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

Abstract

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.

Characteristics of the Temperature and Humidity Variations of Burial-Type Stone Relics and a Fitting Model

“…The model was utilized to further evaluate the effects of the heat storage temperature and initial soil moisture content on the heat transfer characteristics of buried pipes [16]. Liu et al [17] proposed an energybalanced routing protocol that could minimize the energy consumption of the network. An improved artificial intelligence population optimization algorithm combining particle swarm and ant colony optimization techniques was used to construct an environmental monitoring system for relic protection [17].…”

“…Liu et al [17] proposed an energybalanced routing protocol that could minimize the energy consumption of the network. An improved artificial intelligence population optimization algorithm combining particle swarm and ant colony optimization techniques was used to construct an environmental monitoring system for relic protection [17]. Cao and Li simulated and predicted the spatial and temporal patterns in 2030 and 2060 using the cellular automata (CA) model and landscape index and assessed the achievement of the carbon peaking and carbon neutrality targets [18].…”