An evaluation of multi-resolution storage for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Application-specific compression for time delay estimation in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Adaptive sampling for sensor networks
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
Adaptive lossless compression in wireless body sensor networks
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Stream-oriented lossless packet compression in wireless sensor networks
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Mobile Networks and Applications
Behavioral reconfigurable compression in body sensor networks
Proceedings of the Fifth International Conference on Body Area Networks
IEEE Journal on Selected Areas in Communications
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Body area sensor networks have been attracting more and more applications which focus on human behaviour and monitoring, ranging from simple positioning to medical applications. These BSNs inherit unique specifications since are composed of light-weight embedded systems. In this paper, we focus on energy and lifetime requirements of these systems which is one of the most challenging design constraints. We study this problem from the angle of data compression and sampling which are both known to be very efficient in energy reduction specially when large amount of data is to be transmitted wirelessly. We introduce the notion of functional compression which utilises classes of data patterns to efficiently represent information through regenerative functions. Furthermore, we propose a reconfigurable compression methods which dynamically uses different compression methods to optimise compression ratio and energy savings. Later we study how sampling rate can be adaptively altered based on the behaviour of data pattern for further reduction in sample counts. We use the data from a wearable sensing system to illustrate the effectiveness of these methods which utilise the context and behaviour of the environment in system optimisation process.