A New Strategy for Dynamic Channel Allocation in CR-WMN Based on RCA

Channel assignment has emerged as an essential study subject in Cognitive Radio-basedWireless Mesh Networks(CR-WMN).In an era of alarming increase in Multi-Radio Multi-Channel(MRMC)network expansion interference is decreased and network throughput is significantly increased when non-overlapping or partially overlapping channels are correctly integrated.Because of its ad hoc behavior,dynamic channel assignment outperforms static channel assignment.Interference reduces network throughput in the CR-WMN.As a result,there is an extensive research gap for an algorithm that dynamically distributes channels while accounting for all types of interference.This work presents a method for dynamic channel allocations using unsupervisedMachine Learning(ML)that considers both coordinated and uncoordinated interference.Unsupervised machine learning uses coordinated and non-coordinated interference for dynamic channel allocation.To determine the applicability of the proposed strategy in reducing channel interference while increasingWMNthroughput,a comparison analysis was performed.When the simulation results of our proposed algorithm are compared to those of the Routing Channel Assignment(RCA)algorithm,the throughput of our proposed algorithm has increased by 34%compared to both coordinated and non-coordinated interferences.

Big Data of Home Energy Management in Cloud Computing

A smart grid is the evolved form of the power grid with the integration of sensing,communication,computing,monitoring,and control technologies.These technologies make the power grid reliable,efficient,and economical.However,the smartness boosts the volume of data in the smart grid.To obligate full benefits,big data has attractive techniques to process and analyze smart grid data.This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid.The offered framework comprises of the following four layers:(i)Data source layer,(ii)Data transmission layer,(iii)Data storage and computing layer,and(iv)Data analysis layer.As a proof of concept,the framework is simulated by taking the dataset of three cities of the Pakistan region and by considering two cloud-based data centers.The results are analyzed by taking into account the following parameters:(i)Heavy load data center,(ii)The impact of peak hour,(iii)High network delay,and(iv)The low network delay.The presented framework may help the power grid to achieve reliability,sustainability,and cost-efficiency for both the users and service providers.