Exploring The Responsibilities Of Single-Inhabitant Smart Homes With Use Cases

DOI: 10.3233/AIS-2010-0076This paper makes a number of contributions to the field of requirements analysis for Smart Homes. It introduces Use Cases as a tool for exploring the responsibilities of Smart Homes and it proposes a modification of the conventional Use Case structure to suit the particular requirements of Smart Homes. It presents a taxonomy of Smart-Home-related Use Cases with seven categories. It draws on those Use Cases as raw material for developing questions and conclusions about the design of Smart Homes for single elderly inhabitants, and it introduces the SHMUC repository, a web-based repository of Use Cases related to Smart Homes that anyone can exploit and to which anyone may contribute

Privacy Mining from IoT-based Smart Homes

Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elders' privacy might be disclosed from smart homes due to non-fully protected network communication or other reasons. To demonstrate how serious this issue is, we introduce in this paper a Privacy Mining Approach (PMA) to mine privacy from smart homes by conducting a series of deductions and analyses on sensor datasets generated by smart homes. The experimental results demonstrate that PMA is able to deduce a global sensor topology for a smart home and disclose elders' privacy in terms of their house layouts.Comment: This paper, which has 11 pages and 7 figures, has been accepted BWCCA 2018 on 13th August 201

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Smart homes and their users:a systematic analysis and key challenges

Published research on smart homes and their users is growing exponentially, yet a clear understanding of who these users are and how they might use smart home technologies is missing from a field being overwhelmingly pushed by technology developers. Through a systematic analysis of peer-reviewed literature on smart homes and their users, this paper takes stock of the dominant research themes and the linkages and disconnects between them. Key findings within each of nine themes are analysed, grouped into three: (1) views of the smart home-functional, instrumental, socio-technical; (2) users and the use of the smart home-prospective users, interactions and decisions, using technologies in the home; and (3) challenges for realising the smart home-hardware and software, design, domestication. These themes are integrated into an organising framework for future research that identifies the presence or absence of cross-cutting relationships between different understandings of smart homes and their users. The usefulness of the organising framework is illustrated in relation to two major concerns-privacy and control-that have been narrowly interpreted to date, precluding deeper insights and potential solutions. Future research on smart homes and their users can benefit by exploring and developing cross-cutting relationships between the research themes identified

Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Understanding users’ acceptance of smart homes

© 2019 Elsevier Ltd Smart homes allow owners to monitor and control wide range of home appliances remotely and intelligently. Despite its potential, the acceptance of smart homes by residents is still far from expectations. Thus, this study aims to investigate the factors that influence residents’ acceptance and usage of smart home. By extending the Technology Acceptance Model (TAM), this research incorporates more factors related to users such as trust, awareness, enjoyment, and perceived risks to study intention to use smart homes and investigate their impact quantitatively by using SEM-PLS approach. Results show that trust, awareness, enjoyment, and perceived risks, with perceived usefulness and perceived ease of use significantly influence attitude towards smart homes which, in turn, impact the intention to use smart homes

Trust in Smart Homes: The Power of Social Influences and Perceived Risks

The increased reliance on smart technologies has caused people to consider smart homes. A smart home is using basic assistive devices to build a home environment that contains many technological features and home appliances that are connected and integrated. In order to adopt smart homes, it is needed that users trust this technology, and the factors that influence trust are yet to be discovered. Thus, the aim of this study is to gain a deeper understanding of customer trust in smart homes by empirically exploring the factors that influence customers’ trust in smart homes, understanding how those factors are intercorrelated, and the influence of power and direction of each factor. To address the research aim, an online survey is conducted to explore the perceptions of the residents of the UAE residents through a convenience sampling approach. As a result, most people believe that smart homes are reliable and competent. By collecting 158 responses and analyzing them through the SEM-PLS approach, it is found that the social influence, perceived security risks, and perceived financial risks significantly impact customers’ trust in smart homes and that the social influences can significantly impact people’s perceived risks (security, privacy, and financial risks) as mediators to trust in smart homes

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes