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Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization

Published: 03 November 2013 Publication History

Abstract

A key factor in a successful sensor network deployment is finding a good balance between maximizing the number of measurements taken (to maintain a good sampling rate) and minimizing the overall energy consumption (to extend the network lifetime). In this work, we present a data-driven statistical model to optimize this tradeoff. Our approach takes advantage of the multivariate nature of the data collected by a heterogeneous sensor network to learn spatio-temporal patterns. These patterns enable us to employ an aggressive duty cycling policy on the individual sensor nodes, thereby reducing the overall energy consumption. Our experiments with the OMNeT++ network simulator using realistic wireless channel conditions, on data collected from two real-world sensor networks, show that we can sample just 20% of the data and can reconstruct the remaining 80% of the data with less than 9% mean error, outperforming similar techniques such is distributed compressive sampling. In addition, energy savings ranging up to 76%, depending on the sampling rate and the hardware configuration of the node.

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  • (2024)Structured Low-Rank Tensor Completion for IoT Spatiotemporal High-Resolution Sensing Data ReconstructionIEEE Internet of Things Journal10.1109/JIOT.2023.331818611:5(8299-8310)Online publication date: 1-Mar-2024
  • (2020)Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC SystemsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2019.294810117:2(833-846)Online publication date: Apr-2020
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    cover image ACM Conferences
    MSWiM '13: Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
    November 2013
    468 pages
    ISBN:9781450323536
    DOI:10.1145/2507924
    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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    Published: 03 November 2013

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    Author Tags

    1. data recovery
    2. energy management
    3. latent variables
    4. statistical modeling
    5. tensor factorization
    6. wireless sensor networks

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    MSWiM '13 Paper Acceptance Rate 42 of 184 submissions, 23%;
    Overall Acceptance Rate 398 of 1,577 submissions, 25%

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    View all
    • (2024)Tensor Completion Using High-Order Spatial Delay Embedding for IoT Multi-Attribute Data ReconstructionIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2024.345879110(715-728)Online publication date: 2024
    • (2024)Structured Low-Rank Tensor Completion for IoT Spatiotemporal High-Resolution Sensing Data ReconstructionIEEE Internet of Things Journal10.1109/JIOT.2023.331818611:5(8299-8310)Online publication date: 1-Mar-2024
    • (2020)Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC SystemsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2019.294810117:2(833-846)Online publication date: Apr-2020
    • (2020)Environmental Monitoring in Wireless Sensor Networks using Structured Matrix CompletionGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9322445(1-5)Online publication date: Dec-2020
    • (2019)Multi-Attribute Data Recovery in Wireless Sensor Networks With Joint Sparsity and Low-Rank Constraints Based on Tensor CompletionIEEE Access10.1109/ACCESS.2019.29421957(135220-135230)Online publication date: 2019
    • (2018)Energy-aware and quality-driven sensor management for green mobile crowd sensingJournal of Network and Computer Applications10.1016/j.jnca.2015.06.02359:C(95-108)Online publication date: 28-Dec-2018
    • (2016)Data recovery in heterogeneous wireless sensor networks based on low-rank tensors2016 IEEE Symposium on Computers and Communication (ISCC)10.1109/ISCC.2016.7543805(616-620)Online publication date: Jun-2016
    • (2015)Sub-Sampling Framework Comparison for Low-Power Data Gathering: A Comparative AnalysisSensors10.3390/s15030505815:3(5058-5080)Online publication date: 2-Mar-2015

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