Research On Multi-coverage In Wireless Sensor Networks

Wireless sensor network (WSN) is a network system composed of a large number ofmicro-sensors, which are deployed in the monitoring area by wireless communication,multi-hop and self-organizing. The information of the monitoring objects is acquisitionedand processed by the collaboration of sensor nodes, and then relays all the traffic to themonitoring center by the sink node. It is widely used in the fields of military,environmental monitoring and forecasting system, and medical health.Coverage control theory reduces the number of active nodes to form an optimizednetwork topology through sleep scheduling and density control, under the condition ofrequired degree of coverage and connectivity. And through the alternative working ofactive nodes and redundant nodes, it extends the lifetime of the entire network. Themulti-coverage problem is a hot research topic of coverage control theory.For the problem of multi-coverage under the case that the sensor nodes deployed inWSN are uniform and highly dense, this paper proposes a k coverage algorithm based onVoronoi diagram. This algorithm uses the information of neighbor’s nodes to establish theset called Voronoi Coverage Neighbor (VCN) node set, and then builds a directed graphreflecting the relationship of the coverage. According to the requirement coverage ofnetwork, it adjusts the state of nodes to achieve k coverage. Simulation results show thatthe algorithm can correctly determine the probability of network coverage with differentcoverage degree, and the number of approximate minimum active nodes is superior toCoverage Configuration Protocol (CCP) algorithm in the literature.For the problem of balanced energy depletion of connectivity and multi-coverage, thispaper analyzes the network performance of coverage and power consumption based onGaussian distribution. This paper gives the calculation model of the coverage probabilityand network lifetime, and analyzes the impact of network parameters and Gaussian parameters. Finally, this paper proposes the solution algorithm of the optimal Gaussianparameters. Simulation results show that the network lifetime increases by2timescompared to the case of randomly uniform distribution.