Intelligent Biomedical Data Analysis and Processing
Remote health monitoring is one kind of E-health service, which transfer the users’ physiological data to the medical data center for analysis or diagnosis. Wireless body area network (WBAN) is a promising technology to achieve physiological information acquiring and delivering and thus has been widely adopted in remote health-monitoring applications. For WBAN, energy consumption is the major concern which has been addressed in many researches. Different from existing works, this work studies a joint scheduling and admission control problem with objective of optimizing the energy efficiency of both intra- and beyond-WBAN link. The problem is formulated as constrained Markov decision processes, and the relative value iteration and Lagrange multiplier approach are used to derive the optimal intelligent algorithm. Simulation results show the proposed algorithm is capable of, in comparison with greedy scheme, achieving nearly 100% throughput improvement in various power consumption budgets. Moreover, the proposed algorithm can achieve up to 5.5× power consumption saving for sensor node in comparison with other scheduling algorithms.
Constrained Markov decision processes (CMDP) Intelligent adaptive learning algorithm Joint intra- and beyond-WBAN Remote health monitoring Wireless body area network
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This work was supported in part by the National Natural Science Foundation of China (No. 61702497), Shenzhen Science and Technology Projects with Grant Number JCYJ20170412110753954.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
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