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It is very hard (or ineffective) to take an old system and add to it security features like plug-ins. Therefore, a computer system is much more reliable designed with the approach of security-by-design. Nowadays, there are several tools, middlewares, and platforms designed with this concept in mind, but they must be appropriately used to guarantee a suitable level of reliability and safety. A security-by-design approach is fundamental when creating a distributed application in the IoT field, composed of sensors, actuators, and cloud services. The IoT usually requires handling different programming languages and technologies in which a developer might not be very expert. Through a use case, we analyzed the security of some IoT components of Amazon Web Services (AWS) from a novice programmer’s point of view. Even if such a platform could be secure by itself, a novice programmer could do something wrong and leave some possible attack points to a malicious user. To this end, we also surveyed a small pool of novice IoT programmers from a consulting engineering company. Even if we discovered that AWS seems quite robust, we noticed that some common security concepts are often not clear or applied, leaving the door open to possible issues.

Several tools have been developed to support novices learning to program. Most of them focus on the code and provide features regarding the visualization of the data structures or the debugging. However, in introductory programming courses, students are typically given exercises in the form of a problem written in natural language; and the first challenge they face is understanding the problem, identifying the relevant information, and then translating that information into code. To our knowledge, little attention has been paid to proposing tools targeted at supporting this problem-solving step, even though it is crucial for deriving a correct solution. In this paper, we present an IDE to encourage novices to understand the problem before start coding, decompose it down into subproblems, explore alternative implementations for each subproblem, and arrange these implementations to build a general solution. Finally, the adopted problem-solving approach is discussed.

In information visualization (InfoVis), the Visualizers (graph designers and creators) have to consider multiple parameters, such as colors and graphic symbols, to obtain a chart that correctly represents a data set. Along with this, visualizers must adequately select the combination of these range of parameters to drive the observers’ attention to the relevant data. When a visualizer drives the attention to relevant aspects of the information, she is providing a starting point to read the graph; this focus point might help the observer to complete the task faster and more efficiently, minimizing distraction from unimportant information. Contemporary tools for InfoVis help visualizers to a certain extent, but most of them currently do not provide insights or suggestions about the modifications needed to drive data attention. This article presents the preliminary results of an exploratory approach to draw the attention to some specific data subset selected by the graph creator, through a systematic variation of some preattentive attributes (i.e., color, texture and orientation). As a first simple method to validate the feasibility of the approach, a set of charts is created from the same source data, with exhaustive variations on preattentive attributes. All generated charts are then automatically evaluated using a salience map algorithm for data analysis images, to identify their focus attention point. After that, the algorithm chooses the chart that best emphasizes the data subset initially specified by the visualizer. To validate our approach, we have implemented a prototype tool, and preliminary results confirm that it is possible to systematically change the attention area of a chart.

End-user programming environments for the IoT such as IFTTT rely on a multitude of low-level trigger-action rules that categorize devices and services by technology or brand. EUPont is a Semantic Web ontology that enables users to meet their needs with fewer, higher-level rules that can be adapted to different contextual situations and as-yet-unknown IoT devices and services.

Internet of Things systems are complex to develop. They are required to exhibit various features and run across several environments. Software developers have to deal with this heterogeneity both when configuring the development and execution environments and when writing the code. Meanwhile, computational notebooks have been gaining prominence due to their capability to consolidate text, executable code, and visualizations in a single document. Although they are mainly used in the field of data science, the characteristics of such notebooks could make them suitable to support the development of IoT systems as well. This work proposes an IoT-tailored literate computing approach in the form of a computational notebook. We present a use case of a typical IoT system involving several interconnected components and describe the implementation of a computational notebook as a tool to support its development. Finally, we point out the opportunities and limitations of this approach.

Nowadays, end users can personalize their smart devices and web applications by defining or reusing IF-THEN rules through dedicated End-User Development (EUD) tools. Despite apparent simplicity, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for example, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques. In this article, we tackle the emerging problem of recommending IF-THEN rules to end users by presenting RecRules , a hybrid and semantic recommendation system. Through a mixed content and collaborative approach, the goal of RecRules is to recommend by functionality : it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tools. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users.

Semantic technologies and Linked Data are increasingly adopted as core application modules, in many knowledge domains and involving various stakeholders: ontology engineers, software architects, doctors, employees, etc. Such a diffusion calls for better access to models and data, which should be direct, mobile, visual and time effective. While a relevant core of research efforts investigated the problem of ontology visualization, discovering different paradigms, layouts, and interaction modalities, a few approaches target mobile devices such as tablets and smartphones. Touch interaction, indeed, has the potential of dramatically improving usability of Linked Data and of semantic-based solutions in real-world applications and mash-ups, by enabling direct and tactile interactions with involved knowledge objects. In this paper, we move a step towards touch-based, mobile interfaces for semantic models by presenting an ontology browsing platform for Android devices. We exploit state of the art touch-based interaction paradigms, e.g., pie menus, pinch-to-zoom, etc., to empower effective ontology browsing. Our research mainly focuses on interactions, yet providing support to different visualization approaches thanks to a clear decoupling between model-level operation and visual representations. Presented results include the design and implementation of a working prototype application, as well as a first validation involving habitual users of semantic technologies. Results show a low learning curve and positive reactions to the proposed paradigms, which are perceived as both innovative and useful.

The spread of voice-driven devices has a positive impact for people with disabilities in smart environments, since such devices allow them to perform a series of daily activities that were difficult or impossible before. As a result, their quality of life and autonomy increase. However, the speech recognition technology employed in such devices becomes limited with people having communication disorders, like dysarthria. People with dysarthria may be unable to control their smart environments, at least with the needed proficiency; this problem may negatively affect the perceived reliability of the entire environment. By exploiting the TORGO database of speech samples pronounced by people with dysarthria, this paper compares the accuracy of the dysarthric speech recognition as achieved by three speech recognition cloud platforms, namely IBM Watson Speech-to-Text, Google Cloud Speech, and Microsoft Azure Bing Speech. Such services, indeed, are used in many virtual assistants deployed in smart environments, such as Google Home. The goal is to investigate whether such cloud platforms are usable to recognize dysarthric speech, and to understand which of them is the most suitable for people with dysarthria. Results suggest that the three platforms have comparable performance in recognizing dysarthric speech and that the accuracy of the recognition is related to the speech intelligibility of the person. Overall, the platforms are limited when the dysarthric speech intelligibility is low (80–90% of word error rate), while they improve up to reach a word error rate of 15–25% for people without abnormality in their speech intelligibility.

The co-existence of various kinds of devices, protocols, architectures, and applications make Internet of Things (IoT) systems complex to develop, even for experienced programmers. When novice programmers are learning to implement these systems, they are required to deal with areas in which they do not have a deep knowledge. Furthermore, besides becoming proficient in these areas separately, they should integrate them and build a system whose components are heterogeneous from both software and hardware perspectives. The accurate understanding of the most challenging issues that novices face is fundamental to envision strategies aimed at easing the development of IoT systems. This paper focuses on identifying such issues in terms of software development tasks that novice programmers encounter when working on IoT systems. To this end, a survey was conducted among 40 novice developers that worked in groups developing IoT systems during several years of a university course. Based on their own experiences, individually and as a group, the most challenging development tasks were identified and prioritized over a common architecture, in terms of difficulty level and efforts. In addition, qualitative data about the causes of these issues was collected and analyzed. Finally, the paper offers critical insights and points out possible future work.

Building energy monitoring and real time control strategies can decrease energy consumption on one hand, and improve comfort on the other hand, by increasing the understanding of the control system. A decision support system for building energy management can be a proper tool for supporting the measurement and management of energy usage and costs of public buildings. The aim of the paper is to describe the architecture of the Decision Support System (DSS), that is being developed within the FP7-Smartcities Project OPTIMUS (OPTimising the energy Use in cities with smart decision support system). The architecture of the system is described considering both the energy related and the information technology aspects. An example of action modeling is also presented and the first results are discussed.

The advent of the 5G network is a key enabler to the growth of IoT, with the promise to innovate and revolutionize contemporary architectures by enabling new IoT-optimized services. Far from being just a bandwidth and latency improvement, the real potential of 5G lies in the intelligent management of network resources, and in the possibility of offering new services at the network level. Developers of IoT applications will no longer be forced to adopt a cloud-centric approach, where all storage and computation is centralized, but will be able to exploit network-provided resources, adopting Edge or Fog computing approaches, with numerous advantages such as higher locality, increased computation power and reliability, reduced latency and power consumption. Network operators, on the other hand, need to offer a compelling set of services while designing the intelligent components of their 5G networks, which would drive IoT developers to prefer their network-hosted services to cloud-based ones managed by over-the-top players. This paper aims at identifying which sets of services may be offered by a 5G network, by analyzing the computing, storage, and communication services that are currently offered by 11 major IoT platform providers, as well as those that are currently not being provided due to limitations of the cloud computing paradigm.

Gaming, both traditional and electronic, is a key activity for children of all ages, enabling them to learn skills, socialize with friends and family, and entertain themselves. Unfortunately, children with disabilities encounter several accessibility barriers that prevent them to participate in mainstream games, unless some adaptations are made to the interfaces. This paper tackles the problem of enabling children with severe motor disabilities to participate in multiplayer games with their peers, thus providing opportunities for socialization and fun inside families or classrooms. We present a collaborative two-player puzzle game, based on several levels of labyrinths that need to be solved by moving the two players' characters. The characteristics of the game (such as the absence of time constraints, and the need of the players to coordinate their moves) were defined in a study group involving computer scientists, psychologists and speech therapists. The game was designed and implemented to be controllable with a single-switch interface, thanks to the GNomon interaction method. A preliminary evaluation has been conducted with 5 couples of able players (mostly children) who enjoyed the game and gave us useful insights.

The definition of intelligent environments (IEs) (Augusto et al. in Hum Comput Inf Sci 3(1):12, 2013) has always been focused around their users, aiming at helping them in a smart and transparent way, and avoiding bothering them or acting against their will. The complexity of IEs, whose technologies range from sensors to machine learning, from distributed architectures to tangible interfaces, from communication protocols to data analysis, challenges researchers from various fields to contribute innovative and effective solutions. In this quest for technical solutions to the myriad requirements of an IEs, user expectations are often left behind, and while researchers tend to focus on niche technical aspects, they risk of losing the big picture of an IE “helping users in their daily life”. This paper analyzes the recent literature of the IEs’ research community, aiming at highlighting to which extent users are taken into account, or are involved, into the reported research works. Evidence shows that, while most papers refer to users in their description, only a small minority actually involve them in the design, testing or experimentation phases.

The co-existence of various kinds of devices, protocols, architectures, and programming languages make Internet of Things (IoT) systems complex to develop, even for experienced programmers. Perforce, Software Engineering challenges are even more difficult to address by novice programmers. Previous research focused on identifying the most challenging issues that novice programmers experience when developing IoT systems. The results suggested that the integration of heterogeneous software components is one of the most painful issues, mainly due to the lack of documentation understandable by inexperienced developers, from both conceptual and technical perspectives. In fact, novice programmers devote a significant effort looking for documentation and code samples willing to understand them conceptually, or in the worst case, at least to make them work. Driven by the research question: "How do the lessons learned by IoT novice programmers can be captured, so they become an asset for other novice developers?", in this paper, we introduce Code Recipes. They consist of summarized and well-defined documentation modules, independent from programming languages or run-time environments, by which non-expert programmers can smoothly become familiar with source code, written by other developers that faced similar issues. Through a use case, we show how Code Recipes are a feasible mechanism to support novice IoT programmers in building their IoT systems.

The design of smart homes, buildings and environments currently suffers from a low maturity of available methodologies and tools. Technologies, devices and protocols strongly bias the design process towards vertical integration, and more flexible solutions based on separation of design concerns are seldom applied. As a result, the current landscape of smart environments is mostly populated by defectively designed solutions where application requirements (e.g., end-user functionality) are too often mixed and intertwined with technical requirements (e.g., managing the network of devices). A mature and effective design process must, instead, rely on a clear separation between the application layer and the underlying enabling technologies, to enable effective design reuse. The role of smart gateways is to enable this separation of concerns and to provide an abstracted view of available automation technology to higher software layers. This paper presents a blueprint for the information technology (IT) architecture of smart buildings that builds on top of established software engineering practices, such as model-driven development and semantic representation, and that avoids many pitfalls inherent in legacy approaches. The paper will also present a representative use case where the approach has been applied and the corresponding modeling and software tools.

With the increase of connected devices and online services, and recently IoT devices, the number of notifications received by every person is growing. The importance of notifications, as they become part of people’s lives, often depends on various factors that can differently influence the reaction and the disruption of recipients. For this reason, the generation and the distribution of notifications has been gaining importance in the design of new applications, services, and smart devices. Nowadays, developers have not yet fully exploited all the advantages that the availability of multiple devices could bring in the customization and the distribution of notifications, e.g., exploiting a cross-device approach. This paper presents XDN (cross device notification), a framework to assist developers in creating cross-device notifications by scripting. The XDN architecture includes: a library to enable developers to design personalized notifications to be distributed among ad-hoc networks of IoT/mobile devices; a GUI to assist developers in implementing and testing (through a simulator) notification strategies; a server-side runtime environment; and an XDN IoT/mobile library for device support. We discuss the requirements for cross device notification systems, and we present the features of the XDN framework, in particular from the point of view of developer advantages, validated through different scenarios.

Programming environments for end-user personalization in the Internet of Things (IoT) are becoming increasingly common. They allow users to define simple IoT applications, i.e., connections between different IoT devices and services. Unfortunately, the adopted representation models are highly technology-dependent, e.g., they often categorize devices and services by manufacturer or brand. Such an approach is not suitable to face the expected growth of the IoT, nor it allows to adapt to yet undiscovered IoT services. In this paper, we present a generic and technology-independent representation for IoT end-user programming environments. The aim of this ``high-level' representation is to allow end-users to create abstract IoT applications that adapt to different contextual situations. We preliminary evaluated the representation by comparing it with the one used by existing programming environments in a user study with 10 participants. Results show that the representation is understandable, and it allows users to create IoT applications more correctly and quickly.

Research activities on healthcare support systems mainly focus on people in their own homes or nurses and doctors in hospitals. A limited amount of research aims at supporting caregivers that work with people with disabilities in assisted living facilities (ALFs). This chapter explores and applies the Internet of Things to the ALF context. In particular, it presents the design, the implementation, and the experimental evaluation of Care4Me, a system supporting the daily activities of assistants. The requirements for designing and implementing Care4Me derive from a literature analysis and from a user study. The solution combines wearable and mobile technologies. With this healthcare support system, caregivers can be automatically alerted of potentially hazardous situations. Furthermore, inhabitants can require assistance instantly and from any point of the facility. The system was evaluated in two ways. The authors performed a functional test with a group of professional caregivers, and deployed the system in an ALF in Italy, collecting the opinions of caregivers and inhabitants.

In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users’ bikes, anti-theft service, information about traveled route, and air pollution monitoring. The proposed SmartBike platform is composed of three main components: the SmartBike mobile sensors for data collection installed on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBike platform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.

This paper presents an exploratory study aimed at identifying the pain points that novice programmers experience, from the software engineering perspective, when developing and deploying smart and distributed systems, that may be classified as Ambient Intelligence (AmI) systems. The exploratory study was conducted among undergraduate students, that worked in groups for developing AmI projects during a university course. Based on their own experiences, individually and as a group, the pain points were identified and prioritized over a common architecture and a set of software development activities. The quantification of the pain points was based on the difficulty level that the students perceived on the development activities and the time they spent completing them. Results represent a starting point for the design of tools and methodologies targeted at overcoming the complexity that novice programmers face when developing AmI systems.

Smart environments SmE are a growing combination of various computing frameworks ubiquitous, pervasive etc., devices, control algorithms, and a complex web of interactions. It is at the core of user facilitation in a number of industrial, domestic, and public areas. On the basis of their application areas, SmE may be critical in terms of correctness, reliability, safety, security, etc. To achieve error-free and requirement-compliant implementation, these systems are designed by resorting to various modeling approaches including ontology and statecharts. This paper attempts to consider correctness, reliability, safety, and security in the design process of SmE and their related components by proposing a design time modeling and formal verification methodology. The proposed methodology covers various design features related to modeling and formal verification of SmE focusing on users, devices, environment, control algorithms, and their interaction against the set of the requirements through model checking. A realistic case study of a Bank Door Security Booth System BDSB is tested. The results show the successful verification of the properties related to the safety, security, and desired reliable behavior of BDSB. Copyright © 2013 John Wiley & Sons, Ltd.

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reported.

Due to the increasing spread of location-aware applications, developers interest in user location estimation has grown in recent years. As users spend the majority of their time in few meaningful places (i.e., groups of near locations that can be considered as a unique place, such as home, school or the workplace), this paper presents a new energy efficient method to estimate user presence in a meaningful place. Specifically, instead of using commonly used but energy hungry methods such as GPS and network positioning techniques, the proposed method applies a Machine Learning algorithm based on Decision Trees, to predict the user presence in a meaningful place by collecting and analyzing: a) user activity, b) information from received notifications (receipt time, generating service, sender-receiver relationship), and c) device status (battery level and ringtone mode). The results demonstrate that, using 20 days of training data and testing the system with data coming from 14 persons, the accuracy (percentage of correct predictions) is 89.40% (standard deviation: 8.27%) with a precision of 89.04% and a recall of 89.40%. Furthermore, the paper analyzes the importance of each considered feature, by comparing the prediction accuracy obtained with different combinations of features.

In the field of Ambient Assisted Living a limited amount of research aims at supporting caregivers that work with people with disabilities in assisted living facilities (ALFs). In fact, research activities on healthcare support systems in AAL mainly focus on improving the quality of life for people in their own homes or supporting nurses and doctors in hospitals. This paper explores and applies the Internet of Things paradigm in the ALFs context. In particular, we present the design, the implementation, and the experimental evaluation of a system capable of supporting the daily activities of healthcare assistants that operate in ALFs for people with physical or cognitive disabilities. The solution combines wearable and mobile technologies to improve assistance requests and anomaly detection. With this healthcare support system, caregivers can be automatically alerted of potentially hazardous situations that happen to the inhabitants while these are out of sight. Furthermore, inhabitants can require assistance instantly and from any point of the facility. We evaluated the system in two ways. We performed a functional test with two professional caregivers, and we deployed the system in an ALF in Italy for 36 hours, collecting the opinions of the involved caregivers and inhabitants.

Children that rely on the use of single switches to access electronic devices, due to physical impairments, can play dynamic video games as their normally developing peers thanks to GNomon: one of the first framework for creating dynamic one-switch video games. However, children with physical impairments that also have cognitive disabilities find additional difficulties in using the current GNomon-based interface. To compensate these difficulties, the "classical" GNomon widget must be rethought and simplified. This paper proposes a parallel between three different widgets specifically designed for GNomon-based interfaces. Each widget employs metaphor in a different way and it has been evaluated with six children with motor and cognitive disabilities. The results are encouraging: a widget, based on the "big and small" concept, is preferred by children and is a candidate to make single-switch games easier to approach.

The increasing popularity and complexity of the Internet of Things (IoT) requires engineers to master various languages and protocols. Interest is also growing in the human factors for transparently and intelligently supporting people with these new IoT systems. New design methodologies are increasingly needed in the toolbox of new electronic and computer engineers, and teaching strategies must allow students to acquire a systems-level view. This article describes the experience of a university undergraduate course in ambient intelligence (AmI) at Politecnico di Torino, Italy. In the course, through a project-based learning approach, students design and prototype an AmI system, and their progress is closely monitored throughout the semester. The article presents the course organization and its main results, including the relationship with industry stakeholders that collaborated in the course delivery.

Buildings account for a relevant fraction of the energy consumed by a country, up to 20-40% of the yearly energy consumption. If only electricity is considered, the fraction is even bigger, reaching around 73% of the total electricity consumption, equally divided into residential and commercial dwellings. Building and Home Automation have a potential to profoundly impact current and future buildings’ energy efficiency by informing users about their current consumption patterns, by suggesting more efficient behaviors, and by pro-actively changing/modifying user actions for reducing the associated energy wastes. In this paper we investigate the capability of an automated home to automatically, and timely, inform users about energy consumption, by harvesting opinions of residential inhabitants on energy feedback interfaces. We report here the results of an on-line survey, involving nearly a thousand participants, about feedback mechanisms suggested by the research community, with the goal of understanding what feedback is felt by home inhabitants easier to understand, more likely to be used, and more effective in promoting behavior changes. Contextually, we also collect and distill users’ attitude towards in-home energy displays and their preferred locations, gathering useful insights on user-driven design of more effective in-home energy displays.

Nowadays, notifications are increasingly gaining momentum in our society. New smart devices and appliances are developed everyday with the ability to generate, send and show messages about their status, acquired data and/or information received from other devices and users. Consequently, the number of notifications received by a user is growing and the tolerance to them could decrease in a short time. This paper presents a smart notification system that uses machine learning algorithms to adequately manage incoming notifications. According to context awareness and user habits, the system decides: a) who should receive an incoming notification; b) what is the best moment to show the notification to the chosen user(s); c) on which device(s) the chosen user(s) should receive the notification; d) which is the best way to notify the incoming notification. After the design of a general architecture, as a first step in building such a system, three different machine learning algorithms were compared in the task of establishing the best device on which the incoming notification should be delivered. The algorithms were applied to a dataset derived from real data provided by the MIT Media Laboratory Reality Mining project, enriched with additional synthetic information.

Intelligent Domotic Environments are a promising approach, based on semantic models and commercially off-the-shelf domotic technologies, to realize new intelligent buildings, but such complexity requires innovative design methodologies and tools for ensuring correctness. Suitable simulation and emulation approaches and tools must be adopted to allow designers to experiment with their ideas and to incrementally verify designed policies in a scenario where the environment is partly emulated and partly composed of real devices. This paper describes a framework, which exploits UML2.0 state diagrams for automatic generation of device simulators from ontology-based descriptions of domotic environments. The DogSim simulator may simulate a complete building automation system in software, or may be integrated in the Dog Gateway, allowing partial simulation of virtual devices alongside with real devices. Experiments on a real home show that the approach is feasible and can easily address both simulation and emulation requirements.

Nowadays home automation, with its increased availability, reliability and with its ever reducing costs is gaining momentum and is starting to become a viable solution for enabling people with disabilities to autonomously interact with their homes and to better communicate with other people. However, especially for people with severe mobility impairments, there is still a lack of tools and interfaces for effective control and interaction with home automation systems, and general-purpose solutions are seldom applicable due to the complexity, asynchronicity, time dependent behavior, and safety concerns typical of the home environment. This paper focuses on user-environment interfaces based on the eye tracking technology, which often is the only viable interaction modality for users as such. We propose an eye-based interface tackling the specific requirements of smart environments, already outlined in a public Recommendation issued by the COGAIN European Network of Excellence. The proposed interface has been implemented as a software prototype based on the ETU universal driver, thus being potentially able to run on a variety of eye trackers, and it is compatible with a wide set of smart home technologies, handled by the Domotic OSGi Gateway. A first interface evaluation, with user testing sessions, has been carried and results show that the interface is quite effective and usable without discomfort by people with almost regular eye movement control.

In the last years Home Automation systems gained new momentum, permeating many human-related environments, from homes to hospitals. The rapid evolution of such systems showed several interoperability pitfalls and a generally insufficient support for advanced user–home interaction. To tackle these emerging issues, recent research works defined the concept of Intelligent Domotic Environments (IDEs) where different automation systems, appliances and devices are integrated into a single powerful environment, capable of providing Ambient Intelligence (AmI) functionalities. IDEs represent one of the first attempts to define a framework for AmI environments based on off-the-shelf domotic systems. This paper contributes to extend IDE capabilities by supporting basic intelligence requirements through a rule-based reasoning mechanism. Starting from a formal model of IDE elements (DogOnt), rules are defined to evaluate environment properties. Property checking is done both off-line, for structural properties, i.e., properties involving the physical structure and configuration of the IDE, and on-line for properties dependent on current IDE states. Two rule languages, SWRL and JenaRules, are considered for rule formalization and their reasoning performance is evaluated by comparing two different rule engines, namely Jess and Jena. Results show that rule-based reasoning can deal with quite complex property checking, effectively addressing basic intelligence for IDEs and providing the basis for more advanced behaviors such as user adaptation and proactive interaction.

A considerable amount of research has been carried out towards enabling average users to customize their smart homes through trigger-action ("if... then...") programming. However, inhabitants of such smart environments keep having problems understanding, administering, troubleshooting, and deriving benefits from the technologies employed in their homes. By synthesizing a broad body of research on end-user programming in smart homes with observations of commercial products and our own experiences, we provide a set of guidelines for designers of future interfaces and tools. Stemming from them, we present the design and the initial evaluation of HomeRules, a mobile and tangible application for end-user programming in smart homes.

The impact of electricity consumption in buildings on the overall energy budget of European and North-American states is steadily growing and requires solutions for achieving a more sustainable development. Smart metering and energy management system can be hardly afforded by residential homes, for their cost and the required granularity. Empowering smart homes with suitable power consumption models allows to estimate, in real-time, the current home consumption on the basis of currently active devices. In this paper we introduce an ontology based power consumption model (PowerOnt) for smart homes, and we discuss some application use cases where the proposed approach may successfully be exploited. Results show that the modeling approach has the potential to support energy efficiency measures into residential homes with few or no metering devices.

Modern smart environments are equipped with a multitude of devices and sensors aimed at intelligent services. The presence of these diverse devices has raised a major problem of managing complex environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the respective smart environments using user defined goals. Generally, the solution advocates that the user goals can be represented by combining devices smart appliances and sensor/actuators in particular states. 'Domotic Effects' is a high level modeling approach, which provides Ambient Intelligence AmI designers and integrators with a high level abstract layer that enables the definition of user goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. This paper describes an approach for the automatic evaluation of domotic effects combined through Boolean expressions, that can provide efficient and intelligent monitoring of the domotic structure of the environment. “Effects Evaluation” addresses the problem of finding the new values of all the domotic effects defined for the environment when one or more devices change their state or one or more sensor value is recorded in the environment, hence determining a new overall state of the environment. The paper also presents an architecture to implement the evaluation of domotic effects. Results obtained from carried out experiments prove the feasibility of the approach and highlight responsiveness of the implemented effect evaluation.

Core War is a game where two or more programs, called warriors, are executed in the same memory area by a time-sharing processor. The final goal of each warrior is to crash the others by overwriting them with illegal instructions. The game was popularized by A. K. Dewdney in his Scientific American column in the mid-1980s. In order to automatically devise strong warriors, /spl mu/GP, a test program generation algorithm, was extended with the ability to assimilate existing code and to detect clones; furthermore, a new selection mechanism for promoting diversity independent from fitness calculations was added. The evolved warriors are the first machine-written programs ever able to become King of the Hill (champion) in all four main international Tiny Hills. This paper shows how playing Core War may help generate effective test programs for validation and test of microprocessors. Tackling a more mundane problem, the described techniques are currently being exploited for the automatic completion and refinement of existing test programs. Preliminary experimental results are reported.

Ever-increasing energy costs and saving requirements, especially in enterprise contexts, are pushing the limits of enterprise resource planning (ERP) to better account for energy, with component-level asset granularity. The authors use an application-oriented approach to explore the aspects involved in designing energy-aware ERPs and describe a prototypical open source implementation based on the Dog Domotic Gateway and the Oratio ERP.

The emergence of economically viable and efficient sensor technology provided impetus to the development of smart devices (or appliances). Modern smart environments are equipped with a multitude of smart devices and sensors, aimed at delivering intelligent services to the users of smart environments. The presence of these diverse smart devices has raised a major problem of managing environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the environments using user defined goals. ‘domotic effects’ is a user goal modeling framework, which provides ambient intelligence (AmI) designers and integrators with an abstract layer that enables the definition of generic goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. The high-level nature of domotic effects also allows the residents to program their personal space as they see fit: they can define different achievement criteria for a particular generic goal, e.g., by defining a combination of devices having some particular states, by using domain-specific custom operators. This paper describes an approach for the automatic enforcement of domotic effects in case of the Boolean application domain, suitable for intelligent monitoring and control in domotic environments. Effect enforcement is the ability to determine device configurations that can achieve a set of generic goals (domotic effects). The paper also presents an architecture to implement the enforcement of Boolean domotic effects, and results obtained from carried out experiments prove the feasibility of the proposed approach and highlight the responsiveness of the implemented effect enforcement architecture.

This paper aims at broadening the scope of hierarchical ATPG to the behavioral-level. The main problem is identified, namely the mismatch of timing models between the behavioral- and gate-levels. As a main contribution of this paper, a theoretical analysis of this problem led to the definition of a novel concept, that of dominated patterns, that captures the needed link between the levels. Some metrics are defined, taken from the software realm, that allow generation of test patterns at the behavioral-level. To validate the concept correctness, different ATPG systems are presented, and experimental results show an improvement in the test quality, thanks to the exploitation of behavioral-level information.

Politecnico di Torino has been actively experimenting distance education scenarios since 1992, through the development of innovative methodologies and tools. The real challenge today, however, is to move from small settings to a large-scale system able to suit the needs of a broad number of users belonging to different categories, from traditional students to part-time or full-time workers, from students living far from Torino to people with participation restriction due to disability. The emphasis then, is not only on the innovation of methodologies and technologies, but on their effective and economically sustainable use in a complex and multi-faceted setting. This chapter describes the services introduced in this direction and gives a preliminary evaluation after the first year of delivery.

This paper addresses human-home interaction mediated by everyday objects, with a particular focus on wrist watches. Everyday wrist-worn devices are turned into flexible home access points by exploiting a modular architecture independent from the underlying home automation system, and from the specific watch device, provided that the necessary capabilities are available. A first working prototype based on a cost-effective consumer watch is presented, and experimental results confirm the viability of the approach.

Circular self test path (CSTP) is an attractive method for automatically transforming sequential circuits generated by automatic synthesis tools into BIST structures. The authors extend this method-making it more suitable for FSMs derived from synthesized control parts-and are integrating it into an industrial design flow supporting testable synthesis. The CSTP approach provides good results in terms of test length and fault coverage in large circuits. It requires substitution of all or some of the flip-flops in the circuit with special cells and their connection to constitute a circular chain. CSTP also has application in industrial environments, and several commercial CAE environments, such as that used by AT&T, now support CSTP as an approach for automatic introduction of BIST in circuits.

Ambient Intelligence and Smart Home Automation systems are currently emerging as feasible and ready to exploit solutions to support more intelligent features inside future and current homes. Thanks to increased availability of off-the-shelf components and to relatively easy to implement solutions we are experiencing a steady evolution of households, causing an ever-increasing users' awareness of the capabilities of such innovative environments. To foster effective adoption of Smart Home Automation technologies in our home environments, traditional architectural and plant design must be complemented by sound design methodologies and tools, supporting the whole environment design cycle, including for example modeling, simulation and emulation, as well as, when feasible, formal model-checking and verification. Several research efforts have already addressed the design of expressive modeling tools, mostly based on Semantic Web technologies, as well as of suitable platforms for adding interoperation and rule-based intelligence to home environments. This paper proposes a new modeling methodology designed to fit the different phases of Intelligent Environments design, with a particular focus on validation and verification of the whole system. Carefully designed separation of modeled entities permits to exploit the DoMAIns framework during all phases of the environment design, from early abstract conception to the final in-field deployment. The DoMAIns design methodology is applied to a sample use case that involves comprehensive modeling and simulation of a Bank Security Booth, including the environment, the control algorithms, the automation devices and the user. Results show that the approach is feasible and that can easily handle different types of environment modeling, required in the different design phases, and for each of them it may support simulation, emulation, or other verification techniques.

Design and development of increasingly complex intelligent environments require rich design flows that include strong validation and verification methodologies. Formal verification techniques are often advocated, and they require formally described models of the smart home devices, their interconnections, and their controlling algorithms. Complete verification can only be achieved if all used models are verified, including individual device models. This paper proposes an approach to formally verify the correctness of device models described as UML State Charts, by checking their consistency with respect to the properties, declared in an Ontology, for the categories to which each device belongs. The paper describes the verification methodology and presents some first verification results.

In the 21st century, all the major countries around the world are coming together to reduce the impact of energy generation and consumption on the global environment. Energy conservation and its efficient usage has become a top agenda on the desks of many governments. In the last decade, the drive to make homes automated and to deliver a better assisted living picked pace and the research into home automation systems accelerated, usually based on a centralized residential gateway. However most devised solutions fail to provide users with information about power consumption of different house appliances. The ability to collect power consumption information can lead us to have a more energy efficient society. The goal addressed in this paper is to enable residential gateways to provide the energy consumption information, in a machine understandable format, to support third party applications and services. To reach this goal, we propose a Semantic Energy Information Publishing Framework. The proposed framework publishes, for different appliances in the house, their power consumption information and other properties, in a machine understandable format. Appliance properties are exposed according to the existing semantic modeling supported by residential gateways, while instantaneous power consumption is modeled through a new modular Energy Profile ontology.

Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deployment.

In the domain of rule-based automation and intelligence most efforts concentrate on building the technological infrastructure, often disregarding user-home interaction requirements. This paper attempts to mitigate this issue by defining a rich-web rule visual design interface specifically aimed at non-skilled home inhabitants.

Ambient Intelligence systems integrate domotic devices and advanced control and intelligent algorithms, thus leading to integrate systems with a high degree of complexity in their behavior. Ensuring the correctness of the design of such system is therefore essential, and this paper proposed a methodology, based on formal modeling and verification techniques, to verify logic and temporal properties of an intelligent ambient. The approach is integrated with the Dog domotic gateway, and automatic translation tools ensure the correctness of the verified model while adding no additional task for system designers.