JOURNAL ARTICLE

Device clustering for fault monitoring in Internet of Things systems

Abstract

This paper investigates the issue of device clustering for fault monitoring in IoT systems. In order to detect device faults quickly, fault monitoring must be conducted regularly and often. Therefore, it is desirable to reduce the communication cost for fault monitoring. We define the problem by extending the multiple traveling salesman problem (mTSP) in an integer programming (IP) formulation. We present heuristic algorithms for constructing both monitoring clusters and also the monitoring route within each cluster. We conduct simulation using different combinations of algorithms. Simulation results show our heuristic algorithms can achieve near optimal solutions on reducing the communication cost, with a low complexity.

Keywords:
Cluster analysis Computer science Heuristic Travelling salesman problem Integer programming Fault (geology) Distributed computing Internet of Things Fault detection and isolation The Internet Real-time computing Algorithm Embedded system Machine learning Artificial intelligence

Metrics

16
Cited By
2.00
FWCI (Field Weighted Citation Impact)
17
Refs
0.89
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Energy Efficient Wireless Sensor Networks
Physical Sciences →  Computer Science →  Computer Networks and Communications
IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications
Software-Defined Networks and 5G
Physical Sciences →  Computer Science →  Computer Networks and Communications

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