Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Abstract

Wireless body area networks (WBAN) are becoming a promising solution for health care applications. WBAN allows monitoring of patients continuously in their own comfort zone. These devices use the industrial, scientific, and medical band (ISM) for communication. This band is overcrowded due to the increasing number of wireless medical devices and other wireless devices occupying this band. This causes interference, which can be damaging and could result in a change in received power. However, WBAN also needs minimum and reliable energy communication for a longer lifetime and improved quality of service. This work addresses both problems and proposes solutions for the same. A cognitive radio controller is employed as a centralized controller with dynamic spectrum allocation properties to mitigate the interference. The sensing of the spectrum is based on compressed sensing with a nonreconstruction model to save energy. To quantify interference measurement, the interference mitigation factor is introduced. Further, to increase energy efficiency, the K-means algorithm is used to cluster WBANs. However, critical emergency data and normal data are categorized as priority data and normal data, respectively, by the proposed priority scheduling algorithm. The performance of this cognitive radio-based system for telemedicine applications is analyzed through simulations. The simulations are performed using MATLAB 2019.