Research On Applications Of Controllable Mobility In Wireless Sensor Networks

With the development boom of communication, sensor and computer technology, wireless sensor networks have been proposed as a new method for information gathering. Recent years, novel approaches of utilizing controllable mobile elements for network control and network optimization have been studied. Mobile elements in the network are considered being able to balance the energy cost among different sensors and to prolong the network lifetime. In this thesis, we do research on the application of controllable mobility in wireless sensor networks with the aim of repairing coverage hole, reducing energy consumption and extending network lifetime. The main contributions of our work are summarized as follows:1) In order to repair coverage hole and balance sensor energy without affecting energy efficiency of the network, controllable mobile elements are introduced into wireless sensor networks. By taking the advantages of mobile sensors with controllable mobility, we design a multi-objective optimization problem for sensor relocation and propose controllable mobility scheme based on genetic algorithms(CMSGA). Our relocation strategy CMSGA follows the searching mechanism of non-dominated sorting genetic algorithm-II(NSGA-II). Fitness value converges to its best after a certain number of iterations, and relocation scheme of mobile sensors is then obtained. Simulation results show that relocation strategy CMSGA with mobile sensors performs better on repairing coverage hole and extending network lifetime in contrast with the counterpart without sensor relocation.2) Novel methods which use unmanned aerial vehicle(UAV) as mobile data collector have been proposed recently. Aiming at optimizing energy consumption and prolonging network lifetime, we consider a framework of layered data collecting in UAV-assisted method and propose UAV-assisted data collecting based on genetic algorithm(UADC-GA). For the realization of UADC-GA, data collecting algorithm is divided into two phases including way point selecting strategy and path planning strategy. In way point selecting phase, UADC-GA jointly considers the optimization of energy consumption and energy balance in fitness function. Optimal way point scheme is obtained by the searching mechanism of genetic algorithm(GA). In path planning phase, for minimizing the flight distance of UAV, ant colony algorithm is introduced for shortest path finding. Simulation results show that the proposed algorithm UADC-GA effectively balances the sensor energy and prolongs the network lifetime without significantly increasing the flight distance of UAV. In addition, UADC-GA performs closer to the theoretical results of system analysis than UADC-LEACH does.