Efficient Evolutionary Techniques for Wireless Body Area Using Cognitive Radio Networks

Abstract

The wireless body area network (WBAN) has certainly been one of the fastest growing sectors nowadays since wireless applications have gradually been on the increase, which results in various wireless body area applications and systems that are operating in unlicensed spectrum bands toward overcrowding of spectral bands and being left out with scarce spectrum space. The radio-frequency spectrums are allocated in advance, and it has been difficult in finding vacant spectral bands for deploying new services or enhancing existing ones. The current amount of scarcity in the available spectrum is primarily due to inefficient fixed frequency allocations rather than a physical shortage in the spectrum. Inefficient spectrum utilization forces toward building an enhanced communication paradigm called cognitive radio (CR) system that adapts dynamically to the environment by learning from its past experience. The wireless BAN system assumes that the primary user’s signal does not change periodically until the channel is opportunistically used by a secondary user. To overcome the aforesaid problems of wireless body area networks, cognitive radio-enabled WBAN is devised by proposing an evolutionary decision fusion algorithm called particle swarm optimization for (i) efficient battery utilization and (ii) uninterrupted data transfer in BAN through efficient spectrum management for critical medical wireless application networks.