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Research On Multi-objective Optimization Of Cooperative Beamforming In Uav-assisted Node Mobile Wireless Sensor Networks

Posted on:2022-12-29Degree:MasterType:Thesis
Country:ChinaCandidate:Y X WangFull Text:PDF
GTID:2518306758491884Subject:Automation Technology
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Mobile wireless sensor networks(MWSNs)are often constrained in terms of both transmission energy and communication distance,because the energy of sensor nodes and the cost of hardware equipped on nodes are limited.UAV-assisted MWSNs have attracted growing attention in enhancing the performance of networks since unmanned aerial vehicles can act as the aerial base stations and have the autonomous nature to collect data.In this paper,considering the scenario that several unmanned aerial vehicles are distributed as aerial base stations,we consider to construct a virtual array antenna consists of mobile sensor nodes and adopt the distributed cooperative beamforming(DCB)to achieve the long-distance and efficient uplink data transmissions with the aerial base stations.Since the energy of antenna is limited and the mainlobe and sidelobe of the direction pattern influence and restrict each other,so the sidelobe levels of the directional pattern are the key factor of the energy loss.In addition,the excitation current of each element in the array antenna is the most direct factor affecting the sidelobe level suppression of the directional pattern.Therefore,in order to reduce the maximum sidelobe levels,when communicating with different aerial base stations eachtime,the mobile sensor nodes can move to the position with the optimal excitation current weight to form a highly directional beam,so as to ensure the maximum data transmission rate.However,the side effect is the generation of motion energy consumption.And the different communication order also directly determines the motion energy consumption.Thus,we formulate a high data transmission rate multi-objective optimization problem of the UAV-assisted MWSNs based DCB to simultaneously maximizes the transmission rate,minimizes maximum sidelobe level and minimizes motion power consumption,so as to increase the energy efficiency of the network,as follows:(1)For the scenario of static deployment of multiple aerial UAV base stations,we formulate a high data transmission rate multi-objective optimization problem based on static aerial base stations by jointly optimizing the positions and excitation current weights of mobile sensor nodes,and the order of communicating with different aerial base stations.At the same time,this paper verifies that the proposed problem is NP hard,and the solution of the problem contains both continuous and discrete properties.However,the traditional multi-objective optimization algorithms are proposed for continuous solution space,and can not be directly used to update the solution in discrete space.Therefore,we propose an improved non-dominated sorting genetic algorithm-?(INSGA-?)which uses a hybrid solution update strategy of Order Crossover and Simple Inversion Mutation to make the traditional algorithm suitable for solving the problem.INSGA-? improves the global search performance and population diversity by introducing the chaos initialization and average grade mechanism.In addition,the effectiveness of the algorithm is verified under the conditions of different array element sizes.The simulation results show that the INSGA-? algorithm has the best performance for the optimization problem.(2)For the scenario of dynamic deployment of multiple aerial UAV base stations,considering the characteristics of strong mobility of unmanned aerial vehicles,they can also move to the optimal position in order to obtain a highly directional pattern in each communication.Therefore,we formulate a high data transmission rate multi-objective optimization problem based on dynamic aerial base stations by jointly optimizing the positions and excitation current weights of mobile sensor nodes,the positions of unmanned aerial vehicles and the order of communicating with different aerial base stations.However,the motion energy consumption at this time is generated by the sensor nodes and the unmanned aerial vehicles.Therefore,we propose an improved multi-objective dragonfly algorithm(IMODA)which uses a hybrid solution update strategy of Partially Matching Crossover and Exchange Mutation to make the algorithm suitable for solving the problem.IMODA introduces Sinusoidal chaos operator to initialize the solution,which improve the performance of the initial solution.The simulation results show that IMODA algorithm can still effectively solve the problem under different array element sizes,and it has better performance than other optimization algorithms.
Keywords/Search Tags:Mobile wireless sensor networks, unmanned aerial vehicles, collaborative beamforming, non-dominated sorting genetic algorithm-?, multi-objective dragonfly algorithm, multi-objective optimization problem
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