A Framework for Anomaly Diagnosis in Smart Homes Based on Ontology

International audienceSmart homes are pervasive environments to enhance the comfort, the security, the safety and the energy consumption of the residence. An ambient intelligence system uses information of devices to represent the context of the home and its residents. Based on a context database, this system infer the daily life activities of the resident. Hence, abnormal behavior or chronic disease can be detected by the system. Due to the complexity of these systems, a large variety of anomalies may occur and disrupt the functioning of critical and essential applications. To detect anomalies and take appropriate measures, an anomaly management system has to be integrated in the overall architecture. In this paper, we propose an anomaly management framework for smart homes. This framework eases the work of designers in the conception of anomaly detection modules and processes to respond to an anomaly appropriately. Our framework can be used in all heterogeneous environments such as smart home because it uses Semantic Web ontologies to represent anomaly information. Our framework can be useful to detect hardware, software, network, operator and context faults. To test the efficiency of our anomaly management framework, we integrate it in the universAAL middleware. Based on a reasoner, our framework can easily infer some context anomalies and take appropriate measures to restore the system in a full functioning state

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación y Universidades

Embedded intelligence for electrical network operation and control

Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management

Modular Approach for Expert System toward Anomaly: N-Layers

International audience—Smart cities and smart homes are booming fields of development of pervasive systems. With the high stakes these systems have to manage, and their sheer complexity, anomalies have to be considered. In these complex systems are many connected components with computing capacities. They can manage anomalies, even if partially, and can act as some kind of expert systems. These expert systems can be relied upon to provide anomaly management. The complexity to manage generic expert systems brings us to suggest another approach. In this paper, the N-layers is discussed. It layers the various existing expert systems to organize them as a more generic expert system. It also aims to correct some difficulties to develop and integrate generic expert systems into various scales complex systems, such as smart cities or smart homes

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Reinforcement learning multi-agent system for faults diagnosis of mircoservices in industrial settings

This paper develops a new framework called MASAD (Multi-Agents System for Anomaly Detection), a hybrid combination of reinforcement learning, and a multi-agents system to identify abnormal behaviors of microservices in industrial environment settings. A multi-agent system is implemented using reinforcement learning, where each agent learns from the given microservice. Intelligent communication among the different agents is then established to enhance the learning of each agent by considering the experience of the agents of the other microservices of the system. The above setting not only allows to identify local anomalies but global ones from the whole microservices architecture. To show the effectiveness of the framework as proposed, we have gone through a thorough experimental analysis on two microservice architectures (NETFLIX, and LAMP). Results showed that our proposed framework can understand the behavior of microservices, and accurately simulate different interactions in the microservices. Besides, our approach outperforms baseline methods in identifying both local and global outliers.submittedVersio

Innovative Business Model for Smart Healthcare Insurance

Information revolution and technology growth have made a considerable contribution to restraining the cost expansion and empowering the customer. They disrupted most business models in different industries. The customer-centric business model has pervaded the different sectors. Smart healthcare has made an enormous shift in patient life and raised their expectations of healthcare services quality. Healthcare insurance is an essential business in the healthcare sector; patients expect a new business model to meet their needs and enhance their wellness. This research develops a holistic smart healthcare architecture based on the recent development of information and communications technology. Then develops a disruptive healthcare insurance business model that adapts to this architecture and classifies the patient according to their technology needs. Finally, and implementing a prototype of a system that matches and suits the healthcare recipient condition to the proper healthcare insurance policy by applying Web Ontology Language (OWL) and rule-based reasoning model using SWRL using Protég