This paper aims to investigate the development of an intelligent refrigerator through embedding intelligent agents in normal refrigerators. The proposed intelligent refrigerator will be able to recognize various food items and learn user consumption habits via the proposed intelligent agent. The system incorporates a low-cost camera which feeds its input to the fuzzy agent in order to classify different food items and track their amounts. Using this information, consumption patterns representing the amount of items taken by users are generated and fed into a neural network system which is aimed to learn user habits and provide feedback to the user in cases where he/she consumes an unusual number of items. The system employs a second camera to distinguish between different users so that user specific information such as items consumed and calories taken can be stored separately. The resulting system has an accuracy of ≃ 90% for item identification and shows a very good performance for learning the habits of different users. The system also provides a graphical interface to display available items which allows users to generate an automated shopping list.

Among all domestic appliances populating our homes, it seems that hobs are not yet enabled with intelligent and energy efficient systems where people still use hobs in the same way their parents or grandparents did. This work aims to develop autonomous cooking systems which are able to provide a better cooking practice while increasing the energy efficiency of existing hobs. In this paper, we will present fuzzy logic based agents which could be embedded in normal hobs converting them to intelligent and more energy efficient ones. The realised system is able to self-regulate the heating during the most widely used cooking techniques including boiling, deep-frying, stir-frying, warming and melting. The agent is also capable of identifying when the user intervention is needed where the system continuously controls the cooking pot temperature preventing it from reaching unwanted values and detects when a pan boils-dry switching the appliance off immediately. We will present various real world experiments where it will be shown that the developed intelligent hob is capable of performing autonomous cooking to the satisfaction of the human user(s) while providing an energy saving of around 15-20% when compared to the human cooking practice.

Ambient intelligence is a new information paradigm, where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive, and responsive to their needs. Hence, one of the important requirements for ambient intelligent environments (AIEs) is the ability to localize the whereabouts of the user in the AIE to address her/his needs. In order to protect user privacy, the use of cameras is not desirable in AIEs, and hence, there is a need to rely on nonintrusive sensors. There are various localization means that are available for outdoor spaces such as those which rely on satellite signals triangulation. However, these outdoor localization means cannot be used in indoor environments. The majority of nonintrusive and noncamera-based indoor localization systems require the installation of extra hardware such as ultrasound emitters/antennas, radio-frequency identification (RFID) antennas, etc. In this paper, we propose a novel indoor localization system that is based on WiFi signals which are free to receive, and they are available in abundance in the majority of domestic spaces. However, free WiFi signals are noisy and uncertain, and their strengths and availability are continuously changing. Hence, we present a fuzzy logic-based system which employs free available WiFi signals to localize a given user in AIEs. The proposed system receives WiFi signals from a large number of existing WiFi access points (up to 170 access points), where no prior knowledge of the access points locations and the environment is required. The system employs an incremental lifelong learning approach to adjust its behavior to the varying and changing WiFi signals to provide a zero-cost localization system which can provide high accuracy in real-world living spaces. We have compared our system in both simulated and real environments with other relevant techniques in the literature, and we have found that our system outperforms the other systems in the offline learning process, whereas our system was the only system which is capable of performing online learning and adaptation. The proposed system was tested in real-world spaces from a living lab intelligent apartment (iSpace) to a town center apartment to a block of offices. In all these experiments, our system has been highly accurate in detecting the user in the given AIEs, and the system was able to adapt its behavior to changes in the AIE or the WiFi signals. We envisage that the proposed system will play an important role in AIEs, especially for privacy concerned situations like elderly care scenarios.

The identification of services during the execution of service-based applications to replace services in them that are no longer available and/or fail to satisfy certain requirements is an important issue. In this paper, we present a framework to support runtime service discovery. This framework can execute service discovery queries in pull and push mode. In pull mode, it executes queries when a need for finding a replacement service arises. In push mode, queries are subscribed to the framework to be executed proactively and, in parallel with the operation of the application, to identify adequate services that could be used if the need for replacing a service arises. Hence, the proactive (push) mode of query execution makes it more likely to avoid interruptions in the operation of service-based applications when a service in them needs to be replaced at runtime. In both modes of query execution, the identification of services relies on distance-based matching of structural, behavioral, quality, and contextual characteristics of services and applications. A prototype implementation of the framework has been developed and an evaluation was carried out to assess the performance of the framework. This evaluation has shown positive results, which are discussed in the paper.

Ambient Intelligence (AmI) is a vision that refers to an information technology paradigm where a physical environment is `aware' of its human occupants' presence/context and is sensitive, adaptive and responsive to their needs. Physical environments that are augmented with AmI are called Ambient Intelligent Environments (AIEs) which are deemed to be intelligent because the system should be able to recognise human occupants, reason with context and program itself to meet the occupants' needs by learning from their behaviour [1]. However, there is a need also to deal with real-world scenarios which involve multiple users occupying a given AIE. In order to handle multi-user AIEs and control them, there is a need to have agents that are able to learn the user(s) behaviours and handle the intra and inter-user uncertainties as people have different preferences and profiles which continuously change. In this paper, we present a zSlices based type-2 fuzzy agent which employs zSlices general type-2 fuzzy systems to learn the user(s) preferences and profiles and handle the encountered intra and inter-user uncertainties. The agent will behave according to a learned user profile that is unique to an individual user or a group of users and so the profile-selection problem manifests when the set of users in an AIE changes (i.e. when people enter/ leave an AIE). The proposed agent employs a novel strategy that we call Dynamic Profile-Selection that uses a cloud-based profile repository in order to support the agent activity in multiple AIEs. To demonstrate the proposed approach, we have conducted real-world experiments on two distinct AIEs which are the intelligent apartment (iSpace) and the intelligent Classroom (iClassroom) located at the University of Essex.

One of the important requirements for Ambient Intelligent Environments (AIEs) is the ability to localise the whereabouts of the user in the AIE to address her/his needs. The outdoor localisation means (like GPS systems) cannot be used in indoor environments. The majority of non intrusive and non camera based indoor localisation systems require the installation of extra hardware such as ultra sound emitters/antennas, RFID antennas, etc. In this paper, we will propose a novel fuzzy logic based indoor localisation system which is based on the WiFi signals which are free to receive and they are available in abundance in the majority of domestic spaces. The proposed system receives WiFi signals from a big number of existing WiFi Access Points (up to 170 Access Points) with no prior knowledge of the access points locations and the environment. The proposed system is able to adapt online incrementally in a lifelong learning mode to deal with the uncertainties and changing conditions facing unknown indoor structures with a few days of calibration at zero-cost deployment with high accuracy. The proposed system was tested in simulated and real environments where the system has given high accuracy (that outperformed the existing techniques) to detect the user in the given AIE and the system was able also to adapt its behaviour to changes in the AIE or the WiFi signals.

In this paper we motivate and illustrate the use of bigraphs as a formal framework and methodology for the description, design and analysis of intelligent environment systems. Through a series of examples, we provide an overview of bigraphs, their composition, their evolution under reaction rules, and their refinement. We argue that bigraphs offer several advantages: first, they are intuitive and lie close to the topic of investigation, second, they are relatively simple to understand and deploy (in contrast to the systems they may analyse), third, they offer a means to tame complexity through multiple description at different levels of abstraction, fourth, and finally, the system itself can be usefully used without having to engage with its mathematical foundations.

Ubiquitous Computing asserts that technology will soon be pervasive in our lives. But how will that technology be organized and made available to us as we roam through the many spaces of our daily lives? How will we see and use that which we have a right to, and more importantly, how will a lack of access rights be enforced? These are questions that have been raised following previous research projects. They become more significant as the concept of intelligent environments scales-up beyond the boundaries of four walls. In this paper we propose a formal model that will form a roadmap for some of our upcoming research. This paper is a hypothetical work that explores some questions and poses some answers - Influenced by experimentation and with a view to further investigation.

As the Home Area Network (HAN) evolves, there is an increase in both the number and diversity of device deployment. This includes embedded devices whose resource constraints do not permit the efficient performance of high level middleware functionality. We herein present the functionality and knowledge representations required to enable such “simpleware” devices to be dynamically represented by proxy within our Nexus middleware framework. We also present a use case to illustrate the proposed solution.

In this short conceptual paper we explore the need for demonstrations of intelligent environments research that can convey what we as researchers think to potential users that have limited exposure to such ideas. This is especially important where physical and virtual worlds meet in the smart home context. We present several exemplars that are intended to promote user understanding through the use of mixed reality technologies.

This paper discusses the requirements of future Home Area Networks (HAN) with respect to resource discovery. We discuss existing methods and provide some experimental data that shows them to be unsuitable for future HANs. In response, we present a new protocol called “Entity Resolution Protocol” (ERP) and experimental data which shows that it significantly outperforms the other methods discussed.

Service discovery has been recognised as an important activity for service-based systems. In this paper we describe a framework for dynamic service discovery that supports the identification of service during the execution time of service-based systems. In the framework, services are identified based on structural, behavioural, quality, and contextual characteristics of a system represented in query languages. The framework supports both pull and push modes of query execution. In the approach, a service is identified based on the computation of distances between a query and a candidate service. A prototype tool has been implemented in order to illustrate and evaluate the framework. The paper also describes the results of a set of experiments that we have conducted to evaluate the work.