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The ageing of populations is increasing pressure on health and social care systems. Potentially, assistive technologies are a way to support the independence of older adults in their daily activities. Among existing assistive technologies, ambient sensing technologies have received less attention than wearable systems. Moreover, there has been little research into cheaper technologies capable of using multiple modalities. A systematic review of the acceptability of assisted living or smart homes in the United Kingdom and the simultaneous use of sounds and vibrations in remote monitoring of assisted living or smart homes will inform and encourage the use of digital monitoring technologies. The acceptability of sensing technologies depends on whether there is any social stigma about their use, for example, the extent to which they invade privacy. The United Kingdom studies reviewed suggest a lack of measurements of the perceived efficacy or effectiveness of the monitoring devices. The primary use of vibration or acoustic technologies has been for detecting falls rather than monitoring health. The review findings suggest the need for further exploration of the acceptability and applicability of remote monitoring technologies, as well as a need for more research into the simultaneous use of sounds and vibrations in health monitoring. [ABSTRACT FROM AUTHOR]

With the Internet of Things (IoT) becoming increasingly prevalent in people's homes, new threats to residents are emerging such as the cyber-physical attack, i.e. a cyber-attack with physical consequences. In this study, we aimed to gain insights into how people experience and respond to cyber-physical attacks to their IoT devices. We conducted a naturalistic field experiment and provided 9 Dutch and 7 UK households, totalling 18 and 13 participants respectively, with a number of smart devices for use in their home. After a period of adaptation, simulated attacks were conducted, leading to events of varying noticeability (e.g., the light going on or off once or several times). After informing people simulated attacks had occurred, the attacks were repeated one more time. User experiences were collected through interviews and analysed with thematic analyses. Four relevant themes were identified, namely (1) the awareness of and concern about privacy and security risks was rather low, (2) the simulated attacks made little impression on the participants, (3) the participants had difficulties with correctly recognizing simulated attacks, and (4) when informed about simulated attacks taking place; participants noticed more simulated attacks and presented decision rules for them (but still were not able to identify and distinguish them well—see Theme 3). The findings emphasise the need for training interventions and an intrusion detection system to increase detection of cyber-physical attacks. [ABSTRACT FROM AUTHOR]

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours. [ABSTRACT FROM AUTHOR]

Nowadays, an increasing amount of attention is being devoted towards passive and non-intrusive sensing methods. The prime example is healthcare applications, where on-body sensors are not always an option or in other applications which require the detection and tracking of unauthorized (non-cooperative) targets within a given environment. Therefore, in this paper we present a dataset consisting of measurements obtained from Radio-Frequency (RF) devices. Essentially, the dataset consists of Ultra-Wideband (UWB) data in the form of Channel Impulse Response (CIR), acquired via a Commercial Off-the-Shelf (COTS) UWB equipment. Approximately 1.6 hours of annotated measurements are provided, which are collected in a residential environment. This dataset can be used to passively track a target's location in an indoor environment. Additionally, it can also be used to advance UWB-based Human Activity Recognition (HAR) since three basic human activities were recorded, namely, sitting, standing and walking. We anticipate that such datasets may be utilized to develop novel algorithms and methodologies for healthcare, smart homes and security applications. Measurement(s) Human physical activity • Human location Technology Type(s) Ultra-wideband impulse radar Factor Type(s) Human location • Human physical activity • Monitoring environment • Participant demographics • Radio-frequency signals Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment residential building Sample Characteristic - Location United Kingdom [ABSTRACT FROM AUTHOR]

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Preferences are fundamental in decision-making, so understanding preference management is key in developing systems that guide the choices of the users. These choices can be decided through argument(s) which are known to have various strengths, as one argument can rely on more certain or vital information than the other. We explored argumentation technique from a previous study, and validated its potentials by applying to it several real-life scenarios. The exploration demonstrates the usefulness of argumentation in handling conflicting preferences and inconsistencies, and provides effective ways to manage, reason and represents users' preferences. Using argumentation, we provide a practical implementation of a system to manage conflicting situations, and a simple interface that aids the flow of preferences from users to the system. We illustrated using the interface, how the changes in users' preferences can effect system output in a smart home. This article describes the functionalities of the implemented system, and illustrates the functions by solving some of the complexities in users' preferences in a real smart home. The system detects potential conflicts, and tries solve them using a redefined precedence order among some preference criteria. We also show how our system is capable of interacting with external sources data. The system was used to access and use live data of a UK supermarket chain store, through their application programming interface (API) and provide users suggestions on their eating habits, based on their set preference(s). The system was used to filter-specific products from the live data, and check the product description, before advising the user accordingly. [ABSTRACT FROM AUTHOR]

The need to accelerate the decarbonization of heating, as well as the rise of the 'smart home', mean that there is an increasing focus on the role of innovative consumer offerings in driving the shift to zero carbon domestic heating. In this context, Heat as a Service (HaaS) business models, which provide consumers with an agreed heating plan rather than simply paying for units of fuel, are receiving increased attention. This paper explores HaaS based on insights from facilitated group discussions with key stakeholders, and learning from HaaS trials, in the United Kingdom. Results identified evidence needs and research gaps related to: addressing issues of trust between consumers and suppliers, supportive policies, financing business models, and openness and interoperability of technology and data. Based on the findings, we propose policy and research recommendations to better understand the role of HaaS business models in decarbonization. [ABSTRACT FROM AUTHOR]

• Smart home technologies offer great potential to lower end-user CO 2 emissions. • Survey conducted on adopters and non-adopters of smart home technologies. • Social network characteristics and communication behaviours investigated. • Potential social barriers to mass market diffusion identified. Consumer-facing digital innovations with the potential to reduce carbon emissions often exist in small market niches and their impact has been limited thus far. Using the established Diffusion of Innovations theory which considers interpersonal communication amongst social networks to be a vital mechanism for exchanging information, we conducted an online survey in the UK to investigate the social networks and communication behaviours of adopters and non-adopters of three different energy saving smart home technologies. Applying social network analysis and statistically testing hypotheses, our results reveal the potential social barriers to the diffusion of information, with social network structure and characteristics creating obstacles. This research provides necessary insights into real early adopters, confirms the importance of focussing research on the often-neglected social elements of diffusion theory and helps identify marketing strategies and policy actions using social mechanisms to accelerate a low carbon transition. [ABSTRACT FROM AUTHOR]

Businesses in the smart home sector are actively promoting the benefits of smart home technologies for consumers, such as convenience, economy and home security. To better understand meanings of and trust in the smart home, we carried out a nationally representative survey of UK consumers designed to measure adoption and acceptability, focusing on awareness, ownership, experience, trust, satisfaction and intention to use. We analysed the results using theories of meanings and acceptability of technologies including semiotics, social construction of technology (SCOT) and sociotechnical affordance. Our findings suggest that the meaning and value proposition of the smart home have not yet achieved closure for consumers, but is already foregrounding risks to privacy and security amongst the other meaning-making possibilities it could afford. Anxiety about the likelihood of a security incident emerges as a prominent factor influencing adoption of smart home technology. This factor negatively impacts adoption. These findings underline how businesses and policymakers will need to work together to act on the sociotechnical affordances of smart home technology in order to increase consumers' trust. This intervention is necessary if barriers to adoption and acceptability of the smart home are to be addressed now and in the future. [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The digital revolution has the potential to transform many aspects of our daily lives, including how we work and how we interact with others. While the services, benefits, and risks of smart home technologies (SHTs) have been vastly explored, this research asks: is there any space for SHTs to create conflicts within homes and their inhabitants? Drawing from a rich empirical dataset consisting of a literature review, semi-structured research interviews with experts, three focus groups across the UK (Manchester, London, and Surrey), and a nationally representative survey (n = 1032), this study investigates how SHTs can enable conflicts with couples and partners, children and parents, landlords and tenants, neighbors and hosts and guests. • Explores users' perceptions on smart home technologies. • Examines how digital technologies can enable conflicts within households. • Explores conflict through a nationally representative survey in the United Kingdom as well as three focus groups (n = 18 respondents). [ABSTRACT FROM AUTHOR]

• Very few households around the world have adopted smart or low-carbon heating systems. • Examines the heating preferences and profiles of 100 smart heating homes in the UK. • Tradeoffs between comfort, value and cost occur when it comes to heating. • People want different numbers of warm hours in their homes at very different times. • Many non-monetary social preferences and practices shape heat choices and patterns. Transitioning to more efficient and less carbon-intensive heating is a monumental policy challenge in the United Kingdom. However, very few households in the UK—and perhaps even elsewhere—have actual experience with state-of-the-art smart heating systems that may utilize enhanced control or feedback. Drawing from a unique sample of actual adopters of smart heating, this study closely examines the heating preferences, practices, and profiles of homes when they are given smarter heating systems. The study utilizes qualitative household data from the Energy System Catapult's Living Laboratory of 100 smart homes in Birmingham (West Midlands), Bridgend (Wales), Manchester (Greater Manchester), and Newcastle (Northumberland). We examine the heating preferences and profiles of participants, with findings inductively organized around the themes of temperature, including tradeoffs between comfort, cost, and value; time, including the utility of heat scheduling; and space, including zonal heating controls. We also discuss patterns of learning, the emergence of environmental values, and issues of discomfort. We conclude by commenting on important distinctions between radiant and ambient heat, as well as between scheduled and on-demand heat. The main findings are 1) tradeoffs between comfort, value and cost occur when it comes to smart heating; 2) people want different numbers of warm hours in their homes at very different times; 3) households chose to heat different numbers of rooms; and 4) there are other non-monetary and non-functional aspects of smart heating that households value. [ABSTRACT FROM AUTHOR]