基于改进粒子群算法和蚁群算法的博物馆文物环境监测能量均衡路由协议

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhouzhou Liu, Yangmei Zhang, Han Peng
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引用次数: 0

摘要

博物馆中基于无线传感器网络的文物环境监测需要海量数据的传输和处理,这反过来又导致了网络流量大、时延高、节点能耗不平衡等问题。为了解决这些问题,提出了一种能量平衡路由协议,该协议可以最大限度地减少网络能耗。采用粒子群优化与蚁群优化相结合的改进群智能优化算法,构建了文物保护环境监测系统。仿真结果表明,与其他经典算法相比,该算法的数据传输路径长度减少了2.5%,表明该算法可以有效地减少网络路径长度,提高网络系统的传输性能。从优化轮次和模拟时间来看,与其他经典算法相比,我们算法下的网络系统能耗降低了57.5%,平均剩余能量方差降低了57.1%。基于该路由协议的文物环境监测系统具有较好的网络负载均衡性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

The environmental monitoring of cultural relics based on wireless sensor networks in museums demands for the transmission and processing of massive data, which in turn leads to problems, such as heavy network traffic, high time delay, and unbalanced node energy consumption. To solve these problems, an energy balanced routing protocol which can minimise the network energy consumption is proposed. The improved swarm intelligence optimisation algorithm which combined particle swarm optimisation with ant colony optimisation is adopted to construct an environmental monitoring system for cultural relics preservation. Simulation results show that the decrease of the path length for data transmission under the proposed algorithm is 2.5% higher than that of other classical algorithms, indicating that the proposed algorithm can effectively reduce the length of network path and improve the transmission performance of network system. In view of optimisation rounds and simulation time, the energy consumption of the network system under our algorithm is 57.5% less compared with other classical algorithms, and the average residual energy variance is 57.1% less. The cultural relics environmental monitoring system based on the proposed routing protocol has better network load balancing performance.

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来源期刊
IET Smart Cities
IET Smart Cities Social Sciences-Urban Studies
CiteScore
7.70
自引率
3.20%
发文量
25
审稿时长
21 weeks
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