research-article

Autoregressive Integrated Model for Time Synchronization in Wireless Sensor Networks

Authors:

Jorge F. SchmidtAuthors Info & Claims

MSWiM '15: Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

Pages 133 - 140

https://doi.org/10.1145/2811587.2811591

Published: 02 November 2015 Publication History

Abstract

Time synchronization is challenging in wireless sensor networks due to the use of low-precision oscillators and the limited computational capacity of resources limited sensor nodes. While several schemes exist, the performance analysis of a majority of them is based on simulations and fail to capture key features of real world deployments. This paper explores the use of autoregressive integrated moving average models to provide a general clock model for sensor nodes with low precision oscillators and limited computational power. Based on measurements with off-the-shelf sensor devices Z1, an autoregressive integrated model for time synchronization is proposed. We derive a synchronization scheme (ARI-Sync) based on this model and compare it against the well known Flooding Time Synchronization Protocol (FTSP) observing significantly improved accuracy, roughly doubling the resynchronization period of Z1 nodes for a typical wireless sensor network application.

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Cited By

Khan Mamun MAlouani A(2022)Automatic Detection of Heart Diseases Using Biomedical Signals: A Literature Review of Current Status and LimitationsAdvances in Information and Communication10.1007/978-3-030-98015-3_29(420-440)Online publication date: 12-Mar-2022
https://doi.org/10.1007/978-3-030-98015-3_29
Tan ZYang XPang MGao SLi MChen P(2020)UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology CommunicationSensors10.3390/s2018513420:18(5134)Online publication date: 9-Sep-2020
https://doi.org/10.3390/s20185134
Tavares Bruscato LHeimfarth TPignaton de Freitas E(2017)Enhancing Time Synchronization Support in Wireless Sensor NetworksSensors10.3390/s1712295617:12(2956)Online publication date: 20-Dec-2017
https://doi.org/10.3390/s17122956

Index Terms

Autoregressive Integrated Model for Time Synchronization in Wireless Sensor Networks
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis
2. Networks
  1. Network protocols

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cover image ACM Conferences

MSWiM '15: Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2015

358 pages

ISBN:9781450337625

DOI:10.1145/2811587

General Chairs:
J. J. Garcia-Luna-Aceves
University of California, USA
,
Graciela Romén Alonso
Universidad Autónoma Metropolitana, Mexico
,
Program Chairs:
Falko Dressler
University of Paderborn, Germany
,
Brahim Bensaou
Hong Kong University of Science and Technology, Hong Kong
,
Antonio F. Loureiro
Federal University of Minas Gerais, Brazil

Copyright © 2015 ACM.

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Published: 02 November 2015

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MSWiM'15: 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 2 - 6, 2015

Cancun, Mexico

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MSWiM '15 Paper Acceptance Rate 34 of 142 submissions, 24%;

Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

Khan Mamun MAlouani A(2022)Automatic Detection of Heart Diseases Using Biomedical Signals: A Literature Review of Current Status and LimitationsAdvances in Information and Communication10.1007/978-3-030-98015-3_29(420-440)Online publication date: 12-Mar-2022
https://doi.org/10.1007/978-3-030-98015-3_29
Tan ZYang XPang MGao SLi MChen P(2020)UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology CommunicationSensors10.3390/s2018513420:18(5134)Online publication date: 9-Sep-2020
https://doi.org/10.3390/s20185134
Tavares Bruscato LHeimfarth TPignaton de Freitas E(2017)Enhancing Time Synchronization Support in Wireless Sensor NetworksSensors10.3390/s1712295617:12(2956)Online publication date: 20-Dec-2017
https://doi.org/10.3390/s17122956

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