TEMPO 3.1: A Body Area Sensor Network Platform for Continuous Movement Assessment
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Markov decision processes for control of a sensor network-based health monitoring system
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Markov decision process (MDP) framework for optimizing software on mobile phones
EMSOFT '09 Proceedings of the seventh ACM international conference on Embedded software
Markov-optimal sensing policy for user state estimation in mobile devices
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Online Data and Execution Profiling for Dynamic Energy-Fidelity Optimization in Body Sensor Networks
BSN '10 Proceedings of the 2010 International Conference on Body Sensor Networks
Optimal Resource Allocation for Pervasive Health Monitoring Systems with Body Sensor Networks
IEEE Transactions on Mobile Computing
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Body sensor networks (BSNs) often operate in dynamic environments, with the collected data profiles---and the resulting importance of data---varying throughout the system runtime. Therefore, the potential for power reduction fluctuates with changing user behavior, creating a dynamic battery lifetime-fidelity relationship that is subject to variations throughout the battery lifetime corresponding to an individual's daily activities---past, present, and future. This paper explores the potential for optimizing the tradeoff between meeting a desired battery lifetime and maximizing system fidelity through run-time adaptation of sensor acquisition (duty cycling) and profile-based predictions of an individual's future activities. A "personal activity profile" describes the expected behavior of an individual and is used to inform the desired battery discharge characteristics over time. Using walking activity traces collected from three human subjects wearing Fitbit® trackers over several months in order to develop such activity profiles, the approach is demonstrated in simulation based based on an analytical power model for an inertial BSN platform incorporating recent sensors. Results show improvements over statically setting a duty cycle for constant power consumption with respect to ideally setting the duty cycle based upon a priori knowledge of activities of interest throughout the system lifetime.