| Radio frequency identification(RFID)technology and wireless energy transmission technology are considered as a very promising technology for identifying object and tracking directions,for example,retail product management,smart warehouse applications,and industrial monitoring.However,in practical,due to the limited energy of the reader radius and wireless rechargeable sensor networks(WRSNs),the application cost of wireless technology is very expensive.The RFID network planning(RNP)and prolonging the lifetime of wireless rechargeable sensor networks is getting more and more attention.The RFID network planning is proposed to study how to determine the number and the locations of readers to optimize the performance of system.The problem of maximizing the lifetime of rechargeable networks is mainly divided into two directions: one-to-many charging model and not disjoint sets covers(NDSC)model.Existing researches are based on traditional algorithms and have high computational complexity.It is difficult to determine the number of readers and charging locations in the network,and is only suitable for smallscale problems.This thesis discusses the RFID network plannnig(RNP)and maximizing the lifetime optimization.The main work is summarized as follows:(1)A hybrid particle swarm algorithm for RFID network planning is proposed.Firstly,the model of RFID network is constructed by simulating the movement of particle swarms.The number of readers in the network is adaptively determined by k-means algorithm.Each reader is considered as a particle which have the initial position and power radius.At the same time,the parameters of the particle swarm algorithm are setted to evaluate the performance of the particles in the population and update the performance in a hierarchical manner.In addition,during the process of the particle swarm algorithm framework,the virtual force operators is introduced to adjust the position of the reader in each particle.The experimental results show that the hybrid particle swarm algorithm based on the RNP can achieve better performance in terms of the number of readers,interference,power and load-balance.(2)A dynamic multi-agent genetic algorithm for optimal charging in wireless rechargeable sensor networks is proposed.It can be seen as a practical application of RNP,that is,a mobile reader charge the sensors in the network.Firstly,each agent is considered as a candidate solution,which can compete or cooperate with neighbors.Secondly,the virtual force operator is introduced during the process of evolution to attract the reader to move toward the clustering centers of sensors obtained by the K-means algorithm.In addition,a special crossover operator is designed to dynamically adjust the number of reader charging positions in the network.Experimental results show that our algorithm is illustrated to be superior for WRSNs in terms of total charging time,maximum charging load,charging efficiency,and total charging distance.(3)A two-stage genetic algorithm under the mode of not disjoint sets covers(NDSC)for maximizing the lifetime of WRSNs is proposed.The lifetime of the sensor network is maximized by randomly deploying sensor locations and reasonably scheduling the working hours of sensor nodes.Firstly,the model of NDSC and the local search strategy is applied to determine the covers.The local search strategy is divided into two parts: full coverage set search strategy and redundant coverage set search strategy.At the same time,a two-stage genetic algorithm is proposed to maximize the network lifetime.At the first stage,the uniform distribution of sensor nodes in the full coverage set is considered as the objective function by adjusting the location of sensors.At the second stage,the objective function is to maximize the lifetime of the wireless sensor network by reasonably scheduling the working hours of the full coverage set.Experimental results show that our algorithm can make full use of sensor energy and effectively maximize the lifetime of wireless sensor networks. |
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