Abdelghani Chibani

Optimization of fault diagnosis based on the combination of Bayesian Networks and Case-Based Reasoning

Fault diagnosis is one of the most important tasks in fault management. The main objective of the fault management system is to detect and localize failures as soon as they occur to minimize their effects on the network performance and therefore on the service quality perceived by users. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity.

QoS based framework for ubiquitous robotic services composition

With the growing emergence of ubiquitous computing and networked systems, ubiquitous robotics is becoming an active research domain. The issue of services composition to offer seamless access to a variety of complex services has received widespread attention in recent years. The majority of the proposed approaches have been inspired from the research undertaken jointly on Workflow and AI-based classical planning techniques. However, the traditional AI-based methods assume that the environment is static and the invocation of the services is deterministic. In ubiquitous robotics, services composition is a challenging issue when the execution environment and services are dynamic and the knowledge about their state and context is uncertain. The services composition requires taking into account the parameters of quality of service (QoS) to adapt the composed service to context of the user and the environment, in particular, dealing with failures such as: service invocation failures, network disconnection, sensor failures, context change due to mobility of objects (robots, sensors, etc.), service discovery failures and service execution failures. In this paper, we present a framework which gives ubiquitous robotic system the ability to dynamically compose and deliver ubiquitous services, and to monitor their execution. The main motivation behind the use of services composition is to decrease time and costs to develop integrated complex applications using robots by transforming them from a single task issuer to smart services provider and human companion, without rebuilding each time the robotic system. To address these new challenges, we propose in this paper a new framework for services composition and monitoring, including QoS estimation and Bayesian learning model to deal with the dynamic and uncertain nature of the environment. This framework includes three levels: abstract plan construction, plan execution, and services discovery and re-composition. This approach is tested under USARSim simulator on a prototype of ubiquitous robotic services for assisting an elderly person at home. The obtained results from extensive tests demonstrate clearly the feasibility and efficiency of our approach.

Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology.

Context-Aware Dynamic Service Composition in Ubiquitous Environment

The service composition aims to provide a variety of high level services. Recent approaches cannot fully satisfy the requirement raised by ubiquitous environment. In this paper, we propose a layered design framework which aims at being flexible and robust to failure service composition. It adopts an abstract way of generating plan using rule-based techniques in order to adapt to the changes occurring on the services and the context of use. The approach optimizes the number of services and the recomposition time in large-scale environment by removing the phase of rediscovery. The framework for service composition and monitoring includes learning mechanism for the service selection, based on an estimation of the reputation for abstract services and the quality (QoS) for concrete services. The proposed approach is tested, under USARSim simulator, on a set of ubiquitous services for assisting elderly or dependant person in a residential environment. The obtained results show the feasibility and the scalability of the approach and a better reactivity to the dynamic and uncertain nature of the ubiquitous environment.

Dempster–Shafer theory-based human activity recognition in smart home environments

Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naïve Bayes classifier and J48 decision tree.

Semantic middleware for context services composition in ubiquitous computing

In this paper, we describe a semantic middleware which aims to provide ubiquitous computing applications with high level contextual knowledge. The proposed middleware architecture is designed around service agent entities, offering transparent and reusable services for context semantic discovery, capture and aggregation. Aggregation of contextual knowledge is modeled using services composition mechanism along with dynamic discovery of available service agents enabled through hierarchical and distributed context directories organization. We introduce also a new ontological model to describe contextual knowledge and services interfaces. The proposed middleware is applied to the design of travel organization service scenario, based on the composition of some ad hoc context services.

Towards an automatic approach for ubiquitous robotic services composition

The field of ubiquitous robotics is becoming an active research domain. One of the more challenging of this domain is providing services composition in a seamless manner. Several recent research efforts have dealt with the services composition problem in ubiquitous environment. However, most of them assume that the composition plan was already constructed despite of the major challenge and complexity that involves this task. In this paper, we propose an approach which generates automatically a flexible plan for the services composition by optimising both the number of services and parameters which appear in this composition. Our plan is constructed in an abstract way in order to be adaptable to the changes which occur on the services and the context of use. This approach is tested under USARSim simulator on a set of ubiquitous robotic services which assist an elderly person. We have also tested our approach when the complexity of services composition increases. Obtained results show clearly the feasibility and scalability of our approach in ubiquitous environment.

Semantic Reasoning Framework to Supervise and Manage Contexts and Objects in Pervasive Computing Environments

In spite of the fact that many of the proposed context awareness frameworks allow scalability and dynamic interaction with heterogeneous devices disseminated in the environment, very often they are not able to fully profit from the semantic technology to supervise and manage the environment. Indeed, using ontologies allows us to integrate information coming from heterogeneous sources and to share it easily. The most important challenge to face in this context is how the system can understand easily the behavior and context events handled by all the actors (person, robot, actuators, sensors ... etc.) to take an adequate decision. The work presented here shows how the semantic reasoning framework used in the SEMbySEM European project, based on the use of a new semantic language (a “μConcept Knowledge Representation Language”) built up on top of RDF(S) and of the corresponding “μConcept Rule Language may facilitate reasoning and acting upon heterogeneous context sources, allowing their ‘intelligent’ monitoring and management and their dynamic visualization. For the validation of the proposed concepts and models, a concrete scenario dedicated to the monitoring of elderly people in smart home has been proposed and implemented.

A semantic approach for enhancing assistive services in ubiquitous robotics

The Ambient Intelligence (AmI) technologies have the potential to create intelligent environments with new generation of assistive services, enhanced with ubiquitous robots. These environments have the ability to be anticipatory, responsive and intelligent providers of assistive services anytime and anywhere. These services can assist frail persons effectively in their daily tasks. One of the main challenging research problems in assistive robotics is to endow ubiquitous robots with ability to pro-actively taking on some tasks to help humans in performing complex activities, by participating with them just as other humans do, in normal societies or organizations.In this paper, we propose a collective intelligence framework based on narrative reasoning and natural language processing. In the proposed approach, we propose a hybrid model that bridges together the Narrative Knowledge Representation Language (NKRL), from natural language processing field, and the HARMS (Humans, software Agents, Robots, Machines and Sensors) model, from multi-agent systems engineering field. This model is able to (i) drive the dialogues between humans, robots and smart devices, (ii) understand a complex situation, and (iii) trigger reactive actions, in the ubiquitous environment, according to given contexts.Two scenarios dedicated to the assistance of a frail person in a smart home equipped with a companion robot and smart objects are implemented and discussed for validation purposes of the proposed framework. Overview of the ubiquitous robotics and ambient intelligence.Hybrid model that bridges together the NKRL and the HARMS model.Context Aware modeling based on the upper ontologies of NKRL.Semantic Reasoning based Collective Intelligence in ubiquitous robotics.

Towards an event-aware approach for ubiquitous computing based on automatic service composition and selection

Service composition in ubiquitous and pervasive environments is becoming an active research domain which has received widespread attention in recent years. It aims to offer seamless access to a variety of high level and complex functionalities by combining existing services. Several frameworks have been designed to support service composition in ubiquitous and pervasive environments. Although some ubiquitous requirements and challenges are relatively well addressed by the proposed frameworks, others are still at a preliminary stage and should be well explored such as, automatic service composition with little human intervention, context and quality of service management, and service selection under uncertainty and changes. For this end, we propose in this paper a layered design approach for flexible and failure tolerant service composition using two main phases: off-line phase and on-line phase. In the off-line phase, a global graph that links all the available abstract services is generated automatically using rule-based technique. The defined rules aim at optimizing both the number of services and parameters that appear in the global graph. In the on-line phase, a subgraph is extracted spontaneously from the global graph according to the occurred and detected event in the environment at real time. Thereafter, the extracted subgraph is performed using service selection strategies. A prototype implementation including real services for event detection in smart home shows clearly the feasibility of the proposed approach in real environment. Also, the set of performed evaluation tests reveals the interest and the performance of the proposed algorithms.