Abstract--Explainability is increasingly seen as an essential feature of rule-based smart environments. While counterfactual explanations, which describe what could have been done differently to achieve a desired outcome, are a powerful tool in eXplainable AI (XAI), no established methods exist for generating them in these rule-based domains. In this paper, we present the first formalization and implementation of counterfactual explanations tailored to this domain. It is implemented as a plugin that extends an existing explanation engine for smart environments. We conducted a user study (N=17) to evaluate our generated counterfactuals against traditional causal explanations. The results show that user preference is highly contextual: causal explanations are favored for their linguistic simplicity and in time-pressured situations, while counterfactuals are preferred for their actionable content, particularly when a user wants to resolve a problem. Our work contributes a practical framework for a new type of explanation in smart environments and provides empirical evidence to guide the choice of when each explanation type is most effective. Smart environments, such as smart homes, offices, and buildings, integrate sensor-enabled devices to support users in decision-making, monitoring, and managing abnormal situations [1], [2]. The rapid adoption of these environments is fueled by advances in the Internet of Things (IoT) and Artificial Intelligence (AI), decreasing device costs, and improved system integration [3]-[5]. Rule-based systems are a prevalent approach for implementing automation in smart environments, by executing predefined rules when certain conditions are met [6], [7].

This work presents a novel architecture for context-aware interactions within smart environments, leveraging Large Language Models (LLMs) to enhance user experiences. Our system integrates user location data obtained through UWB tags and sensor-equipped smart homes with real-time human activity recognition (HAR) to provide a comprehensive understanding of user context. This contextual information is then fed to an LLM-powered chatbot, enabling it to generate personalised interactions and recommendations based on the user's current activity and environment. This approach moves beyond traditional static chatbot interactions by dynamically adapting to the user's real-time situation. A case study conducted from a real-world dataset demonstrates the feasibility and effectiveness of our proposed architecture, showcasing its potential to create more intuitive and helpful interactions within smart homes. The results highlight the significant benefits of integrating LLM with real-time activity and location data to deliver personalised and contextually relevant user experiences.

Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance space functionality, optimize energy utilization, and improve user comfort and well-being. The integration of emerging AI methodologies into these environments facilitates the formation of AI-driven smart spaces, which further enhance functionalities of the spaces by enabling advanced applications such as personalized comfort settings, interactive living spaces, and automatization of the space systems, all resulting in enhanced indoor experiences of the users. In this paper, we present a systematic survey of existing research on the foundational components of AI-driven smart spaces, including sensor technologies, data communication protocols, sensor network management and maintenance strategies, as well as the data collection, processing and analytics. Given the pivotal role of AI in establishing AI-powered smart spaces, we explore the opportunities and challenges associated with traditional machine learning (ML) approaches, such as deep learning (DL), and emerging methodologies including large language models (LLMs). Finally, we provide key insights necessary for the development of AI-driven smart spaces, propose future research directions, and sheds light on the path forward.

Robot vacuum cleaners have become increasingly popular and are widely used in various smart environments. To improve user convenience, manufacturers also introduced smartphone applications that enable users to customize cleaning settings or access information about their robot vacuum cleaners. While this integration enhances the interaction between users and their robot vacuum cleaners, it results in potential privacy concerns because users' personal information may be exposed. To address these concerns, end-to-end encryption is implemented between the application, cloud service, and robot vacuum cleaners to secure the exchanged information. Nevertheless, network header metadata remains unencrypted and it is still vulnerable to network eavesdropping. In this paper, we investigate the potential risk of private information exposure through such metadata. A popular robot vacuum cleaner was deployed in a real smart environment where passive network eavesdropping was conducted during several selected cleaning events. Our extensive analysis, based on Association Rule Learning, demonstrates that it is feasible to identify certain events using only the captured Internet traffic metadata, thereby potentially exposing private user information and raising privacy concerns.

This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments. Through AI agents that accompany users, Follow-Me AI negotiates data management based on user consent, aligns environmental controls as well as user communication and computes resources available in the environment with user preferences, and predicts user behavior to proactively adjust the smart environment. The manuscript illustrates this concept with a detailed example of Follow-Me AI in a smart campus setting, detailing the interactions with the building's management system for optimal comfort and efficiency. Finally, this article looks into the challenges and opportunities related to Follow-Me AI.

In the presence of interaction conflicts, user trust in automation plays an important role in accepting intelligent environments such as smart homes. In this paper, a factorial research design is employed to investigate and compare the single and joint effects of Level of Automation (LoA), Frequency of Automated responses (FoA), and Conflict Intensity (CI) on human trust and acceptance of automation in the context of smart homes. To study these effects, we conducted web-based experiments to gather data from 324 online participants who experienced the system through a 3D simulation of a smart home. The findings show that the level and frequency of automation had an impact on user trust in smart environments. Furthermore, the results demonstrate that the users' acceptance of automated smart environments decreased in the presence of automation failures and interaction conflicts.

Smart environments powered by the Internet of Things aim at improving our daily lives by automatically tuning ambient parameters (e.g. temperature, interior light) and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not consider mediating conflicting goals among different users and/or system administrators, and feature limited compatibility across different IoT verticals. In this article, we propose a declarative framework to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals encompassing multiple IoT systems. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples.

Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings and have access to online contextual information. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. Hence, IoT applications must be equipped with self-adaptation techniques to manage their run-time uncertainties locally or in cooperation with each other. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in human-centered environments. In this paper, we have considered the Smart Home as the case study of smart environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home'In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments.

Machine vision coupled with artificial intelligence (AI) has made great strides toward letting computers understand images. Thanks to deep learning, which processes information in a way analogous to the human brain, machine vision is doing everything from keeping self-driving cars on the right track to improving cancer diagnosis by examining biopsy slides or x-ray images. Now some researchers are going beyond what the human eye or a camera lens can see, using machine learning to watch what people are doing on the other side of a wall. The technique relies on low-power radio frequency (RF) signals, which reflect off living tissue and metal but pass easily through wooden or plaster interior walls. AI can decipher those signals, not only to detect the presence of people, but also to see how they are moving, and even to predict the activity they are engaged in, from talking on a phone to brushing their teeth.

Make the world work for 100% of humanity, in the shortest possible time, through spontaneous cooperation without ecological offense or disadvantage of anyone. As a design leader, keeping one foot in the present and the other an optimistic future is all in a typical day. However, a little over a year ago, the universe decided to kick one of those feet out from under me and present me with an opportunity to experience life with a physical impairment. For the next three months, that injury left me wheelchair bound and gave me an opportunity to pivot my design from adoption, conversion and retention of users to accessibility and the rapid shift to Inclusive Design. According to the CDC, 1 in 4 US adults live with a disability.