An energy cooperation method of wireless sensor networks based on partially observable Markov decision processes. (February 2023)

• Record Type: Journal Article
• Authors: Zhang, Qin; Liu, Yutang
• Is Part Of: Sustainable energy technologies and assessments. Volume 55(2023)
• Publication Date: 2023-02
• Subjects: POMDP -- WSN -- Energy cooperation -- Target following; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102997
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: The performance of routing protocols in wireless sensor networks, such as energy conservation, energy and load balancing. These indicators have a very important impact on the network lifetime. Energy cooperation technology refers to the purposeful transfer of energy from nodes with sufficient energy to nodes with insufficient energy. Firstly, according to POMDP (Partially Observable Markov Decision Process) model, related theories and solving algorithms, this paper proposes an energy cooperation method based on POMDP, establishes an interference decision model based on POMDP, and solves the model. This paper proposes an energy balance routing algorithm based on multi-attribute decision making (EBR_MDM). This algorithm is used to solve the application problem of wireless sensor networks with stable data flow. Including the uneven distribution of network energy, hot spots and energy holes, packet loss caused by node overload and retransmission energy consumption. According to the relative entropy, the attribute weight is calculated, and the optimal forward data is selected to the neighboring nodes. The simulation results show that the network running time of EBR_MDM is increased by 22.1 %, 48.7 % and 101.3 % respectively compared with other comparison protocols. Finally, because the sensor nodes are randomly arranged in the predetermined monitoring area, and the distribution of each node is chaotic, this paper establishes TMA (Trilateral Measurement Algorithm) algorithm to locate the target. The simulation results show that the positioning error of TMA may be about 1.2 m due to trilateration, but it is still within an acceptable range.