Wireless sensor network node coverage optimization method and apparatus

Abstract

The present invention discloses a wireless sensor network node coverage optimization method and apparatus. According to the wireless sensor network node coverage optimization method provided by the present invention, a genetic algorithm and a simulated annealing algorithm are improved; the improved genetic algorithm and improved simulated annealing algorithm are combined, so that the possibility of local convergence can be lowered; a condition for terminating evolution is set to terminate the algorithm when total evolution generations are equal to preset evolution generations LG or when an optimal fitness value in the populations of successive LG / 4 generations does not change, and therefore, the problem of the local optimum of the algorithm caused by too few update generations and the problem of low efficiency of the algorithm caused by two many update times can be solved; the local search of wireless sensor network node coverage can be enhanced; convergence speed can be improved; a calculation amount can be decreased; and optimization efficiency can be improved.

Description

technical field [0001] The present application relates to the field of wireless communication technology, in particular to a method and device for optimizing the coverage of wireless sensor network nodes. Background technique [0002] Wireless Sensor Network (WSN) is a distributed sensing network composed of small smart devices with computing, storage and wireless communication capabilities. The sensing nodes (sensors) in the network have self-organizing characteristics, and can complete the transfer of information and data in a specific area or in a specific environment by adopting a multi-hop routing and forwarding method. Due to the flexible and fast networking characteristics of the sensor network, reliable and highly adaptable survivability, it is widely used in military and civilian fields. [0003] There are two ways of initial node distribution in wireless sensor networks, one is large-scale random deployment, and the other is purposeful deployment for specific purp...

AI Technical Summary

Problems solved by technology

[0004] At present, genetic algorithm (GA, Genetic Algorithms) and simulated annealing algorithm (SA, Simulated Annealing) are used to solve the optimization problem of wireless sensor network nodes. Although genetic algorithm has good global search ability, it has made great achievements in optimizing wireless sensor network node coverage. A certain optimization effect, but due to the defects of genetic algorithm itself, using genetic algorithm to optimize wireless sensor network nodes has poor local search ability, which is prone to premature convergence of the algorithm

The simulated annealing algorithm starts from a certain higher temperature, which is called the initial temperature. With the continuous decrease of the temperature parameter, the solution in the algorithm tends to be stable, but such a stable solution may be a local optimal solution. At this time , the simulated annealing algorithm will jump out of such a local optimal solution with a certain probability to find the global optimal solution of the objective function. Although the existing simulated annealing algorithm uses the Monte Carlo (Metropolis) probability acceptance criterion, it has a strong Local search ability can avoid falling into local optimal solution, but there are problems of slow convergence speed, large amount of calculation and low efficiency

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