Open AccessResearch on Energy Efficiency Application of Wireless Communication Energy Saving and Emission Reduction Based on Self-organizing Network Game Technology
Author(s) -
Shengqiang Bai,
Tao Zou,
Xibai Rong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/2/022019
Subject(s) - computer science , node (physics) , reliability (semiconductor) , game theory , database transaction , channel (broadcasting) , energy consumption , wireless , adaptability , efficient energy use , function (biology) , task (project management) , energy (signal processing) , wireless network , computer network , wireless ad hoc network , distributed computing , telecommunications , engineering , mathematics , ecology , physics , statistics , electrical engineering , structural engineering , systems engineering , quantum mechanics , evolutionary biology , biology , mathematical economics , programming language , power (physics)
Energy efficiency is a very important performance index in wireless ad hoc networks. So far, there is no universally accepted definition of the energy efficiency of self-organizing networks, and most of the conclusions drawn are asymptotic or qualitative, and its practicality is very limited. Aiming at this problem, a trust model based on game theory is proposed. The model introduces a time decay function to improve the accuracy and dynamic adaptability of trust evaluation, and introduces a node transaction density function in the recommendation trust calculation to calculate the recommendation performance of the recommended node. Reliability: In order to effectively encourage nodes to actively provide high-quality services and punish bad behaviour nodes, game theory is also introduced to analyse the trust value of nodes and decide whether to forward messages according to the analysis results. The simulation results show that the algorithm can adaptively deploy network nodes according to the distribution of task requirements, and quickly achieve the optimal layout.