Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks

Mahmudimanesh, M. and Khelil, A. and Suri, Neeraj (2012) Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks. In: 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012). IEEE, pp. 389-397. ISBN 9781467324335

Full text not available from this repository.


Compressive Sampling (CS) is a powerful sampling technique that allows accurately reconstructing a compressible signal from a few random linear measurements. CS theory has applications in sensory systems where acquiring individual samples is either expensive or infeasible. A Wireless Sensor Network (WSN) is a distributed sensory system comprised of resource-limited sensor nodes. Transferring all the recorded samples in a WSN can easily result in data traffic that can exceed the network capacity. There are ongoing attempts to devise efficient and accurate compression schemes for WSNs and CS has proved to be a key sampling method compared to many other existing techniques. In this paper, specifically targeting the dominant WSN deployments of multi-hop WSNs, we develop a novel CS-based concept of sampling window as an efficient spatio-temporal signal acquisition/compression technique. We show that much higher energy-efficient signal acquisition is possible, if composite temporal and spatial correlations are considered. Our model is also capable of abnormal event detection which is a crucial feature in WSNs. It guarantees balanced energy consumption by the sensor nodes in a multi-hop topology to prevent overloaded nodes and network partitioning. © 2012 IEEE.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
Deposited By:
Deposited On:
14 Oct 2019 13:46
Last Modified:
21 Nov 2022 17:07