The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application.

As the use of AI systems continues to increase, so do concerns over their lack of fairness, legitimacy and accountability. Such harmful automated decision-making can be guarded against by ensuring AI systems are contestable by design: responsive to human intervention throughout the system lifecycle. Contestable AI by design is a small but growing field of research. However, most available knowledge requires a significant amount of translation to be applicable in practice. A proven way of conveying intermediate-level, generative design knowledge is in the form of frameworks. In this article we use qualitative-interpretative methods and visual mapping techniques to extract from the literature sociotechnical features and practices that contribute to contestable AI, and synthesize these into a design framework.

This paper focuses on how the Internet of Things (IoT) could contribute to the transition to a circular economy (CE), through supporting circular business model and design strategies. While literature has highlighted the opportunities for IoT to support circular strategies in business, little has been published about actual implementations in practice. The aim of this study was therefore to understand how companies to date have implemented IoT for circular strategies, and how these implementations compare to the range of opportunities described in literature. To that end, a two-step approach was followed. Firstly, building on academic literature, a framework was developed which categorizes different IoT-enabled circular strategies. The framework recognizes tracking, monitoring, control, optimization, and design evolution as IoT capabilities. Efficiency in use, increased utilization, and product lifetime extension are distinguished as circular in-use strategies, while reuse, remanufacturing, and recycling are distinguished as circular looping strategies. The framework complements previously published work, as it adds additional detail to the categorization, and allows for easy mapping of diverse cases. Secondly, 40 cases from practice were analyzed and mapped to the framework. This way, practice-based insights were derived about the current distribution of IoT-enabled circular strategies implemented in practice. The results show that current implementation of IoT-enabled circular strategies mainly supports two strategies in the use phase: efficiency in use and product lifetime extension. Only a small number of the reviewed cases display IoT-enabled looping (reuse, remanufacturing, and recycling). Similarly, few cases describe ‘design evolution’ for CE, i.e., the feedback of data from products in use to support circular design. Based on these results, this study identifies the need for future research to further investigate why IoT-enabled looping strategies and design evolution for circular strategies have not been implemented to scale.

The Open University's My Digital Life course offers a learning infrastructure that allows complete novices to experiment with, and learn about, Internet-of-Things technologies by engaging in a range of activities that include collaborative and collective programming of real-world sensing applications. The first Web extra at http://youtu.be/JUpkHqivXp4 is a video presentation in which Arosha Bandara, a lecturer in computing at the Open University, explains how embedded, interconnected devices have grown to affect our lives. The second Web extra at http://youtu.be/VL37sjj_I1sis a video demonstration of Sense from the Open University's My Digital Life course. Sense is a newly developed visual programming language and programming environment based on SenseBoard, an embedded networked sensor device that supports active learning and experimentation with sensing, actuation, and networking by students at home.

A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.

If a mobile computing device knows how it is positioned and oriented in relation to other devices nearby, then it can provide enhanced support for multi-device and multi-user interactions. Existing systems that provide position information to mobile computers are reliant on externally deployed infrastructure, such as beacons or sensors in the environment. We introduce the Relate system, which provides fine-grained relative position information to co-located devices on the basis of peer-to-peer sensing, thus overcoming dependence on any external infrastructure. The system is realised as a hardware/software plug-in, using ultrasound for peer-to-peer sensing, USB to interface with standard mobile devices, and data abstraction and inferencing to map sensor data to a spatial model that maintains both quantitative and qualitative relationships. We present a set of services and applications to demonstrate the utility of the system. We report experimental results on the accuracy of the relative position and orientation estimates, and other aspects of system performance