DocumentCode
3577291
Title
A Soft Fault Detection Mechanism with High Accuracy on Machine-to-Machine Communication Networks
Author
Yuan-Kang Shih ; Hung-Yu Wei
Author_Institution
Intel-NTU Connected Context Comput. Center, Nat. Taiwan Univ., Taipei, Taiwan
fYear
2014
Firstpage
340
Lastpage
343
Abstract
The machine-to-machine (M2M) communication network is a promising concept to improve the user experience. The M2M communication enables two M2M devices in proximity of each other to establish a direct local link and bypass the base station. Machine-to-machine communication can be applied to various important fields, such as military applications, public safety, environmental monitoring, commercial and social networking, and health and medical monitoring, ect. Hence the networks must exclude the faulty devices or faulty sensors to ensure the network Quality of Service (QoS). Fault detection is highly preferred on machine-to-machine communication networks. Device fault can be divided into two types: hard fault and soft fault. We will give a mechanism with capability to detect and distinguish soft fault devices. The simulation results indicate when a large number of machine-to-machine devices under a machine-to-machine communication network, the false alarm rate of the soft fault detection mechanism is close to 0% and the detection accuracy of it is close to 100%.
Keywords
cellular radio; fault diagnosis; quality of service; radio links; M2M communication network; QoS; base station; direct local link; hard fault detection; machine-to-machine communication cellular network; quality of service; soft fault detection mechanism; Accuracy; Communication networks; Conferences; Fault detection; Sensors; Wireless communication; Wireless sensor networks; M2M; Machine-to-Machine; QoS; fault detection;
fLanguage
English
Publisher
ieee
Conference_Titel
Internet of Things (iThings), 2014 IEEE International Conference on, and Green Computing and Communications (GreenCom), IEEE and Cyber, Physical and Social Computing(CPSCom), IEEE
Print_ISBN
978-1-4799-5967-9
Type
conf
DOI
10.1109/iThings.2014.63
Filename
7059688
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