An Application of Grey Prediction to Transmission Power Control in Mobile Sensor Networks
Jin‐Shyan Lee, Yun-Chen Lee · IEEE Internet of Things Journal · 2018
Energy management in wireless sensor networks is an important issue for the Internet of Things applications. Transmission power control (TPC) is one of the approaches to conserve energy in sensor networks. However, most existing TPC methods assume sensor nodes are stationary after deployment and thus do not support mobile sensing environments. In this paper, a predictive TPC approach, namely grey-fuzzy-logicbased TPC (grey-FTPC), has been proposed for mobile sensor networks using grey prediction and fuzzy inference systems. Simulation results indicate that the proposed approach not only reduces the energy consumption, but also maintains a proper packet delivery ratio (PDR) for mobile sensing environments. On average, the proposed grey-FTPC achieves 92.3% of the PDR of the conventional maximum transmission energy (MTE) approach, but consumes only 57.4% of the transmission energy of the MTE. Moreover, the grey-FTPC approach has an approximate performance compared with the Markov-based TPC, but does not require exhaustive experimental data to build models. Hence, it is believed that the proposed grey-FTPC approach could further be applied to highly mobile sensing environments.