Research challenges in wireless networks of biomedical sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Energy Scavenging for Mobile and Wireless Electronics
IEEE Pervasive Computing
Analysis of the Severity of Dyskinesia in Patients with Parkinson's Disease via Wearable Sensors
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
IEEE Transactions on Mobile Computing
Critical-Path based Low-Energy Scheduling Algorithms for Body Area Network Systems
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Cooperative and Reliable ARQ Protocols for Energy Harvesting Wireless Sensor Nodes
IEEE Transactions on Wireless Communications
Dynamic routing trees with energy harvesting constraints for wireless body area networks
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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This paper addresses the problem of developing energy efficient transmission strategies for Body Sensor Networks (BSNs) with energy harvesting. It is assumed that multiple transmission modes that allow a tradeoff between the energy consumption and packet error probability are available to the sensor nodes. Taking into account the energy harvesting capabilities of the nodes, decision policies are developed to determine the transmission mode to use at a given instant of time in order to maximize the quality of coverage. The problem is formulated as a Markov Decision Process (MDP) and the performance of the transmission policy thus derived is compared with that of energy balancing as well as aggressive policies. An upper bound on the performance of arbitrary policies, and lower bounds specific to energy balancing and aggressive policies are derived.