A self-healing framework for online sensor data

Tuan Anh Nguyen, Marco Aiello, Takuro Yonezawa, Kenji Tei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Autonomic Computing, ICAC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages295-300
Number of pages6
ISBN (Electronic)9781467369701
DOIs
Publication statusPublished - 2015 Sep 14
Externally publishedYes
Event12th IEEE International Conference on Autonomic Computing, ICAC 2015 - Grenoble, France
Duration: 2015 Jul 72015 Jul 10

Other

Other12th IEEE International Conference on Autonomic Computing, ICAC 2015
CountryFrance
CityGrenoble
Period15/7/715/7/10

Fingerprint

Sensors
Engines
Ubiquitous computing
Fault detection
Sensor networks
Wireless sensor networks
Topology
Availability
Smart city

Keywords

  • Fault tolerance for WSNs
  • Online sensor data
  • Seal-healing framework
  • Smart environments
  • Wireless sensor network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software
  • Control and Systems Engineering

Cite this

Nguyen, T. A., Aiello, M., Yonezawa, T., & Tei, K. (2015). A self-healing framework for online sensor data. In Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015 (pp. 295-300). [7266983] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC.2015.61

A self-healing framework for online sensor data. / Nguyen, Tuan Anh; Aiello, Marco; Yonezawa, Takuro; Tei, Kenji.

Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 295-300 7266983.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nguyen, TA, Aiello, M, Yonezawa, T & Tei, K 2015, A self-healing framework for online sensor data. in Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015., 7266983, Institute of Electrical and Electronics Engineers Inc., pp. 295-300, 12th IEEE International Conference on Autonomic Computing, ICAC 2015, Grenoble, France, 15/7/7. https://doi.org/10.1109/ICAC.2015.61
Nguyen TA, Aiello M, Yonezawa T, Tei K. A self-healing framework for online sensor data. In Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 295-300. 7266983 https://doi.org/10.1109/ICAC.2015.61
Nguyen, Tuan Anh ; Aiello, Marco ; Yonezawa, Takuro ; Tei, Kenji. / A self-healing framework for online sensor data. Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 295-300
@inproceedings{a6b9b8efd08e442ba0a4bafaf78cba9c,
title = "A self-healing framework for online sensor data",
abstract = "In pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.",
keywords = "Fault tolerance for WSNs, Online sensor data, Seal-healing framework, Smart environments, Wireless sensor network",
author = "Nguyen, {Tuan Anh} and Marco Aiello and Takuro Yonezawa and Kenji Tei",
year = "2015",
month = "9",
day = "14",
doi = "10.1109/ICAC.2015.61",
language = "English",
pages = "295--300",
booktitle = "Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A self-healing framework for online sensor data

AU - Nguyen, Tuan Anh

AU - Aiello, Marco

AU - Yonezawa, Takuro

AU - Tei, Kenji

PY - 2015/9/14

Y1 - 2015/9/14

N2 - In pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.

AB - In pervasive computing environments, wireless sensor networks (WSNs) play an important role, collecting reliable and accurate context information so that applications are able to provide services to users on demand. In such environments, sensors should be self-adaptive by taking correct decisions based on sensed data in real-time. However, sensor data is often faulty. Faults are not so exceptional and in most deployments tend to occur frequently. Therefore, the capability of self-healing is important to ensure higher levels of reliability and availability. We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. Within our framework, a variety of fault detection and classification algorithms can be applied, depending on the characteristics of the sensor data types as well as the topology of the sensor networks. A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. To validate the applicability, we illustrate a case study where our solution is implemented as an adaptation engine and integrated seamlessly into the ClouT system. The engine processes data coming from physical sensors deployed in Santander, Spain, providing corrected sensor data to other smart city applications developed in the ClouT project.

KW - Fault tolerance for WSNs

KW - Online sensor data

KW - Seal-healing framework

KW - Smart environments

KW - Wireless sensor network

UR - http://www.scopus.com/inward/record.url?scp=84961843657&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961843657&partnerID=8YFLogxK

U2 - 10.1109/ICAC.2015.61

DO - 10.1109/ICAC.2015.61

M3 - Conference contribution

AN - SCOPUS:84961843657

SP - 295

EP - 300

BT - Proceedings - IEEE International Conference on Autonomic Computing, ICAC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -