Fuzzy Based Cluster Greedy Optimization and Convolutional Neural Networks Based Scheme for Internet of Medical Things Based Healthcare Resource Allocation in Cognitive Wireless Powered Communication Network

Abstract

A cognitive wireless powered communication network (CWPCN) for spectrum distribution in IoMT based healthcare systems is employed with a principal network, which in turn deals with security issues from various attacks like Denial of Service (DoS), Man-In-the-Middle, or phishing attacks. In this, a new protocol is proposed for wireless powered SU (secondary users) so as to cooperate with PU (primary user) of the healthcare network. At the time of wireless power transfer (WPT) in a IoMT based healthcare network, the first harvest energy of SUs was carried from power signals broadcasted by the cognitive hybrid access point. Then the harvested energy is employed while gaining transmission opportunities simultaneously all through the phase of Wireless Information Transfer (WIT) of healthcare system. Furthermore, Fuzzy based cluster greedy algorithm is introduced for reducing the interruption of PU secrecy prospect and to offer the best optimal values in the healthcare data. In this approach, the injection impact and reactive jamming attacks on wireless transmission are analyzed. These can be recognized through a Convolutional Neural Network (CNN) to detect different attack types and classify them. Finally, the results were compared with the existing method.

Performance optimization of cognitive wireless powered communication network

“…Bi et al provided a comprehensive overview of wireless charging technology in WPCNs, given a comprehensive overview of the application of wireless charging in communication networks, and offered new ideas and directions for the future development of communication technology [3]. Bhuvaneswari et al proposed a new protocol for wireless power secondary users in order to work with primary users in medical networks, and introduced a fuzzy-based clustering greedy algorithm to reduce the disruption of the primary user's confidentiality prospect and provide the best optimal value in medical data [4]. Jafari et al studied the total throughput of secondary users in the in-band full-duplex cognitive wireless power backscatter communication network, and derived the closed-form expression of the optimal allocation time and energy of the secondary backscatter sensor, which was solved in a low-complexity and high-efficiency manner [5].…”