An Intelligent Multi-stage Channel Acquisition Model for CR-WBANs: A Context Aware Approach

channel acquisition.;hybrid cooperative spectrum sensing.;data transmission prioritization.;fuzzy logic.;neural network.;spectrum sensing accuracy;probability of channel acquisition.

Elgadi, Refga

University of Waterloo

关键词: channel acquisition.;    hybrid cooperative spectrum sensing.;    data transmission prioritization.;    fuzzy logic.;    neural network.;    spectrum sensing accuracy;    probability of channel acquisition.;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/12094/1/Elgadi-Refga.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Cognitive Radio (CR) came as a solution to mitigate challenges that wireless body area networks (WBANs) suffer from. CR is an intelligence-based technology that senses, observes, and learns from its operating environment to access licensed bands in the spectrum when they are not being utilized by primary users. Deploying a CR technology in WBANs applications, enhances spectrum scalability, increases system robustness, and decreases latency. Accordingly, CR-WBANs help in building a more efficient and reliable ubiquitous healthcare system than conventional WBANs do. However, CR-WBANs are still evolving, and many challenges need to be investigated, in particular, is how to acquire a channel and prioritize data streams among multiple CR-users (i.e., multiple patients) based on the severity of their health status, in a manner to decrease network latency and increase network scalability. To address this challenge, this work proposes a novel intelligent channel acquisition model for multiple CR-WBANs within ubiquitous healthcare system, whereby contextual data, namely, channel properties, intra-node characteristics, and patients’ profile information, is integrated in channel acquisition decision process. The proposed work is a multi-stage fusion system that is composed of local and global decisions units. A fuzzy logic system is utilized to make decisions in the local unit, which are sensing the channel availability and assessing the severity of patients;; health status. Moreover, a neural network is employed as a global sensing decision center, whereby local sensing decisions, channel properties, and intra-node characteristics are augmented in the decision process. Furthermore, a cluster-based heuristic algorithm is formulated, in the global decision unit, to prioritize data streams among CR-users based on the criticality of their health conditions (i.e., acute, urgent, and normal). Patients;; local health assessments and avatars (e.g., age, medical history, etc.) are exploited in the prioritization process.