Automatic diagnosis of mobile communication networks under imprecise parameters

Self-management is one of the challenges for realizing Ambient Intelligence in pervasive computing. In this paper, we propose and present a semantic web based selfmanagement approach for a pervasive service middleware where dynamic context information is encoded in a set of self-management context ontologies. The proposed approach is justified from the characteristics of pervasive computing and the open world assumption and reasoning potentials of semantic web and its rule language. To enable real-time self-management, application level and network level state reporting is employed in our approach. State changes are triggering execution of self-management rules for adaption, monitoring, diagnosis, and so on. Evaluations of self-diagnosis in terms of extensibility, performance, and scalability show that the semantic web based self-management approach is effective to achieve the selfdiagnosis goals, and lay a solid foundation for further selfmanagement work.

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“…[3]. These work is not utilizing the context ontologies that are already existing in pervasive systems.…”

Section: Related Workmentioning

confidence: 97%

Semantic Web Based Self-Management for a Pervasive Service Middleware

Zhang

Hansen

2008

2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems

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“…Industrial maintenance has evolved from condition based maintenance to predictive maintenance thanks to new technologies as BNs, being able to support the decision process on fault diagnosis and troubleshooting based on the faults prediction for non-critical machinery such as elevators and machine tools (Gilabert, 2006). But there are lots of references in the literature about the application of BNs in other fields of application: sensor validation (Ibargüengoytia, 2006), reliability analysis of Systems (Doguc, 2009), detection of broken rail for railway infrastructure (Oukhellou, 2008), diagnosis of radio access networks of cellular Systems (Barco, 2009), etc.…”

Section: Bayesian Network Overviewmentioning

confidence: 99%

IMPROVING AIRCRAFT MAINTENANCE WITH INNOVATIVE PROGNOSTICS AND HEALTH MANAGEMENT TECHNIQUES - Case of Study: Brake Wear Degradation

Ferreiro¹,

Arnáiz²

2010

Proceedings of the 2nd International Conference on Agents and Artificial Intelligence

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Maintenance is going through to major changes in a lot of activity fields where the current maintenance strategy must adjust to the new requirements. The aeronautics industry belongs to one these activity fields which are trying to carry out important changes around its maintenance strategy. It needs to minimize the cost for the maintenance support and to increase its operational reliability and availability (avoiding delays, cancellations, etc) which would lead to a further decrease in costs. However, to support this change, it requires transforming the traditional corrective maintenance practice of "fail and fix" to "prevent and predict". The aim of this article is to show the usefulness and the benefits of innovative techniques such as Bayesian Networks to support an intelligent function "decision support", the basis for the new type of maintenance strategy based on prediction and prognosis. It helps to achieve a maximum optimization of resources and operational availability while minimizing economic costs, and replaces the current maintenance carried out in the aircraft industry up to now.

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“…The nearest stored problem will indicate the root cause. Bayesian Networks (BN) have also been used to perform the diagnosis [5]. A Knowledge Acquisition Tool that helps troubleshooting experts to set the parameters of the BN in [5] is also presented in [6].…”

Section: Introductionmentioning

confidence: 99%

LTE performance data reduction for knowledge acquisition

Khatib

Barco

Serrano

et al. 2014

2014 IEEE Globecom Workshops (GC Wkshps)

Self Cite

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In the last years, mobile networks have seen a great increase in complexity, as the data traffic, the demand for quality and the variety of offered services have grown. The management costs of modern networks are growing, at the same time as operators compete to offer shorter downtime and less impact of network issues on the user experience. Self-Organizing Networks (SON) offer a solution to these problems. Among these SON functionalities in cellular networks, self-healing automates the resolution of problems in the radio access network. To perform the task of diagnosis (or root cause analysis), Knowledge-Based Systems (KBS) are often used. These systems need a previous process of training (or learning), in which they are fed instances of real problems. In this paper, an algorithm for extracting the key information for these vectors is proposed. The inputs of the algorithm are big matrices of time-dependent network performance data, and the outputs are simple one-dimensional vectors ready to be used in learning algorithms.

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Automatic diagnosis of mobile communication networks under imprecise parameters

Cited by 26 publications

References 25 publications

Semantic Web Based Self-Management for a Pervasive Service Middleware

Semantic Web Based Self-Management for a Pervasive Service Middleware

IMPROVING AIRCRAFT MAINTENANCE WITH INNOVATIVE PROGNOSTICS AND HEALTH MANAGEMENT TECHNIQUES - Case of Study: Brake Wear Degradation

LTE performance data reduction for knowledge acquisition

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