| Summary: | 碩士 === 國立中正大學 === 通訊工程研究所 === 101 === Wireless sensor networks (WSNs) is one of the top ten emerging technologies of the 21st century, WSN can perform sensing and networking tasks and it has been employed in many applications, such as military, biomedical care, smart grid, and environment monitoring. Due to limited resources of the battery on the sensor nodes, how to effectively use the limited energy is one of the most critical issues for WSNs. Since sensors are densely deployed, which allow the WSN to prolong the network lifetime via scheduling and routing. Scheduling consider how to schedule the active and sleep period for each sensor node to save energy while maintaining full coverage of WSN. Routing means how to select routing path to balance energy consumption of the active node, as a result to prolong the network lifetime. Since full coverage and optimal routes are proved to be an NP-complete problem, many practitioners and researchers proposed an evolutionary algorithm to solve these issues. However, they seldom focus on Evolutionary algorithms to balance the residual energy to adjust sensor rang and optimal routes design of WSNs. In this thesis, we propose Genetic Algorithm (GA) to balance the residual energy to adjust sensor rang to meet full coverage, and Genetic Programming (GP) is used to create optimal routes to prolong the network lifetime to balance energy consumption of the active node. Experimental results show that the proposed heuristic scheme extends the network lifetime under various tested scenarios and effectively using the residual energy of the sensor nodes.
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