Node Scheduling Method for Heterogeneous Directed Sensor Networks Oriented to Different Priority Targets

Abstract

The invention discloses a heterogeneous directional sensor network node scheduling method for different priority targets, which converts the heterogeneous directional node scheduling problem into a set coverage problem and uses a genetic algorithm to solve it, and introduces the learning automata into the genetic algorithm In the process, the adaptive selection of mutation parameters and crossover factors in the genetic algorithm is realized, thereby improving the convergence ability and optimization ability of the genetic algorithm, and then realizing the goal of extending the life cycle of the network under the premise of ensuring the monitoring target coverage requirements . Beneficial effects: the algorithm has good convergence, strong optimization ability, and long network life.

Description

technical field [0001] The invention relates to the technical field of directed sensor networks, in particular to a heterogeneous directed sensor network node scheduling method for different priority targets. Background technique [0002] Energy problem is an important problem in directed sensor network and a key factor restricting its application. Node scheduling achieves the purpose of improving network energy utilization efficiency and prolonging network service time by switching between node working and dormant states. [0003] In recent years, directed sensor node scheduling algorithm has been paid more and more attention by researchers, and has achieved certain results. However, existing research results have the following limitations: [0004] First, most of the existing scheduling algorithms have equal importance for monitoring targets, that is, they ignore that some monitoring targets may be more important than other monitoring targets in real monitoring, that is,...

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Problems solved by technology

[0005] Second, most of the existing directional sensor network scheduling algorithms assume that the parameters of the directional sensor nodes participating in the scheduling operation are the same, and the same parameters include: sensing radius, sensing angle, communication radius and energy, etc.

However, the performance of its algorithm depends heavily on the key parameters in the algorithm, such as crossover probability and mutation probability. At the same time, these parameters can only be determined by experience and set as constants, and cannot be adaptively selected according to the characteristics of the problem to be solved, which affects its optimization. performance

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