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Research On WSN Coverage Optimization Based On Swarm Intelligence Algorith

Posted on:2024-07-09Degree:MasterType:Thesis
Country:ChinaCandidate:Y H HuangFull Text:PDF
GTID:2568307130459224Subject:Electronic information
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As the core foundation of the internet of things(Io T)technology,wireless sensor network(WSN)is widely used in various fields and has become one of the research hotspots in the field of information technology.Among them,because the good or bad node coverage optimization will affect the performance of the whole network,the coverage optimization problem of sensor nodes is one of the basic and critical problems in wireless sensor networks.The node coverage problem involves the optimization of node coverage,network connectivity,node coverage efficiency,and the number of nodes.However,for these coverage optimization problems,either single-objective or multi-objective optimization problems,finding the optimal solution to a single problem or a compromise solution to a multi-objective problem is a very complex and challenging research problem,most of which have been shown to be an NP-hard problem.For such problems,traditional algorithms are difficult to solve effectively,and the metaheuristic algorithm based on swarm intelligence can find a solution close to the optimal solution in a reasonable time using limited computational resources,providing an effective approach to the coverage optimization problem of wireless sensor networks.In this paper,combined with swarm intelligence algorithm,the single-objective and multi-objective optimization problems of node coverage are mainly studied as follows:(1)A multi-strategy improved coot bird swarm optimization algorithm(ICOOT)is proposed to address the problems of slow convergence speed and insufficient optimization performance of the coot bird swarm optimization algorithm(COOT).Firstly,the chaotic tent map is used to initialize the population,increase the diversity of the population.Secondly,the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population,and accelerate the convergence speed of the algorithm.Thirdly,Cauchy mutation and an opposition-based learning strategy are fused to further enhance the ability of the algorithm to jump out of the local optimum and improve the solution accuracy of the algorithm.Finally,23 benchmark functions are used to test the optimization performance of the algorithm and compare with other seven swarm intelligence algorithms.By analyzing the numerical results and convergence curves of the simulation experiments,it is shown that the ICOOT algorithm has reliable convergence speed and better global exploration ability.(2)A multi-strategy improved multi-objective artificial hummingbird algorithm(IMOAHA)is proposed to address the shortcomings of the multi-objective artificial hummingbird algorithm(MOAHA)in terms of solution diversity,convergence,and distributivity.Firstly,the chaotic cat map is used to initialize the population,increase the diversity of the population.Secondly,in the guided foraging stage of hummingbirds,the golden sine strategy is introduced to improve the local search ability of the algorithm and accelerate the convergence speed of the algorithm.Thirdly,in the territorial foraging stage,a nonlinear convergence factor strategy is introduced to effectively balance the global and local search capabilities of the algorithm.Finally,15 test functions are used to test the performance metrics of GD and IGD respectively,and compared with other multi-objective optimization algorithms.The simulation results verify the effectiveness of the IMOAHA algorithm.(3)For the single-objective optimization problem,each objective is optimized in turn using the ICOOT algorithm on the problems of node coverage rate,network connectivity,node coverage efficiency,and number of nodes,respectively.At the same time,these optimization problems are compared and analyzed with other swarm intelligence optimization algorithms on homogeneous and heterogeneous wireless sensor networks,respectively.Simulation results show that in terms of coverage optimization effect,the ICOOT algorithm can effectively improve the coverage rate and coverage efficiency of sensor nodes,improve the distribution of nodes,and improve the network connectivity of nodes compared with several other algorithms.Finally,the ICOOT algorithm is used for four typical node deployment application scenarios for practical situations.(4)For the multi-objective optimization problem,the IMOAHA algorithm is used to optimize the two objective functions of node coverage and network connectivity simultaneously,and compared with other multi-objective optimization algorithms.The simulation results show that the IMOAHA algorithm can obtain a better Pareto front surface for the multi-objective optimization problem of WSN node coverage,which means that it can provide decision makers with a wider range of decision options and reflects its practical application value more.Finally,for the actual application situation,40 Pareto solution set schemes are obtained using the IMOAHA algorithm for decision makers to make scheme selection,and three multi-objective node deployment schemes for typical scenarios are given.
Keywords/Search Tags:Wireless sensor network, Coot bird swarm optimization algorithm, Multi-objective artificial hummingbird algorithm, Node coverage optimization, Multi-objective optimization, Pareto optimal solution
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