A Heuristic Algorithm for the Weighted-Critical-Square-Grid Coverage Problem in Wireless Sensor Networks

• Main Authors: Chang, Chia-Hsiang, 張家祥
• Other Authors: Sheu, Pi-Rong
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/v3t2y9

碩士 === 國立雲林科技大學 === 電機工程系 === 107 === In wireless sensor networks, the goal of the critical-square-grid coverage problem is to cover all the critical square grids with a minimum number of sensors. The goal of the weighted-critical-square-grid coverage problem is to cover some weighted critical square grids with a limited number of sensors to obtain the maximum profits. The goal of the two-level critical-square-grid coverage problem is to cover all the critical square grids with a limited number of sensors and to cover some weighted critical square grids with the remaining sensors to obtain the maximum profits. Some heuristic algorithms have been proposed to solve these problems. For example, STBCGCA (Steiner-Tree-Based Critical Grid Covering Algorithm) was proposed to solve the critical-square-grid coverage problem. Reduction+PBA (Reduction+Profit-Based Algorithm) was proposed to solve the weighted-critical-square-grid coverage problem. The two-level heuristic algorithm was proposed to solve the two-level critical-square-grid coverage problem. In this thesis, we discover that the two-level heuristic algorithm can also be used to solve the critical-square-grid coverage problem, but it cannot be used to solve the weighted-critical-square-grid coverage problem.Therefore, we modify the two-level heuristic algorithm such that it can also be used to solve the weighted-critical-square-grid coverage problem. We call the modified two-level heuristic algorithm the new two-level heuristic algorithm.In this thesis, based on the original two-level heuristic algorithm, we design a new heuristic algorithm to solve the weighted-critical-square-grid coverage problem. Our heuristic algorithm improves two aspects of the new two-level heuristic algorithm. First, in contrast to the new two-level heuristic algorithm where any location capable of deploying a sensor is given a weight, our heuristic algorithm gives any such location a new weight. This improvement allows our heuristic algorithm to choose the shortest path which passes or is close to the weighted critical square grids, each of which has a larger profit, when building a tree. Second, in contrast to the new two-level heuristic algorithm where the weighted critical square grid with the largest profit is selected as the first weighted critical square grid to be covered, our heuristic algorithm selects the weighted critical square grid with the largest new weight as the first weighted critical square grid to be covered. This improvement gives our heuristic algorithm a better chance of obtaining a larger sum of profit when covering the weighted critical square grids. Finally, in this thesis, we use computer simulations to analyze and compare the performance of our heuristic algorithm with that of Reduction+PBA and that of the new two-level heuristic algorithm. Computer simulation results show that compared with Reduction+PBA and the new two-level heuristic algorithm, our heuristic algorithm can get a larger sum of profit.