LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction

• Published in: Applied sciences Vol. 11; no. 19; p. 9064
• Main Authors: Fu, Chunling, Zhou, Lin, Hu, Zhentao, Jin, Yong, Bai, Ke, Wang, Chen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Accuracy; cluster head selection; Energy consumption; energy optimization; Genetic algorithms; node sleep strategy; Noise; Sensors; state prediction; Wireless networks; wireless sensor network
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11199064

When some nodes cooperatively track moving targets in a wireless sensor network, some things including network working node selection and network energy consumption are influenced. Thus, this paper proposes an improved algorithm LEACH-MTC (LEACH with Moving Target Constraint) based on low energy adaptive clustering hierarchy protocol (LEACH). First, based on the two-step linearization of the nonlinear dynamic model, the state of the nonlinear moving target is predicted by the extended Kalman filter (EKF). Second, combining the state prediction of the moving target and the performance of collaborative monitoring, this paper constructs an ellipse monitoring area of some working nodes to consist with the direction of the target movement. Subsequently, the node sleep strategy corresponding to the state prediction of moving target is designed. Finally, the cluster head selection strategy is proposed based on energy balance utilizing the state prediction of the moving target. Simulation results show that the proposed LEACH-MTC algorithm can not only ensure the real-time consistency between the changing direction of area and the direction of target movement, but also increase the number of working nodes’ survival and reduce the network energy consumption.