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 sampling for sensor networks
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
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
Resource aware programming in the Pixie OS
Proceedings of the 6th ACM conference on Embedded network sensor systems
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
Neural Network Gait Classification for On-Body Inertial Sensors
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Toward unsupervised activity discovery using multi-dimensional motif detection in time series
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IEEE Transactions on Multimedia
IEEE Transactions on Information Technology in Biomedicine
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
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Energy-fidelity trade-offs are central to the performance of many technologies, but they are essential in wireless body area sensor networks (BASNs) due to severe energy and processing constraints and the critical nature of certain 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, capable of high compression ratios, can incur a reduction in application fidelity. In order to maximize system performance, these trade-offs must be considered at runtime due to the dynamic nature of BASN applications, including sensed data, operating environments, user actuation, etc. BASNs therefore require energy-fidelity scalability, so automated and user-initiated trade-offs can be made dynamically. This article presents a data rate scalability framework within a motion-based health application context which demonstrates the design of efficient and efficacious wireless health systems.