Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Control of HVAC system comfort by sampling

The sampling of the users comfort, allows observing and predicting the level of comfort on the HVAC (heating, ventilation, and air conditioning) systems. The development of online sampling systems assists in the recognition of the behavior patterns that occur in the offices. This paper presents a user-friendly tool designed and developed in order to make easier knowledge extraction and representation to make possible decisions about which demand that must prevail, the user comfort or saving into a central system. This decision may depend on the occupation and feeling of comfort of its occupants. Some studies have put neutral thermal conditions outside the ranges of comfort of the ASHRAE standard. The actual rules of the HVAC systems are based on studies carried out on specific populations in a specific space, which are not valid in certain situations. This is a dynamic idea of the comfort based in real data. The methodology used provides important and useful information to be able to select the comfort set-point of the rooms of a central heating system without the need to use fixed values based on programmed time schedules or any other methodology. The response to comfort in an area of a building throughout the day can be seen in this study. The users were assessed using a standard set of key questions in order to measure the level of satisfaction with environmental factors, thanks to a questionnaire of imprecise answers. We seek an improvement in the building users, regardless of their particularities

User activities outliers detection; integration of statistical and computational intelligence techniques

In this paper, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). In the first stage, the Hamming distance is used to measure the differences between different activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error. In the second stage of the process, the calculated indices are provided as inputs to the FRBSs to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioural patterns

Discovering frequent user-environment interactions in intelligent environments

Intelligent Environments are expected to act proactively, anticipating the user's needs and preferences. To do that, the environment must somehow obtain knowledge of those need and preferences, but unlike current computing systems, in Intelligent Environments the user ideally should be released from the burden of providing information or programming any device as much as possible. Therefore, automated learning of a user's most common behaviors becomes an important step towards allowing an environment to provide highly personalized services. In this paper we present a system that takes information collected by sensors as a starting point, and then discovers frequent relationships between actions carried out by the user. The algorithm developed to discover such patterns is supported by a language to represent those patterns and a system of interaction which provides the user the option to fine tune their preferences in a natural way, just by speaking to the system

A survey on the evolution of the notion of context-awareness

The notion of Context has been considered for a long time in different areas of Computer Science. This article considers the use of context-based reasoning from the earlier perspective of AI as well as the newer developments in Ubiquitous Computing. Both communities have been somehow interested in the potential of context-reasoning to support real-time meaningful reactions from systems. We explain how the concept evolved in each of these different approaches. We found initially each of them considered this topic quite independently and separated from each other, however latest developments have started to show signs of cross-fertilization amongst these areas. The aim of our survey is to provide an understanding on the way context and context-reasoning were approached, to show that work in each area is complementary, and to highlight there are positive synergies arising amongst them. The overarching goal of this article is to encourage further and longer-term synergies between those interested in further understanding and using context-based reasoning

Metodología para el análisis y toma de decisiones mediante muestreo en los edificios

The sampling of the users comfort, allows observing and predicting the level of comfort on the HVAC system. The development of online sampling systems assists in the recognition of the behaviour patterns that occur in the offices. This paper presents a methodology specially designed and developed in order to make easier knowledge extraction and representation, in this way it possible to make decisions about the comfort in buildings. The methodology used provides important and useful information to select the comfort set-point of the rooms of a central HVAC system without the need to use fixed values based on programmed time schedules or any other methodology. In this methodology, the users are evaluated by using a standard set of key questions in order to measure the level of satisfaction respect to environmental factors, thanks to a questionnaire of imprecise answers. We seek an improvement in the building users, regardless of their particularities.El muestreo del confort de los usuarios, permite observar y predecir el nivel de confort en el sistema de aire acondicionado. El desarrollo de los sistemas de muestreo online ayuda en el reconocimiento de patrones de comportamiento que se producen en las oficinas. En este trabajo se presenta una metodología especialmente diseñada y desarrollada con el fin de facilitar la extracción y representación del conocimiento, de esta manera es posible tomar decisiones sobre el confort en los edificios. La metodología utilizada proporciona información importante y útil para seleccionar el punto de ajuste del confort de las habitaciones para un sistema de climatización central, sin la necesidad de utilizar valores fijos, basados en horarios programados o cualquier otra metodología. En esta metodología, los usuarios son evaluados mediante el uso de un conjunto estándar de preguntas clave para medir el nivel de satisfacción respecto a los factores ambientales, gracias a un cuestionario de respuestas imprecisas. Buscamos una mejora en los usuarios de los edificios, independientemente de sus particularidades

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars