research-article

Distributed Data-Centric Adaptive Sampling for Cyber-Physical Systems

Authors:

Hariharasudhan Viswanathan,

Dario PompiliAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 9, Issue 4

Article No.: 21, Pages 1 - 27

https://doi.org/10.1145/2644820

Published: 14 January 2015 Publication History

Abstract

A data-centric joint adaptive sampling and sleep scheduling solution, SILENCE, for autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena is proposed. Adaptive sampling and sleep scheduling can help realize the much needed resource efficiency by minimizing the communication and processing overhead in densely deployed autonomic sensor-based systems. The proposed solution exploits the spatiotemporal correlation in sensed data and eliminates redundancy in transmitted data through selective representation without compromising on accuracy of reconstruction of the monitored phenomenon at a remote monitor node. Differently from existing adaptive sampling solutions, SILENCE employs temporal causality analysis to not only track the variation in the underlying phenomenon but also its cause and direction of propagation in the field. The causality analysis and the same correlations are then leveraged for adaptive sleep scheduling aimed at saving energy in wireless sensor networks (WSNs). SILENCE outperforms traditional adaptive sampling solutions as well as the recently proposed compressive sampling techniques. Real experiments were performed on a WSN testbed monitoring temperature and humidity distribution in a rack of servers, and the simulations were performed on TOSSIM, the TinyOS simulator.

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Distributed Data-Centric Adaptive Sampling for Cyber-Physical Systems

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 9, Issue 4

January 2015

137 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2695594

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2015 ACM.

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Publication History

Published: 14 January 2015

Accepted: 01 July 2014

Revised: 01 June 2014

Received: 01 March 2013

Published in TAAS Volume 9, Issue 4

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Cited By

Sadhu VLi ZQi ZPompili D(2023)High-Resolution Data Acquisition and Joint Source-Channel Coding in Underwater IoTIEEE Internet of Things Journal10.1109/JIOT.2023.323958010:16(14003-14013)Online publication date: 15-Aug-2023
https://doi.org/10.1109/JIOT.2023.3239580
Rahmati MSadhu VPompili D(2019)ECO-UW IoT: Eco-friendly Reliable and Persistent Data Transmission in Underwater Internet of Things2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)10.1109/SAHCN.2019.8824906(1-9)Online publication date: Jun-2019
https://doi.org/10.1109/SAHCN.2019.8824906
Carrero MMusicante Mdos Santos AHara C(2019)SLEDS: A DSL for Data-Centric Storage on Wireless Sensor NetworksBig Social Data and Urban Computing10.1007/978-3-030-11238-7_5(74-89)Online publication date: 23-Jan-2019
https://doi.org/10.1007/978-3-030-11238-7_5
Das SMisra SWolfinger BObaidat M(2016)Temporal-Correlation-Aware Dynamic Self-Management of Wireless Sensor NetworksIEEE Transactions on Industrial Informatics10.1109/TII.2016.259475812:6(2127-2138)Online publication date: Dec-2016
https://doi.org/10.1109/TII.2016.2594758

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