Design Of Lightweight Secure Authentication Mechanism For Wireless Body Area Networks

Abstract

A state-of-the-art information technology that integrates wearable health monitoring newlinedevices with telemedicine systems may aid in the early detection of abnormal diseases newlineand mitigate their detrimental effects. When it comes to data transfer and newlinecommunication, wireless technology is vital. A wireless body area network newline(WBAN) is a sensor network that supports innovative patient healthcare monitoring. newlineWBANs are often referred to as body sensor networks (BSNs). There has been a lot of newlineWBAN research for continuous patient monitoring in recent years. Sadly, the current newlineWBAN does not provide patients with the needed QoS. To substantially impact newlineWBANs, QoS provisioning has to solve energy efficiency, congestion, and security newlinechallenges. Earlier researchers focused on it, but the protocol or approach becomes newlinecomplex when dealing with these problems. So, this research work aims to develop a newlinelightweight approach that focuses on all three challenges and handles them newlineeffectively. For this, the approach is developed by considering one issue at a time, and newlinefinally, by combining them, a novel approach is designed that solves the challenges newlineefficiently. Firstly an energy-efficient approach is developed using optimization newlinealgorithms, then a congestion-aware approach is proposed, which also utilizes soft newlinecomputing approaches, and finally, security is added using blockchain technology to newlinethis to propose a secure energy-efficient and congestion-aware approach. newlineThe first approach, dynamic cluster-based optimized path selection (DCOPS), is newlineproposed to deal with the energy efficiency challenges. DCOPS work in two phases, newline(a) Dynamic Clustering and (b) Energy Efficient Path Selection. First, it uses a newlinedragonfly algorithm (DFO) to divide the network into clusters; this clustering is called newlinedynamic because it is not fixed in size and not with fixed nodes. Then, it considers newlinefitness values and compares the current energy and distance of the nodes to add them newlineto a cluster. Next, further Adaptive Cuckoo Search Optimization (ACSO) selects the