WSN低能耗数据收集遗传粒子群算法研究

发布时间：2018-05-03 00:29

本文选题：无线传感器网络 + 数据收集　；参考：《计算机科学》2017年03期

【摘要】：针对设施农业无线传感器网络节点分布不均匀、能量约束严格的特点,为降低网络总能耗,提出一种改进的遗传粒子群算法,构建一棵树高受限且网络总能耗最小的数据收集树。首先,随机生成连通图网络,采用父节点表示法将生成树编码成粒子;然后,设计一种随机生成数据收集树算法,随机产生满足树高限制的生成树;最后,考虑节点能耗均衡,设计一种粒子单点突变算法,实现对节点能耗最优值的比较。通过粒子单点变异、交叉以及优化新粒子,提高了种群多样性,避免了算法过早陷入局部最优解,在满足时延要求的同时,降低了网络总能耗。实验表明,与有树高约束的DL-DCT算法相比,所提算法降低了7.34%的网络总能耗,延长了网络平均生存期。
[Abstract]:In order to reduce the total energy consumption of the network, an improved genetic particle swarm optimization (PSO) algorithm is proposed to reduce the total energy consumption of the network. In order to reduce the total energy consumption of the network, a data collection tree with a tree height limited and the minimum total energy consumption of the network is constructed. First, the connected graph network is randomly generated and the tree is generated by the parent node representation. Then, a random generated data collection tree algorithm is designed to generate random tree which satisfies the height limit of the tree. Finally, considering the energy balance of the nodes, a single point mutation algorithm of particle is designed to achieve the comparison of the optimal value of energy consumption. The algorithm avoids the local optimal solution prematurely and reduces the total energy consumption of the network while meeting the delay requirement. The experiment shows that compared with the DL-DCT algorithm with high tree constraints, the proposed algorithm reduces the total energy consumption by 7.34% and prolongs the average lifetime of the network.

【作者单位】：东南大学移动通信国家重点实验室;徐州工业职业技术学院信息与电气工程学院;
【基金】：国家自然科学基金(6504030000) 移动通信国家重点实验室基金(2015A03) 徐州市科技发展基金(XF13C035) 院级科研课题基金(XGY201414)资助
【分类号】：TP212.9;TN929.5;TP18

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