Fuzzy based clustering in CWPSN using machine learning model
researchers have addressed the problems in the lack of radio spectrum availability and enabled the allocation of dynamic spectrumaccess in specific fields. The main challenge has been to support the radio spectrum allocation using intelligent adaptive learningand decision-making techniques so that various requirements of 5G wireless networks can be encountered. Machine learning (ML)is one of the most promising artificial intelligence tools conceived to support cognitive wireless networks. This paper aims toprovide energy optimization and enhance security to cognitive wireless power sensor networks using a novel protocol duringresource allocation. In addition to the existing methods, a novel protocol, fuzzy cluster-based greedy algorithms for attackprediction and energy harvesting using a machine-language model based on neural network techniques have been introduced. Thesimulation has been done using MATLAB software tools which gives efficient results.
Energy harvesting, Greedy algorithm, CNN, Primary user, Machine learning, Artificial intelligence
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