文献类型：期刊文献

英文题名：Energy-Aware Data Gathering Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization

作者：Zhang, Hong[1,2];Li, Zhanming[1]

第一作者：张红;Zhang, Hong

通信作者：Zhang, H[1];Zhang, H[2]

机构：[1]Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China;[2]Gansu Univ Chinese Med, Networks & Informat Adm Ctr, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Networks & Informat Adm Ctr, Lanzhou 730000, Peoples R China.|[10735]甘肃中医药大学;

年份：2020

卷号：8

起止页码：177219

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211210119491);Scopus(收录号：2-s2.0-85102822974);WOS:【SCI-EXPANDED(收录号:WOS:000577884600001)】；

基金：This work was supported in part by the Project of Gansu Province for Industrial and Information Development under Grant 23051358, and in part by the Project of Gansu Province for Guiding Scientific and Technological Innovation and Development under Grant 2018ZX-05.

语种：英文

外文关键词：Wireless sensor networks; Energy consumption; Data collection; Vegetation; Delays; Particle swarm optimization; Relays; Wireless sensor networks; data gathering; particle swarm optimization; energy efficiency

摘要：In order to mitigate the hot spot problem and prolong the network lifetime, data gathering with mobile sink is an effective measure to enhance the system performance. However, the movement strategy of sink node can be regarded as traveling salesman problem, which can hardly obtain the solution with polynomial running time. To address above problem, an energy-aware data gathering mechanism for mobile sink in wireless sensor networks using particle swarm optimization is introduced. Firstly, the mathematical model is established according to the total energy consumption and delay constraints for mobile sink's data collection. Then, the optimal rendezvous points are selected to aggregate data originated from the source nodes through multi-hop relay, and the aggregation tree will be constructed for data transmission. The spanning tree is encoded into particles, and the random method is designed to generate the data collection spanning tree with constrain of tree height limit. Furthermore, a particle swarm optimization strategy with adaptive elite mutation is designed to improve the population diversity and avoid falling into the local optimal solution prematurely. Experimental results show that the proposed method can meet the delay requirements and reduce the total energy consumption of the network.