The Strength of Weak Learnability
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A coverage-preserving node scheduling scheme for large wireless sensor networks
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Pattern Classification (2nd Edition)
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Combining Pattern Classifiers: Methods and Algorithms
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Co-Grid: an efficient coverage maintenance protocol for distributed sensor networks
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The coverage problem in a wireless sensor network
Mobile Networks and Applications
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Action coverage formulation for power optimization in body sensor networks
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IEEE Journal on Selected Areas in Communications
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Journal of Ambient Intelligence and Smart Environments
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
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IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
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Mobile Networks and Applications - Special issue on Wireless and Personal Communications
Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf
Journal of Ambient Intelligence and Smart Environments
ACM Transactions on Embedded Computing Systems (TECS)
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Advances in technology have led to the development of various light-weight sensor devices that can be woven into the physical environment of our daily lives. Such systems enable on-body and mobile health-care monitoring. Our interest particularly lies in the area of movement-monitoring platforms that operate with inertial sensors. In this paper, we introduce the notion of compatibility graphs and describe how they can be utilized for power optimization. We first formulate an action coverage problem that will consider the sensing coverage from a collaborative signal processing perspective. Our solution is capable of eliminating redundant sensor nodes while maintaining the quality of service. The problem we outline can be transformed into an NP-hard problem. Therefore, we propose an ILP formulation to attain a lower bound on the solution and a fast greedy technique. Moreover, we present a system for dynamically activating and deactivating sensor nodes in real time. We then use our graph representation to develop an efficient formulation for maximum lifetime. This formulation provides sufficient information for finding activation duties for each sensor node. Finally, we demonstrate the effectiveness of our techniques on data collected from several subjects.