Wireless sensor network node coverage optimization method based on Voronoi diagram for blind area
A wireless sensor and sensor node technology, applied in the field of optimization of wireless sensor network node coverage control, can solve problems such as blind spots in target points
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2014-11-19
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Abstract
Description
Technical field:
[0001] The present invention relates to a coverage optimization method in the technical field of wireless sensor networks, and in particular to an optimization method for coverage control of wireless sensor network nodes based on a Voronoi diagram. Background technique:
[0002] Wireless Sensor Networks (Wireless Sensor Networks, WSNs) is a multi-hop self-organizing network composed of a large number of static or mobile sensor nodes that are deployed in the monitoring area and have sensing, computing and communication capabilities. These sensor nodes can perceive, collect and process the information needed by users in real time, and send the processed information to the user terminal by means of wireless transmission. WSN involves multiple disciplines such as micro-electromechanical systems (MEMS), integrated circuits (IC), wireless communications, and low-power embedded systems. With its low power consumption, low cost, distributed and self-organizing character...
Examples
Embodiment Construction
[0064] The method of the invention will be further described below in conjunction with the accompanying drawings.
[0065] The wireless sensor network coverage optimization problem model is usually established in this way.
[0066] (1) Assuming that a two-dimensional plane T of size L×W is the monitoring area, randomly throw N sensor nodes S within the range of T, and move N sensor nodes S to maximize the coverage CR of the monitoring area T .
[0067] (2) Sensor node set S={s 1 ,s 2 ,...,S N }, all nodes are isomorphic nodes and have the same perception radius R s And communication radius R c , The position of each node is denoted as s i =(x i ,y i ).
[0068] Discretize the monitoring area T into a×b target points, and record the position of each target point as t j =(x j ,y j ), where j∈[1,a×b]. The higher the density of the target point, the higher the accuracy of the coverage.
[0069] The invention uses the Boolean perception model to calculate the perception probability Target...