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Buildings and other facilities must protect their assets and occupants from hazards and unauthorised intrusion, with access control systems a critical component of the management of the facilities. The advent of pervasive systems and cyber-physical systems means that access control can become dynamic, responding to the observed locations of people and objects, and meeting the stated needs of the occupants. We present three components of a smart ambient access control system for safety and security. We describe dynamic routing around mobile assets or hazards, dynamic security classification which changes the security state of rooms and zones based on observed presence of items, and dynamic zone configuration, which adapts the access states of controlled doorways to construct larger virtual zones, to provide for free movement within newly constructed zones, and to provide secure zones around assets and people as required. The components are demonstrated in an architecture integrating GPS-enabled devices, webservers, management software and optimisation solvers.

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.

Smart spaces deliver digital services to optimize space use and enhance user experience. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash; there is the potential risk of physical harm to the space and its users. Ensuring safety in this type of cyberphysical system is critically important.

NGSI-LD is an open specification released by ETSI which proposes an information model and an API for an easy to use and standard management of context information. The NGSI- LD information model is framed within an ontology and adopts JSON-LD as serialization format for context information. This paper presents an approach to the implementation of the NGSI- LD speci?cation over a SPARQL Event Processing Architecture. This work is being developed within the European-Brasilian H2020 SWAMP project focused on implementing an Internet of Things platform providing services for smart water management in agriculture.

Smart spaces provide a platform for cooperative service construction by many devices in the Internet of Things (IoT) environments. When a service is constructed the service needs delivering to appropriate clients, which is typically implemented using the subscription operation (i.e., information-driven service construction). The passive form of subscription is ineffective in the IoT settings since the centralized solution—smart space information broker—needs to control all service construction updates and to notify all interested clients. This article considers the problem of active control for information-driven service construction when each client can use its own (individual) strategy to (additionally) control ongoing updates in the subscribed information. Five strategies for active control are selected for this study. For some simplified assumptions, analytical estimates are provided. For close-to-real evaluation of the strategies a simulation model is developed, based on which several performance metrics are experimentally studied.

This research discusses about a way to identify appliances in the smart space using artificial neural network (ANN). Smart appliances identification technique can solve a few specific problems for AAL like reducing the implementation complexity in the application level, finding the anomaly in the early stages. We have proposed an ANN-based approach to identify the appliances using smart space data. The smart appliances identification process is initiated by collecting data related to power consumption of smart appliances which are used for AAL services. An individual template along ANN-based approach is proposed to improve the accuracy. Through the interaction of the template, the proposed system has the ability to improve its identification by labeling the unlabeled input data. An ANN-based feedforward algorithm multilayer perceptron (MLP) is used for identifying the appliances. The output from MLP is observed whether the data pattern is a new class or not. The template provides the possible solution based on features and data. The proposed model has achieved identification performance with an average of 93% accuracy.

International audience; Next generation infrastructures will be deeply impacted by the introduction of the Internet of Things, with the objective of improving their current operating models and methods. This follows the trend of the Web of Things (WoT), which projects to seamlessly connect various smart devices and IoT frameworks due to its flexible nature, hence, enabling new innovative services and opportunities. However, it also means that more and more data need to be protected from external threats and unauthorized accesses. Therefore, additional security and privacy mechanisms need to be deployed, together with an efficient management of these IoT devices. This issue becomes more convoluted and hardly tractable when dealing with several devices of the infrastructure dispatched over different physical Local Area Networks (LAN)s that we call “Smart Spaces” (SS)s. Thus, this paper proposes to solve this issue by introducing an architecture based on a Software Defined Networking controller for providing a secure access to the different SSs of a smart infrastructure, in a centralized way. We argue that a centralized view can greatly simplify and improve the security management of such infrastructures. Moreover, to support our assumptions, a Proof of Concept, with a concrete implementation of all the components is provided.

The increasingly large availability of sensors in modern houses, due to the establishment of home assistants, allow to think in terms of smart houses where behaviours can be automatized based on user habits. Common tasks required to this aim include activity prediction, i.e., the task of forecasting what is the next activity a human is going to perform in the smart space based on past sensor logs. In this paper, we propose a novel activity prediction method for smart houses based on the seminal probabilistic method named Marked Temporal Point Process Prediction.

Visual programming languages are a common way for enabling end users to create simple, customized programs in their smart homes. Although visual programming aims at lowering the syntactic and cognitive barriers existing languages lack a simple method for accessing services of smart spaces surrounding the home and therefore restrain the innovative capabilities of users. For example, accessing information about energy consumption or smart parking is often cumbersome and limited to users with advanced technical skills. Based on expert interviews, we extended the existing visual programming language Node-RED with functionalities that enable users to easily access data and services within and beyond their smart homes. Using this prototype, we performed a first evaluation of our solution with some inhabitants of smart homes.

In this paper, we describe the educational and technological design of a highly modularized STEM learning toolkit for students to learn the technologies behind IoT and Fintech through setting up an unmanned smart shop on their own. The modularized design allows students to work individually or collaborate as a team, which facilitates both the teaching needs required from the experimental class and also the self-study needs of researchers. This physical unmanned smart space laboratory is a project from a vocational institute in Hong Kong. We conducted a user study to evaluate the performance of the smart shop learning toolkit and had received positive feedback, in which 87% of the students prefer to learn the smart shop technologies and STEM through our toolkit. Our results demonstrate that our smart technology learning toolkit is an effective and fun way to teach students about STEM and the cutting-edge technologies.

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in...

Continued oscillation between digital environments and physical realities has created a demand for hyper-interactive community spaces. Because the overall cost of prototyping extended reality (XR) experiences can require large initial investments of time and money, current art spaces could be used for prototyping digital environments with physical spaces. Integrating an art gallery with digital sensors, cameras, and projectors could allow for rapid prototyping of XR development, solving rudimentary problems found with spatial navigation, sensory exposure, and psychological ramifications in a more cost efficient manner.

The existing wide spectrum of embedded, multimedia, and mobile equipment provides an effective base for making museums smart or intelligent. By using the Internet of Things technology and the smart spaces paradigm the distance can be shortened between exhibits and their descriptive information on one side and consumers and providers of this information on the other side. In this paper, the authors discuss a smart museum concept and present a semantic approach to design of advanced information services for smart museums. The central point is introduction of the semantic layer to create a semantic network. The proposed approach reduces the semantic layer development to the following components: software infrastructure, semantic layer ontology, and mobile user access. For these components the authors provide design solutions, which are analyzed in respect to particular services for the History Museum of Petrozavodsk State University. The semantic approach can be applied to development of many museum services as well as in various digital environments of museums and cultural heritage areas.

International audience; Handling large knowledge bases of information from different domains such as the World Wide Web is a complex problem addressed in the Resource Description Framework (RDF) by adding semantic meaning to the data itself. The amount of linked data has brought with it a number of specialized databases that are capable of storing and processing RDF data, called RDF stores. We explore the RDF store landscape with the aim of finding an RDF store that sufficiently meets the storage needs of an enhanced living environment, more concretely the requirements of a Smart Space platform aimed at running on a cluster set up of low-power hardware that can be run locally entirely at home with the purpose of logging data for a reactive assistive system involving, e.g., activity recognition or domotics. We present a literature analysis of RDF stores and identify promising candidates for implementation of consumer Smart Spaces. Based on the insights provided with our study, we conclude by suggesting different relevant aspects of RDF storage systems that need to be considered in Ambient Assisted Living environments and a comparison of available solutions.

The more information users disclose to pervasive systems or social media, the better quality and enhanced experience they enjoy for a wider variety of personalised services. However, the privacy concerns of individuals that use such systems have dramatically risen the last years, especially after several events of massive security breaches in various computing or communication systems that have reached the news. This chapter presents the approach being employed by the SOCIETIES project to protect the privacy of sensitive user data and ensure the trustworthiness of delivered services via social and pervasive computing systems. This framework has already been designed, implemented and evaluated via real user trials engaging wide and heterogeneous user populations. In addition to the respective requirements, architecture and features discussed herewith, this chapter elaborates on the user trial that has been conducted in university settings to validate this system focusing on the privacy and trust evaluation results obtained.

The proliferation of smart spaces, such as smart buildings, is increasing opportunities for offenders to exploit the interplay between cyber and physical components, in order to trigger security incidents. Organizations are obliged to report security incidents to comply with recent data protection regulations. Organizations can also use incident reports to improve security of the smart spaces where they operate. Incident reporting is often documented in structured natural language. However, reports often do not capture relevant information about cyber and physical vulnerabilities present in a smart space that are exploited during an incident. Moreover, sharing information about security incidents can be difficult, or even impossible, since a report may contain sensitive information about an organization. In previous work, we provided a meta-model to represent security incidents in smart spaces. We also developed an automated approach to share incident knowledge across different organizations. In this paper we focus on incident reporting. We provide a System Editor to represent smart buildings where incidents can occur. Our editor allows us to represent cyber and physical components within a smart building and their interplay. We also propose an Incident Editor to represent the activities of an incident, including -for each activity- the target and the resources exploited, the location where the activity occurred, and the activity initiator. Building on our previous work, incidents represented using our editor can be shared across various organizations, and instantiated in different smart spaces to assess how they can re-occur. We also propose an Incident Filter component that allows viewing and prioritizing the most relevant incident instantiations, for example, involving a minimum number of activities. We assess the feasibility of our approach in assisting incident reporting using an example of a security incident that occurred in a research center. European Commission Horizon 2020 European Research Council Science Foundation Ireland

An approach to the organization of interactions between services in the smart space (hybrid system control) is illustrated based on the example of a room cleaning scenario in the smart-home system. The smart-space technology is used to support interactions between system services, while the ontology-based approach is used to provide interoperability of the services. Hybrid automata are used to describe the behavior of each individual service, which enables both the automatic generation of the program code and the verification of system components.

Commercial off-the-shelf gaming devices (e.g. such as Kinect) are demonstrating to have a great potential beyond their initial service purpose. In particular, when integrated within the environment or as part of smart objects, peripheral COTS for gaming may facilitate the definition of novel interaction methods, particularly applicable to smart spaces service concepts. In this direction, this paper describes a system prototype that makes possible to deliver multimodal interaction with the media contents in a Virtual Window. Using a Kinect device, the Interactive Window itself adjusts the video clipping to the real time perspective of the user — who can freely move within the sensor coverage are. On the clipped video, the user is able to select objects by pointing at meaningful image sections and to initiate actions related to them. Voice orders may also complete the interaction when necessary. Although implemented for smart spaces, the service concept can also be applied to learning, remote control processes or teleconference.

In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space applications, state-of-the-art AR technology is not ready or adequate for real-world deployments due to its insufficient accuracy. The accuracy limitation is directly attributed to uncertainties stemming from multiple sources in the AR system. Hence, one of the major goals of AR research is to improve system accuracy by minimizing or managing the uncertainties encountered throughout the AR process. As we cannot manage uncertainties well without measuring them, we must first quantify their impact. Nevertheless, such a quantification process is very challenging given that uncertainties come from diverse and heterogeneous sources. In this article, we propose an approach, which can account for multiple uncertainty sources and assess their impact on AR systems. We introduce several metrics to quantify the various uncertainties and their impact. We then conduct a quantitative impact analysis of uncertainties utilizing data collected from actual smart spaces that we have instrumented. The analysis is intended to serve as groundwork for developing “diagnostic” accuracy measures of AR systems capable of pinpointing the sources of accuracy loss. This is to be contrasted with the currently used accuracy measures.

Smart space is gaining popularity in academia and industry due to its deep influence on humans, computers, and physical objects. However, quality of experience has attracted less attention in the design of smart space, which results in poor user satisfaction in actual design solutions. In this article, we propose a systematic method for augmenting smart space design, which allows the refinement of high-level user needs into the underlying design solution by leveraging an intermediate representation model, meeting nearly optimal user satisfaction including locality satisfaction, energy consumption satisfaction, QoS satisfaction, and human computer interaction satisfaction. Also, we present the design platform of smart space, which achieves the design flow combined with the space model and platform library. Finally, we use a home health care application to demonstrate the effectiveness and feasibility of our methodology.

Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

In Smart Environments, people are immersed in time and space in an augmented reality: computing systems are ubiquitous, intelligence pervades the whole environment and exploits space and time awareness to provide a globally contextualised, adaptive user experience. Because of their socio-technical nature, the development of Smart Environments calls for skills, concepts, methodologies, technologies from the most diverse fields – AI, coordination, distributed systems, organisational sciences, etc. –, thus inherently calling for a multi-paradigm perspective. This paper explores the Smart Environment context moving from two basic bricks: Butlers for Smart Spaces, a framework for the design of smart services for users immersed and interacting with their surrounding environment; and Home Manager, a multi-paradigm, agent-based platform for the implementation of Smart Living contexts—particularly focused on the reasoning aspects, so as to anticipate the users’ needs. For concreteness, we take a Smart Kitchen as our running example: we first discuss how it can be devised using Butlers for Smart Spaces, and then deployed on the Home Manager platform—highlighting in particular the context reasoning, prediction and adaptation aspects.

Network robots provide services to users in smart spaces while being connected to ubiquitous instruments through wireless networks in ubiquitous environments. For more effective behavior planning of network robots, it is necessary to reduce the state space by recognizing a smart space as a set of spaces. This paper proposes a space classification algorithm based on automatic graph generation and naive Bayes classification. The proposed algorithm first filters spaces in order of priority using automatically generated graphs, thereby minimizing the number of tasks that need to be predefined by a human. The filtered spaces then induce the final space classification result using naive Bayes space classification. The results of experiments conducted using virtual agents in virtual environments indicate that the performance of the proposed algorithm is better than that of conventional naive Bayes space classification.

Mobile health (m-Health) scenarios and Internet of Things (IoT) technologies form an important direction for enhancing medical systems for Ambient Assisted Living (AAL). Yet current development meets with two challenges: 1) use of patient's health data with strong security guarantees in mobile network and resource-constrained assumptions and in emergency situations, 2) inclusion of personal data to the entire system for “smart” service construction and delivery. This paper presents a smart space based architectural model that adopts emerging IoT technologies to enable security of personal mobile data and their intelligent utilization in health services. To support the service intelligence, the authors employ the smart spaces approach with its prominent technologies adopted from IoT and Semantic Web. The intelligence and security solutions are considered symbiotic to present better user-experience, security level, and utility of a system.

This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies - nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms - have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications.

The integration of semantic technologies and TV services is a substantial innovation to improve the services to users in an environment that is extended beyond the fixed home environment. But currently, this integration is mainly limited to provide personalized recommendation services and systems by matching user static preferences. Designing and development of interactive TV (iTV) applications using semantic technologies are not realized yet. In this work, we explore the potential of introduction semantic technologies and smart spaces in design and development of iTV applications. We use an example scenario to show how future iTV applications include the mesh-up of information from different sources. We proposed a methodology and show how ontology-driven approach can help to design and develop these iTV applications. We demonstrate the suitability of our ontology-driven application development tools and rule-based approach for the development of highly dynamic context-aware iTV applications.

Current smart spaces require more and more sophisticated sensors able to acquire the state of the environment in order to provide advanced and customized services. Among the most important environmental variables, locations of users and their identities represent a primary concern for smart home applications. Despite some years of investigation in indoor positioning, the availability of systems designed as components pluggable into complex home automation platforms is limited. We present People Localization and Tracking for HomE Automation PLaTHEA, a vision-based indoor localization system specifically tailored for Ambient Assisted Living applications. PLaTHEA features a novel technique to acquire a stereo video stream from a couple of independent and not synchronized network-attached cameras, thus easing its physical deployment. The input stream is processed by integrating well-known techniques with a novel tracking approach targeted to indoor spaces. The system has a modular architecture that offers clear interfaces exposed as Web services, and it can run on off-the-shelf and cheap hardware both in terms of sensing devices and computing units. We evaluated PLaTHEA in real usage conditions and reported the measured performance in terms of precision and accuracy. Low light, crowded and large monitored environments might slightly decrease the performance of the system; nevertheless, the results here presented show that it is perfectly suitable to be employed in the typical domestic day-to-day life settings. Copyright © 2014 John Wiley & Sons, Ltd.

During the past few decades, cars have transformed from being relatively mundane spaces underpinned by mechanical platforms with sprinklings of electronics into complex computational platforms. As a result of this continual development and the smart spaces it has enabled, we are now seeing the signs of tectonic shifts in car-related experiences, ranging from ride services to autonomous vehicles. These are underpinned by the key elements of intelligent systems--sensing, actuation, computation, and communication resources--which are now routinely built-in to cars, along with additional resources brought in via mobile devices and beamed-in from the cloud. This special issue offers an insight into what we can expect from smart vehicle spaces and the technology that enables them.

A number of intelligent devices and services have been created and deployed in pervasive environment. Yet it could be tedious to manipulate, interact and analyze them and generate proper composition plan for a given scenario. In this paper, an automatic service rules generation algorithm is proposed to address the problem. Rules are extracted from the history context acquired from previous manual operations. A service rule management tool is developed for achieving the automatic extraction and visual correction for service rules based on user preferences. Instead of manual processing, the scheme provides an effective way to decrease unnecessary user interventions in smart space. As our working prototype and examples demonstrate, indoor illumination services can be applied and updated automatically by using SWRLbased rules.

Smart spaces are dynamic environments for sharing information. People use phones to store and contact personal information and increasingly they have high-speed Internet connections. The smartphone has become a powerful computer in its own hands. In addition, smartphones are now being widely used in interactive broadcast systems. But this makes these attractive devices to challenges. The key challenges for smart spaces include security and interoperability between heterogeneous devices. To solve this problem, we survey these trends and propose the Interactive Broadcast Management System Architecture.

Smart spaces are dynamic environments for sharing device information. Key challenges for smart spaces include security and interoperability between heterogeneous devices. Thus, smart spaces and its environments must provide feasible solutions for authentication, access control and privacy. Open source Smart Space platform Smart-M3 is actively developed but does not have a sufficient security mechanism yet. The main focus of this paper is analysis and development of security mechanisms for Smart-M3 platform.

Smart Cities need to be designed to allow the inclusion of all kinds of citizens. For instance, motor disabled people like wheelchair users may have problems to interact with the city. Internet of Things (IoT) technologies provide the tools to include all citizens in the Smart City context. For example, wheelchair users may not be able to reach items placed beyond their arm’s length, limiting their independence in everyday activities like shopping, or visiting libraries. We have developed a system that enables wheelchair users to interact with items placed beyond their arm’s length, with the help of Augmented Reality (AR) and Radio Frequency Identification (RFID) technologies. Our proposed system is an interactive AR application that runs on different interfaces, allowing the user to digitally interact with the physical items on the shelf, thanks to an updated inventory provided by an RFID system. The resulting experience is close to being able to browse a shelf, clicking on it and obtaining information about the items it contains, allowing wheelchair users to shop independently, and providing autonomy in their everyday activities. Fourteen wheelchair users with different degrees of impairment have participated in the study and development of the system. The evaluation results show promising results towards more independence of wheelchair users, providing an opportunity for equality improvement. This work was partly funded by the Spanish Government through projects TIN2012-34965 PIGALL, TIN2011-27076-C03-02 CO-PRIVACY, TIN2014-57364-C2-2-R SMARTGLACIS, TEC2015-71303-R SINERGIA, and TSI-020602-2012-147 IRIS. The authors also acknowledge support from Obra Social “la Caixa” -ACUP through project 2011ACUP00261.

With Apps a popular concept was introduced allowing end-users to easily extend their devices such as smartphones or computers with specific functionality. Two million Apps have ever since found their way into each of the popular App-stores Google Play and Apple Store. We argue that the App-concept is not only well applicable to single devices but also to complete environments equipped with smart networked things. In the moment when Apps can be easily downloaded and executed in home, office and industry environments a wide new applications space will be opened up. In this work we introduce the concept of Smart Space Apps that can be downloaded from a cloud-based App-store into a smart environment where they dynamically utilize the capabilities of available smart things to optimally achieve the purpose they were installed for. We introduce a unified schema for the access of sensors and actuators of heterogeneous devices from within Smart Space Apps and describe the middleware and runtime that implements this approach. We explain how Apps are packaged into an exchangeable format and published within a cloud-based App-store. Multiple application use cases are shown and challenges of this novel approach are discussed.

International audience; One of the main issues regarding the monitoring of persons in a smart home environment is the accuracy of the daily control of the person, the health prevention and the timely prediction of abnormal situations. To tackle this problem, this work proposes the use of an improved version of the Elman Neural Network (Elman-NN). In order to minimize the error between inputs and desired outputs, we optimize some criteria of the network to gain good results. We propose to use the Differential Evolution algorithm in the learning step of the Elman-NN to evolve the error performance. Our proposed model is responsible to predict the activities of the monitored elderly and to detect any state changes. This hybridization will help to optimize the weight and the bias of the network to achieve our objective function and to obtain a good network. The experimental results reveal that the proposed model is satisfactory for elderly person’s movement prediction.

A mobile health (mHealth) system is responsible to support and provision of healthcare services using mobile communication devices, such as mobile phones and tablet computers. Although mHealth system development for Internet of Things (IoT) environments is still in the early stage, the existing research shows the essential potential impact on the healthcare service industry. This chapter overviews important IoT use cases in healthcare focusing on the proposed IoT solutions for smart services provided by an mHealth system. The study discusses recent experience on development of smart space based mHealth systems. IoT-aware opportunities and properties of healthcare services are considered, including our vision on service intelligence in the mHealth case. Smart spaces based methods are described for mHealth system development, including multi-agent architectures, semantics-oriented information sharing, and operation with multisource heterogeneous data.

An approach to design Ambient Assisted Living systems is presented, which is based on APEX, a framework for prototyping ubiquitous environments. The approach is illustrated through the design of a smart environment within a care home for older people. Prototypes allow participants in the design process to experience the proposed design and enable developers to explore design alternatives rapidly. APEX provides the means to explore alternative environment designs virtually. The prototypes developed with APEX offered a mediating representation, allowing users to be involved in the design process. A group of residents in a city-based care home were involved in the design. The paper describes the design process as well as lessons learned for the future design of AAL systems., Jose C. Campos was funded by project NORTE-01-0145-FEDER-000016, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Jose Luis Silva acknowledges support from Fundacao para a Ciencia e a Tecnologia (FCT, Portugal), through project UID/EEA/50009/2013. Michael Harrison was funded by EPSRC research Grant EP/G059063/1: CHI+MED (Computer-Human Interaction for Medical Devices).

Advances in mobile computing, sensors, controls, ubiquitous networking, and other indoor automation infrastructure enable buildings to operate more intelligently, providing new levels of energy efficiency, safety, convenience, and quality of life. However, many features of these “smart spaces” require sensing, aggregation, analysis, and storage of potentially sensitive information about room occupants. The privacy of the information manipulated by smart spaces quickly becomes a key barrier in realizing the full value of ambient systems and is the focus of this paper. We approach this challenge first by surveying current privacy definitions and mechanisms (access control, fc-anonymity, and differential privacy) under the assumption of ambient sensors and networking found in smart spaces. We then identify how existing approaches are not suitable for smart spaces under major smart space privacy scenarios and propose adaptations with strong potential for addressing these scenarios.

International audience; Detect efficiently the activities of daily living of elderly people at home in order to provide a secure life and to intervene in the necessary time is an important problem we propose here an improved artificial neural network model. As we need an efficient prediction model, we propose a recurrent output neural network model (RO-NN) combined with a genetic algorithm (GA) which surely monitors and predicts the state of the concerned elderly person. Furthermore, we propose a prediction algorithm “Unusual Behavior Algorithm (UBA)” dedicated to detect the unusual activities and hold us account in the dangerous state.

This paper presents an interactive Soapbox platform, which utilizes networked public displays to encourage local citizens to participate in civic engagement. Soapbox is comprised of three subsystems: Soapbox for speaker (SFS), Soapbox for audience (SFA) and Shoutbox. SFS allows people to deliver a speech to the public. SFA enables audience members to watch a speech and give feedback to it. Shoutbox is an input tool that runs on personal devices and is used to author comments and share them with the speaker and other members of the audience. The Soapbox system was evaluated in both the lab and the field. The evaluation demonstrated that users are positive about trying the platform, and can quickly grasp how to use it. Further, participants thought that Soapbox is very appropriate for its purpose, and can effectively attract more citizens to participate in civic engagement.

One of the major concerns of sensor signal analytics is missing or incomplete samples. Data misses occur mainly because of delays or faults in data capturing infrastructure. We propose a novel mechanism for missing data imputation. The novelty of our mechanism arises from its exploitation of the semantics of different features of the sensor signal to estimate the missing samples. Our methods are based on statistical learning and analysis. We demonstrate the efficacy of the proposed method using real data from smart-energy meters.

The increasing number of smart spaces, fostered by the Internet of Things (IoT) and the Web of Things (WoT), as well as the user mobility in these spaces, can lead to smart spaces overlap, where a certain smart object may be used in different smart spaces. This paper presents the use of Model-Driven Engineering techniques to propose: (i) a language to enable the definition of access rules for smart objects and ubiquitous applications; and (ii) an algorithm to process models written in this language, enabling addressing smart objects' concurrent access issues that might arise from smart spaces overlap. The proposal validation was performed by modeling a ubiquitous computing scenario's access rules.

The number of connected devices and services available across the Internet of Things (IoT) is rapidly expanding. In this paper, we present a novel mechanism that improves the ability of mobile clients to dynamically discover potentially unknown IoT services across the IoT's increasingly fragmented protocol landscape. Our approach leverages the detected usage patterns of crowds of mobile users interacting with various IoT services in-situ. We model these usage patterns as a contextual bandit problem that incorporates arbitrarily complex contextual cues, such as device settings, users, environment, activities, etc. We present a modified LinUCB-hybrid algorithm that regulates the exploitation of known services based on contextual cues and makes use of exploration at appropriate times to discover contextually relevant IoT services for mobile users who may not have sufficient prior knowledge. These recommended services can then be used by other middleware platforms, plug-ins or apps to compose new services. We present the overall approach, an operational prototype system and a validation of the system, obtained using two experimental scenarios, which demonstrates promising initial results.

A smart space enhances a networked computing environment by enabling information sharing for digital devices. We consider the emerging case of Internet of Things (IoT) environments, where the role of wireless communication and mobile participation becomes crucial. This paper studies smart spaces created by deploying a Semantic Information Broker (SIB). The latter defines an information hub such that any participant uses it as a knowledge base of the IoT environment. We discuss two directions for SIB deployment options: (i) SIB deployment in dependence on a hosting computing environment and (ii) augmented software deployment with SIB to perform specified knowledge processing. The deployment options are analyzed in respect to such application domains as collaborative work, electronic tourism (e-Tourism), and mobile healthcare (m-Health). Our experimental evaluation study indicates that the deployment options support overcoming the performance issues caused by the wireless and mobile settings of IoT environments.

This paper describes a contemporary concept store, offering a technology-rich blend of entertainment and interactivity targeted to help customers in their shopping experiences while shortening the time they waste queuing.

Application performance in a smart space is affected by faulty behaviours of nodes and communication networks. Detection of faults helps diagnosis of problems and maintenance can be done to restore performance, for example, by replacing or reconfiguring faulty parts. Fault detection methods in the literature are too complex for typical low-resource devices and they do not perform well in detecting intermittent faults. We propose a fully distributed fault detection method that relies on evaluating statements about trustworthiness of aggregated data from neighbors. Given one or more trust statements that describe a fault-free state, the trustor node determines for each observation coming from the trustee whether it is an outlier or not. Several fault types can be explored using different trust statements whose parameters are assessed differently. The trustor subsequently captures the observation history of the trustee node in only two evidence variables using evidence update rules that give more weight to recent observations. The proposed method detects not only permanent faults but also intermittent faults with high accuracy and low false alarm rate.