Cargando…

A Fault Tolerance Mechanism for On-Road Sensor Networks

On-Road Sensor Networks (ORSNs) play an important role in capturing traffic flow data for predicting short-term traffic patterns, driving assistance and self-driving vehicles. However, this kind of network is prone to large-scale communication failure if a few sensors physically fail. In this paper,...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Lei, Guo, Shaoyong, Sun, Jialu, Yu, Peng, Li, Wenjing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191040/
https://www.ncbi.nlm.nih.gov/pubmed/27918483
http://dx.doi.org/10.3390/s16122059
_version_ 1782487542925361152
author Feng, Lei
Guo, Shaoyong
Sun, Jialu
Yu, Peng
Li, Wenjing
author_facet Feng, Lei
Guo, Shaoyong
Sun, Jialu
Yu, Peng
Li, Wenjing
author_sort Feng, Lei
collection PubMed
description On-Road Sensor Networks (ORSNs) play an important role in capturing traffic flow data for predicting short-term traffic patterns, driving assistance and self-driving vehicles. However, this kind of network is prone to large-scale communication failure if a few sensors physically fail. In this paper, to ensure that the network works normally, an effective fault-tolerance mechanism for ORSNs which mainly consists of backup on-road sensor deployment, redundant cluster head deployment and an adaptive failure detection and recovery method is proposed. Firstly, based on the N − x principle and the sensors’ failure rate, this paper formulates the backup sensor deployment problem in the form of a two-objective optimization, which explains the trade-off between the cost and fault resumption. In consideration of improving the network resilience further, this paper introduces a redundant cluster head deployment model according to the coverage constraint. Then a common solving method combining integer-continuing and sequential quadratic programming is explored to determine the optimal location of these two deployment problems. Moreover, an Adaptive Detection and Resume (ADR) protocol is deigned to recover the system communication through route and cluster adjustment if there is a backup on-road sensor mismatch. The final experiments show that our proposed mechanism can achieve an average 90% recovery rate and reduce the average number of failed sensors at most by 35.7%.
format Online
Article
Text
id pubmed-5191040
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-51910402017-01-03 A Fault Tolerance Mechanism for On-Road Sensor Networks Feng, Lei Guo, Shaoyong Sun, Jialu Yu, Peng Li, Wenjing Sensors (Basel) Article On-Road Sensor Networks (ORSNs) play an important role in capturing traffic flow data for predicting short-term traffic patterns, driving assistance and self-driving vehicles. However, this kind of network is prone to large-scale communication failure if a few sensors physically fail. In this paper, to ensure that the network works normally, an effective fault-tolerance mechanism for ORSNs which mainly consists of backup on-road sensor deployment, redundant cluster head deployment and an adaptive failure detection and recovery method is proposed. Firstly, based on the N − x principle and the sensors’ failure rate, this paper formulates the backup sensor deployment problem in the form of a two-objective optimization, which explains the trade-off between the cost and fault resumption. In consideration of improving the network resilience further, this paper introduces a redundant cluster head deployment model according to the coverage constraint. Then a common solving method combining integer-continuing and sequential quadratic programming is explored to determine the optimal location of these two deployment problems. Moreover, an Adaptive Detection and Resume (ADR) protocol is deigned to recover the system communication through route and cluster adjustment if there is a backup on-road sensor mismatch. The final experiments show that our proposed mechanism can achieve an average 90% recovery rate and reduce the average number of failed sensors at most by 35.7%. MDPI 2016-12-03 /pmc/articles/PMC5191040/ /pubmed/27918483 http://dx.doi.org/10.3390/s16122059 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Feng, Lei
Guo, Shaoyong
Sun, Jialu
Yu, Peng
Li, Wenjing
A Fault Tolerance Mechanism for On-Road Sensor Networks
title A Fault Tolerance Mechanism for On-Road Sensor Networks
title_full A Fault Tolerance Mechanism for On-Road Sensor Networks
title_fullStr A Fault Tolerance Mechanism for On-Road Sensor Networks
title_full_unstemmed A Fault Tolerance Mechanism for On-Road Sensor Networks
title_short A Fault Tolerance Mechanism for On-Road Sensor Networks
title_sort fault tolerance mechanism for on-road sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191040/
https://www.ncbi.nlm.nih.gov/pubmed/27918483
http://dx.doi.org/10.3390/s16122059
work_keys_str_mv AT fenglei afaulttolerancemechanismforonroadsensornetworks
AT guoshaoyong afaulttolerancemechanismforonroadsensornetworks
AT sunjialu afaulttolerancemechanismforonroadsensornetworks
AT yupeng afaulttolerancemechanismforonroadsensornetworks
AT liwenjing afaulttolerancemechanismforonroadsensornetworks
AT fenglei faulttolerancemechanismforonroadsensornetworks
AT guoshaoyong faulttolerancemechanismforonroadsensornetworks
AT sunjialu faulttolerancemechanismforonroadsensornetworks
AT yupeng faulttolerancemechanismforonroadsensornetworks
AT liwenjing faulttolerancemechanismforonroadsensornetworks