INTEROPERABILITY OF TELECOMMUNICATIONS EQUIPMENT FOR CENTRAL MONITORING AND DIAGNOSTICS

Abstract

Telecommunications networks are ever growing and rapidly expanding. Their management becomes complicated with same kind of equipment purchased from differ- ent manufacturers and incorporation of newer technologies to accommodate customer demands. In such a scenario, modeling of an ever changing telecommunications network becomes complicated. Automatic methods are necessary and modeling of event/alarm intensity becomes crucial for monitoring of a telecommunication network in these settings. The framework of Salmenkivi [1, 2] has been extended to incorporate classical Poisson likelihood and Bayesian integrated likelihoods proposed by Scargle [3]. Scargle has proposed three Bayesian integrated likelihoods to segment γ−ray bursts coming from the space. He has used these Bayesian integrated likelihoods with hierarchical algorithm to segment the data to model intensity of Gamma ray bursts. Two of those three likelihoods mentioned as Scargle1 and Scargle2 likelihoods are used under both hierarchical and dynamic programming algorithms to model intensity of event/alarm data collected from a typical telecommunications network. Unlike Salmenkivi, this study directly considers the discrete event/alarm data. Event/alarm data collected from telecommunications networks and a large amount of synthetic datasets are processed with hierarchical and dynamic programming algorithms by employing classical Poisson and Bayesian integrated likelihoods. The same data has also been processed with hierarchical Bayesian models proposed by Green [4] and Dobigeon et al., [5, 6]. The results of hierarchical and dynamic programming algorithms are compared with those obtained from hierarchical Bayesian models. Finally, the British coal mining disasters dataset is processed with hierarchical and dynamic programming algorithms in various time resolutions. This is done to focus on event/alarm thresholds below 1. New results have emerged and a different behavior of classical Poisson and Bayesian integrated likelihoods has been found and reported. A novel hierarchical Bayesian model has been proposed and simulated with Gibbs sampler that models time differences between events/alarms.