A framework for modelling mobile radio access networks for intelligent fault management

Postprin

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models

Review of Data Mining Techniques for Churn Prediction in Telecom

Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models