A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare ApplicationsReport as inadecuate


A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications


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Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia





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Academic Editors: Giancarlo Fortino, Hassan Gasemzadeh, Wenfeng Li, Yin Zhang and Luca Benini

Abstract Wireless body area networks are increasingly featuring cognitive capabilities. This work deals with the emerging concept of cognitive body area networks. In particular, the paper addresses two important issues, namely spectrum sharing and interferences. We propose methods for channel and power allocation. The former builds upon a reinforcement learning mechanism, whereas the latter is based on convex optimization. Furthermore, we also propose a mathematical channel model for off-body communication links in line with the IEEE 802.15.6 standard. Simulation results for a nursing home scenario show that the proposed approach yields the best performance in terms of throughput and QoS for dynamic environments. For example, in a highly demanding scenario our approach can provide throughput up to 7 Mbps, while giving an average of 97.2% of time QoS satisfaction in terms of throughput. Simulation results also show that the power optimization algorithm enables reducing transmission power by approximately 4.5 dBm, thereby sensibly and significantly reducing interference. View Full-Text

Keywords: cognitive body area network; reinforcement learning; channel allocation; power allocation; channel model; wireless body area network cognitive body area network; reinforcement learning; channel allocation; power allocation; channel model; wireless body area network





Author: Tauseef Ahmed * and Yannick Le Moullec

Source: http://mdpi.com/



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