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Background: People have various and changing needs as they age and the number of people living with some form of dementia is steadily increasing. Smart homes have a unique potential to provide assisted living but are often designed rigidly with a specific and fixed problem in mind. Objectives: To make smart-ready homes and communities that can be adaptively and easily updated over time to support varying user needs and to deliver the needed assistance, empowerment and living independence. Method: The design and deployment of programmable assistive environment for older adults. Results: The use of platform technology (a special form of what is known today as the Internet of Things, or IoT) has enabled the decoupling of goal setting and application development from sensing and assistive technology deployment and insertion in the assistive environment. Personalising a smart home or changing its applications and its interfaces dynamically as the user needs change was possible and has been demonstrated successfully in one house - the Gator Tech Smart House. Scaling up the platform technology approach to a planned living community is underway at one of UK’s National Health Services (NHS) Healthy New Town projects. Conclusions: There is a great need to integrate technology with living spaces to provide assistance and independent living. But to smarten these spaces for lifelong living, the technology and the smart home applications must be flexible, adaptive and changeable over time. However, people do not just live at home, they live in communities. Looking at the big picture (communities), as well as the small (homes), we consider how to progress beyond smart-ready homes towards smart-ready communities.

With individual behaviour and lifestyle determining 30-50% of people’s health, research and supportive technology for affecting behaviour alteration remain urgently needed. Most existing persuasive systems are designed to persuade a user to change a finite set of behaviours to achieve a specific goal. However, if the user’s situation or goal changes, such systems cannot adapt to the changes. A much more robust type of persuasive systems is needed today to enable adequate health navigation and to empower people to face and change their own realities in terms of a large variety of health behaviours and lifestyles. In this paper, we provide a perspective on the impressive body of work contributed over the past 15 years, to better look into the future of persuasive health and to the opportunities a broader theoretical framework and practical methodologies may bring about. We present a taxonomy that attempts to explain the contributions in this field including health behaviour theory, cybernetic action behaviour models, social cognitive theory, and control theory. We identify potentially promising approaches to advance persuasive health’s efficacy in empowering individuals to improve their own health outcomes.

With individual behaviour and lifestyle determining 30-50% of people’s health, research and supportive technology for affecting behaviour alteration remain urgently needed. Most existing persuasive systems are designed to persuade a user to change a finite set of behaviours to achieve a specific goal. However, if the user’s situation or goal changes, such systems cannot adapt to the changes. A much more robust type of persuasive systems is needed today to enable adequate health navigation and to empower people to face and change their own realities in terms of a large variety of health behaviours and lifestyles. In this paper, we provide a perspective on the impressive body of work contributed over the past 15 years, to better look into the future of persuasive health and to the opportunities a broader theoretical framework and practical methodologies may bring about. We present a taxonomy that attempts to explain the contributions in this field including health behaviour theory, cybernetic action behaviour models, social cognitive theory, and control theory. We identify potentially promising approaches to advance persuasive health’s efficacy in empowering individuals to improve their own health outcomes.

We argue that the success of the Internet of Things (IoT) vision will greatly depend on how its main ingredient—the “thing”—is architected and prepared to engage. The IoT’s fragmented and wide-varying nature introduces the need for additional effort to homogenize these things so they may blend together with the surrounding space to create opportunities for powerful and unprecedented IoT applications. We introduce the IoT Device Description Language (IoT-DDL), a machine- and human-readable descriptive language for things, seeking to achieve such integration and homogenization. IoT-DDL explicitly tools things to self-discover and securely share their own capabilities, entities, and services, including the various cloudbased accessories that may be attached to them. We also present the Atlas thing architecture—a lightweight architecture for things that fully exploits IoT-DDL and its specifications. Our architecture provides new OS layers, services, and capabilities we believe a thing must have in order to be prepared to engage in IoT scenarios and applications. The architecture and IoT-DDL enable things to generate their offered services and self-formulate APIs for such services, on the fly, at power-on or whenever a thing description changes. The architecture takes advantage of widely used device management, micro-services, security, and communication standards and protocols. We present details of IoT-DDL and corresponding parts of the thing architecture. We demonstrate some features of IoT-DDL and the architecture through proof-of-concept implementations. Finally, we present a benchmarking study to measure and assess time performance and energy consumption characteristics of our architecture and IoT-DDL on real hardware platforms.

For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the incentive paid for each specific sensing task, an extra compensation is paid to each participant if s/he is assigned with more than one task type. Additionally, based on the prediction of the participants' mobility pattern, MTPS adopts an iterative greedy process to achieve a near-optimal allocation solution. Extensive evaluation based on real-world mobility data shows that our approach outperforms the baseline methods, and theoretical analysis proves that it has a good approximation bound.

Recent advances in the Internet of Things (IoT) and pervasive and ubiquitous computing provide a glimpse into the future of our planet and reveal exciting visions of many smart things: smart cities, smart homes, smart cars, and other smart spaces such as malls, workplaces, hotels, schools, and much more. Driven by a technological revolution offering “low-power many things and wireless almost everything,” we could, in only a decade, envision and prototype impressive smart space systems that improve quality of life, enhance awareness of resources and the environment, and enrich users' experience. However, prototyping is one thing and actual large-scale deployments are another. The massive scale of sensors and devices that will be deployed in smart cities of the future will be challenging. Without an ecosystem and a scalable architecture in place, it will be extremely difficult to manage or program such an expanding and massive IoT. In this paper, we introduce the Cloud-Edge-Beneath (CEB) architecture and present its salient scalability features. We also present a validation study based on an event-driven programming model demonstrating CEB's scaling behavior in face of IoT expansion and under dynamically increasing loads.