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Although wireless sensor networks have developed over many years, they are still a very forward-looking technology because humans hope for a “global village”; hence wireless sensor network technologies are necessary for replace manpower and improve communication. In addition, some areas of interest are difficult to reach or hazardous to the human body. It means that the people must place many sensors in these areas of interest so that they can collect, analyze or manage these sensors remotely. However, the network lifetime of wireless sensor networks is based on their battery power so that the sensors will be run out of power eventually. Therefore, ensuring the energy efficiency of wireless sensor networks has become an indispensable issue. Nevertheless, since no energy efficiency mechanisms can be regarded as a sustainable solution, the wireless rechargeable sensor network has been proposed, with the chargers deployed to provide needed power for the sensors. Deployment cost will increase when the number of the chargers increases, but it can help the whole wireless sensor network avoid being crashed. In other words, they have the trade-off relationship; as well, many metrics may impact the final policy for chargers deployment such as distance, transmission radius, power requirement of the sensors, and so on. It means that the chargers deployment problem is very complex and may not obtain the results in exponential time. Currently, the existing methods of charger deployment adopt a greedy-based operation so that the solutions will fall into the local optimum more easily, signifying that this topic still has some room for improvement.
In this thesis, we divide the charger deployment problem into two types: the fixed chargers placement problem and the mobile autonomous charging vehicle path selection problem. In the fixed chargers placement problem, we first propose a more efficient way for initiating the field of interest (FoI) to find the better candidate positions of chargers with lower computation costs. Based on that, we further designed the application of four metaheuristic algorithms to solve the local optimum problem. Since we know that metaheuristic algorithms always require more computation costs for escaping local optimum, to obtain better solution searching and fitness function calculating, we designed a framework to solve these problems via effectively reducing the solution space. In order to provide more flexible planning, we combined the charger and self-propelled vehicle, and then mapped the mobile chargers path selection problem into the travelling salesman problem (TSP). We also designed the application of four metaheuristic algorithms and enhanced them for finding the higher fitness value between the charging path and number of dead sensors. The simulation results show that the proposed method can achieve the best price–performance ratio. | en_US |