research-article

A Framework for the Inference of Sensing Measurements Based on Correlation

Authors:

Simone Silvestri,

Rahul Urgaonkar,

Bong Jun KoAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 15, Issue 1

Article No.: 4, Pages 1 - 28

https://doi.org/10.1145/3272035

Published: 15 December 2018 Publication History

Abstract

Sensor networks are commonly adopted to collect a variety of measurements in indoor and outdoor settings. However, collecting such measurements from every node in the network, although providing high accuracy and resolution of the phenomena of interest, may easily cause sensors’ battery depletion. In this article, we show that measurement correlation can be successfully exploited to reduce the amount of data collected in the network without significantly sacrificing the monitoring accuracy. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and we formulate an optimization problem to select the monitors under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. We also develop statistical approaches that automatically switch between learning and estimation phases to take into account the variability occurring in real networks. Simulations carried out on real-world traces show that our approach outperforms previous solutions based on compressed sensing, and it can be successfully applied to the real application of solar irradiance prediction of photovoltaics systems.

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Cited By

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https://dl.acm.org/doi/10.1145/3649892
Tao XSilvestri S(2023)Integrating UAV and LoRaWAN in WSN for Intelligent Monitoring in Large-scale Rural Farms2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150234(166-167)Online publication date: 13-Mar-2023
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Index Terms

A Framework for the Inference of Sensing Measurements Based on Correlation
1. Networks

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 15, Issue 1

February 2019

382 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3300201

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 15 December 2018

Accepted: 01 August 2018

Revised: 01 August 2018

Received: 01 August 2017

Published in TOSN Volume 15, Issue 1

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NSF EPSCoR
U.S. Army Research Laboratory and the U.K. Ministry of Defence
NATO - North Atlantic Treaty Organization SPS

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Cited By

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https://dl.acm.org/doi/10.1145/3649892
Tao XSilvestri S(2023)Integrating UAV and LoRaWAN in WSN for Intelligent Monitoring in Large-scale Rural Farms2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops56833.2023.10150234(166-167)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PerComWorkshops56833.2023.10150234
Laidi RDjenouri DBalasingham I(2022)On Predicting Sensor Readings With Sequence Modeling and Reinforcement Learning for Energy-Efficient IoT ApplicationsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.311614152:8(5140-5151)Online publication date: Aug-2022
https://doi.org/10.1109/TSMC.2021.3116141
Meech JStanley-Marbell P(2022)An Algorithm for Sensor Data Uncertainty QuantificationIEEE Sensors Letters10.1109/LSENS.2021.31337616:1(1-4)Online publication date: Jan-2022
https://doi.org/10.1109/LSENS.2021.3133761
Singla PSarangi S(2022)CmpctArch: A Generic Low Power Architecture for Compact Data Structures in Energy Harvesting Devices2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI54635.2022.00046(194-199)Online publication date: Jul-2022
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Gupta VDe S(2020)Collaborative Multi-Sensing in Energy Harvesting Wireless Sensor NetworksIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2020.29955026(426-441)Online publication date: 2020
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Gong LLin HLi ZQian FLi YMa XLiu Y(2020)Systematically Landing Machine Learning onto Market-Scale Mobile Malware DetectionIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2020.3046092(1-1)Online publication date: 2020
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Ma DHan GXiao AHu YAhmed SAujla GCao H(2019)FAT-WSN: A Non Destructive and Secure Aggregation Strategy for Energy Saving in WSN2019 IEEE Globecom Workshops (GC Wkshps)10.1109/GCWkshps45667.2019.9024492(1-6)Online publication date: Dec-2019
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