Internet of Things-enabled home automation is starting to significantly transform our daily lives. Users must be able to configure and coordinate the connected objects in their dwellings to personalise and fully benefit from their potentialities. Trigger-action programming is one relevant approach to enable users to create useful automations. However, current approaches mainly based on visual Web or mobile interfaces have limitations, such as the difficulty in finding and selecting the correct object and associated services. Mobile augmented reality is an interaction modality that can support more direct and usable automation control. In this perspective, the support of a recommendation system can facilitate users in creating personalised automations. This paper presents a system for generating personalised daily automations recommendations and presenting them in a mobile augmented reality solution in order to facilitate their monitoring and creation. Seven classification approaches were assessed on different datasets to determine their ability to provide personalised and context-aware recommendations. We also report a user study (N = 16) showing that the personalised recommendation system improves user performance when creating automations in a trigger action format with a mobile augmented reality editing environment.

Automations can help support the achievement of various types of goals in a smart home. We present an architecture enabling the possibility of supporting user goals consistent with the principles of a circular economy (such as energy saving and waste recycling). It includes meta-design tools for creating automations that better fit user needs and controlling their execution through innovative modalities based on conversational agents and augmented reality, and a home Digital Twin useful for supporting relevant simulations and analytics to understand the potential impact of specific automations on aspects relevant for circular economy.

Automations in the context of smart homes have been adopted more and more frequently; thus, users should be able to control them and create automations most suitable to their needs. Current solutions for this purpose are based on visual apps with conceptual representations of possible automation elements. However, they tend to be static, abstract, and detached from the user's real context. In this paper, we propose a novel solution based on mobile augmented reality, which provides situated, dynamic representations associated with the physical objects available in the current users' context while they are freely moving about. It allows direct interaction with the objects of interest, monitoring nearby objects' automations while moving, and creating new automations or modifying existing ones. It also supports users with recommendations of object and service configurations relevant to complete the editing of the new automations. The paper also reports on a user test, which provided positive feedback.

Nowadays, when dealing with Internet of Things (IoT) for people with no prior experience in programming or in designing technology, End-User Development (EUD) solutions offer wide and powerful approaches to support end-users in designing their own IoT smart things and systems. The main goal of this edition of the workshop is to encourage and stimulate a wealthy confrontation on heterogeneous topics related to EUD for IoT applications that exploits different interaction paradigms and innovative interface design. The outcome of the workshop are thought-provoking contributions that range from accessibility and security for IoT systems and devices up to personalization of smart objects.

Given the growing adoption of Internet of Things (IoT) technologies, several approaches have been presented to enable people to increase their control over their smart devices and provide relevant support. Recommendation systems have been proposed in many domains, but have received limited attention in the area of End-User Development (EUD). We propose a novel approach for formulating recommendations in this area, based on deconstructing trigger-action rules into sequences of elements and the links between them. For this purpose, we propose a solution inspired by methods aimed at addressing the sequence-prediction problem. We have used this approach to provide users with two different types of recommendations: full rules for the one being edited, and parts of rules relevant for the next step to take in order to complete the current rule editing. In this paper, we present the design and a first evaluation of the two different possibilities to generate and display recommendations in a block-based EUD environment for creating automations for IoT contexts.

Personalization rules based on the trigger-action paradigm have recently garnered increasing interest in Internet of Things (IoT) applications. However, composing trigger-action rules can be a challenging task for end users, especially when the rules' complexity increases. Users have to decide about various aspects: which triggers and actions to select, how to combine multiple triggers or actions, and whether some previously defined rule can help in the composition process. We propose a visual environment, Block Rule Composer, to address these problems. It consists of a tool for creating rules based on visual blocks, integrated with recommendation techniques in order to provide intelligent support during rule creation. We also report on a first test which provided positive indications and suggestions for further design improvements.