary

Problems solved by technology

A virtual force algorithm (virtual force algorithm, VFA), calculates the "attraction" and "repulsion" of the nodes according to the distance between the two nodes, so that the distance between the nodes reaches the specified threshold, so as to achieve the purpose of uniform deployment of nodes, but this method There are many deficiencies in the repeated coverage and uniform coverage of the network; the wireless sensor network coverage enhancement method based on local virtual forces constructs the interaction force between nodes and nodes, mobile nodes and the boundary of the target area, but this method has local maxima Covering the situation; combining the virtual force method and the particle swarm method, a new intelligent method, virtual force direct co-evolutionary particle swarm optimization (VFCPSO), has emerged. A group of nodes with high coverage is obtained, in which the virtual force is used to guide the update of the particle position in order to converge to the optimal solution faster, but the evolutionary diversity of this method is poor and the speed is slow; a kind of uncovered area exploration The heuristic method (uncovered region exploration algorithm, UREA), this method guides the node to move to the uncovered area by calculating the uncovered area in the neighborhood around the node, this method uses the information of the surrounding nodes more fully than the virtual force method, so it can be more Efficiently improve network coverage, and in practical applications can also reduce communication overhead, thereby saving energy consumption, but the calculation speed of this method is still slow

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