Game Theory Based Distributed Optimization On Energy Efficiency In Internet Of Things

The rapid development of Internet of Things(IoT)provide the modern society a huge plat for information exchange,based on the universal connect of everything.This is the further development of the information obtain and share.In addition,the rapid development of wireless communication,supports the larger scale information obtain and information exchange everywhere.Compared with the wired communication based IoT network,the wireless communication based IoT network promotes the IoT to all aspect of life anywhere and anytime.However,wireless communication supported IoT also faces the constrain from the energy,because the energy limited battery is usually used.Then the research on energy efficiency is practical and widely focused.This paper studies on the optimization of the energy efficient in the IoT.We focus on the globally energy optimization of system in a distributed manner.The main context is as follow:First,to improve the system's energy efficiency on the choice of the wireless communication channel,we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game.Then we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner.The proposed algorithm is proved to asymptotically converge to the global optimum with geometric speed.The simulations results verify the theoretic analysis and the performance of proposed algorithm..Second,to improve the energy efficiency on the choice of access point(AP)in the IoT,we formulate an energy oriented AP selection problem and propose a game model which is proved to be an exact potential game.Then we design a distributed learning algorithm to obtain the globally optimal solution to the problem in a distributed manner.Simulation results verify the theoretic analysis and show that the proposed approach could achieve much higher energy efficiency.