Automated context aware composition of Advanced Telecom Services for environmental early warnings

This paper presents one of the main components of a framework for automated composition of Advanced Telecom Services for environmental early Warnings. The framework, called AUTO, is composed by three main modules: a request processing module that transforms natural language and context information into a planning instance; the automated planning module, based on PELEA, an architecture for planning and execution; and the Service Execution Environment Advance Telecom Services. This paper focuses on the description of the translation of the user request in natural language and his context into planning instances. These planning instances represent service composition tasks based on Automated Planning. The advantages of this approach, like the automatic inclusion of context and user preferences in the composition of services, will be presented. Also, the current implementation will be described and some experimentation will prove the viability of AUTO

Rule-based monitoring and error detecting for converged telecommunication processes

Convergent process may be defined as a composition of services which integrates functionalities from Web and Telecommunication domains. However, during execution of convergent processes some services may fail; in such cases a reconfiguration process must be triggered to recover normal behaviour of composite process. Previous works have developed mechanisms for reducing reconfiguration time while initial restrictions are maintained; this is achieved by replacing regions of services instead of individual services. The present work presents an approach for monitoring and error detecting in convergent processes using rule production systems based on ITIL model. The approach was tested in the monitoring module of the AUTO framework, whose architecture and performance are discussed to show that this approach can efficiently detect errors and repair convergent processes in telecom environments

Comparing Drools and Ontology Reasoning Approaches for Automated Monitoring in Telecommunication Processes

AbstractAutomated reconfiguration is one of the crucial tasks in telecommunication service composition. The first step in reconfiguration is the monitoring phase. The problem of monitoring and error detection frequently appears in different telecommunications architectures. This article describes the main components of the architecture for monitoring module in AUTO framework. The monitoring approach is based on semantic technologies and ITIL framework. Equally, this paper presents an analysis and comparison of two approaches for the implementation of the module: Drools and semantic formalism. The results of this study may be applicable to other telecommunication domains

HADES: a Hybrid Anomaly Detection System for Large-Scale Cyber-Physical Systems

Smart cities rely on large-scale heterogeneous distributed systems known as Cyber-Physical Systems (CPS). Information systems based on CPS typically analyse a massive amount of data collected from various data sources that operate under noisy and dynamic conditions. How to determine the quality and reliability of such data is an open research problem that concerns the overall system safety, reliability and security. Our research goal is to tackle the challenge of real-time data quality assessment for large-scale CPS applications with a hybrid anomaly detection system. In this paper we describe the architecture of HADES, our Hybrid Anomaly DEtection System for sensors data monitoring, storage, processing, analysis, and management. Such data will be filtered with correlation-based outlier detection techniques, and then processed by predictive analytics for anomaly detection

An Open Source Software Architecture for Smart Buildings

Open-source software has helped opening the software market to different players, usually cut off by licenses of expensive software packages. We claim that in the Built Environment a similar open source disruption can happen by putting together different projects in a software architecture based on open data standards. This paper describes the main open-source components of such software architecture, the Smart Building Controller (SBC) that we are developing, and possible future applications

A Semantic loT Early Warning System for Natural Environment Crisis Management

An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model.We use lightweight semantics for metadata to enhance rich sensor data acquisition.We use heavyweight semantics for top level W3CWeb Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a reployed EWS infrastructure

A Semantic IoT Early Warning System for Natural Environment Crisis Management

This work was supported in part by the European FP7 Funded Project TRIDEC under Grant 258723, the other project partners in helping to deliver the complete project Syste, in particular, GFZ, and the German Research Centre for Geosciences, Potsdam, Germany. The work of R. Tao was supported by the Queen Mary University of London for a Ph.D. studentship

Automated context aware composition of Advanced Telecom Services for environmental early warnings

This paper presents one of the main components of a framework for automated composition of Advanced Telecom Services for environmental early Warnings. The framework, called AUTO, is composed by three main modules: a request processing module that transforms natural language and context information into a planning instance; the automated planning module, based on PELEA, an architecture for planning and execution; and the Service Execution Environment Advance Telecom Services. This paper focuses on the description of the translation of the user request in natural language and his context into planning instances. These planning instances represent service composition tasks based on Automated Planning. The advantages of this approach, like the automatic inclusion of context and user preferences in the composition of services, will be presented. Also, the current implementation will be described and some experimentation will prove the viability of AUTO