Heterogeneous directed sensor network node scheduling method oriented to different priority targets

Abstract

The invention discloses a heterogeneous directed sensor network node scheduling method oriented to different priority targets. The method comprises the following steps: converting a heterogeneous directed sensor network node scheduling problem into a set coverage problem, and solving the same by adopting a genetic algorithm, realizing the self-adaptive selection of variation parameters and crossover factors in the genetic algorithm by importing a learning automation into the genetic algorithm, thereby improving the convergence capacity and the optimizing capacity of the genetic capacity, and realizing the target of prolonging the life cycle of a network in the premise of guaranteeing the requirement target of the monitoring target. The method has the advantages that the algorithm is good in convergence, string in optimizing capacity and long in network life.

Description

Technical field [0001] The invention relates to the technical field of directed sensor networks, in particular to a method for scheduling heterogeneous directed sensor network nodes facing different priority targets. Background technique [0002] The energy problem is an important problem of directed sensor networks and a key factor restricting its application. Node scheduling achieves the purpose of improving network energy utilization efficiency and extending network service time by switching between node working and sleep states. [0003] In recent years, the directional sensor node scheduling algorithm has attracted more and more researchers' attention and has achieved certain results. However, the existing research results have the following limitations: [0004] First, most of the existing scheduling algorithms are equally important for monitoring targets, which means that some monitoring targets may be more important than other monitoring targets in actual monitoring, that i...

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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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