Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Dimensions: why do we need a new data handling architecture for sensor networks?
ACM SIGCOMM Computer Communication Review
Adaptive Power-Fidelity in Energy-Aware Wireless Embedded Systems
RTSS '01 Proceedings of the 22nd IEEE Real-Time Systems Symposium
Energy-aware lossless data compression
ACM Transactions on Computer Systems (TOCS)
Data compression algorithms for energy-constrained devices in delay tolerant networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Accurate prediction of power consumption in sensor networks
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
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Energy-fidelity tradeoffs are central to many battery-constrained systems, but they are essential in body area sensor networks (BASNs) due to energy and resource constraints, and the critical nature of many healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques can incur a reduction in application fidelity. In order to maximize system performance, these tradeoffs must be considered at run-time due to the variable nature of BASN application, including sensed data, operating environments, user actuation, etc. BASNs therefore require energy-fidelity scalability, so automated and user-initiated tradeoff decisions can be made dynamically. This paper explores the utility of energy-fidelity scalability in BASNs from a data-centric perspective. Compression algorithms are identified that can be implemented on resource constrained BASN nodes and that have "knobs" capable of trading off compression ratios (and resulting transmission energy) with fidelity. To demonstrate the potential of energy-fidelity scalability on a real BASN and for a real application, the tradeoff space is established by adjusting these algorithms for different movement disorder data sets collected by a custom accelerometer-based BASN. Finally, mechanisms for energy-fidelity dynamic control are explored.