Distributed energy‐efficient channel assignment in cognitive mesh network for IoT systems

Abstract

Internet of Things (IoT) has attracted a lot of attention to solve the real‐world problem to improve quality of life. Cognitive radio mesh networks (CRMNs) can provide connectivity to a large number of IoT nodes and have the potential to self‐organize and reconfigure to make efficient use of spectrum resources. The growing concern regarding energy consumption in communication infrastructure and low power IoT nodes has led researchers to regard minimizing of energy consumption. In this paper, we propose a distributed energy‐efficient channel assignment in CRMNs for the IoT systems. We formulate an optimization problem for channel assignment and sharing in CRMNs for IoT systems with an objective to maximize energy‐efficiency of the network. We consider constraints on both intra and inter‐link interference. We then propose two distributed approaches to solve the channel assignment problem: (1) greedy approach and (2) game theoretic approach while considering the scalability and self‐organization requirements of the CRMNs for the IoT systems. We then present extensive simulation results to demonstrate the performance of proposed schemes.

Multi‐objective spectrum assignment in heterogeneous cognitive radio networks for internet of things

“…In third case, if N < K, the N selected channels get assigned to N SUs, and the remaining K À N channels will not be used in this frame because each SU possesses a single antenna. In Equation (12), C4 indicates the interference temperature constraint, and x ik is the decision variable. Its value is either 0 or 1 where x ik ¼ 1 indicates that channel k is assigned to the i th SU.…”

“…Since the multi-objective problem given in Equation ( 12) is a Mixed Integer Linear Programming (MILP) problem, in this paper, we adopted ε-constraint to solve it. The problem in (12) can be written in ε-constraint form as follows:…”

“…Thus, it is necessary for SUs to be smart enough to be capable of sensing channel interference, learning from the environment, and having the capability to dynamically access the spectrum. [11][12][13] An SU should dynamically utilize the spectrum holes, and in order to do that, it should be able to adapt its operating parameters as per the requirements of the surrounding environment. During this whole process, it should not harm the interest of PUs.…”

“…In CRNs, unlicensed/secondary users (SUs) are allowed to access an idle/unused portion (spectrum holes) of the licensed spectrum as long as they do not interfere with the licensed/primary users (PUs). Thus, it is necessary for SUs to be smart enough to be capable of sensing channel interference, learning from the environment, and having the capability to dynamically access the spectrum 11–13 . An SU should dynamically utilize the spectrum holes, and in order to do that, it should be able to adapt its operating parameters as per the requirements of the surrounding environment.…”

Game theoretic application for energy efficient mobility handling in wireless sensor network

“…Game theory has been applied in many networks such as Internet of Things (IoT) cellular n/w, heterogeneous wireless n/w, cloud n/w, etc in vast potential to solve various issues. Power control and channel assignment problem in IoT cellular n/w have been solved by using non cooperative games in References 23,24 and cooperative game theory in Reference 25. Moreover, routing algorithm have been developed for IoT n/w by using game theory in Reference 26.…”