Spatial-frequency data acquisition using rotational invariant pattern matching in smart environments

The home automation controls the electronic devices remotely and automatically with the help of context aware behaviour. Context awareness is the process where data from various sensors and input from user or user action must be interpreted and corresponding commands to actuators must be generated. This context aware automation and decision support process is done with the help of adaptive rules which is defined for the automatic behaviour of devices in the smart home environment. The consistent behaviour of the devices in the smart environment will capture the customer satisfaction. This work describes the process of checking the consistent behaviour of the context aware system in smart home environment using formal modelling and verification method. We have discussed a mechanism to check the consistent behaviour of the adaptive mechanism of smart home environment. The result ensures the consistent behaviour of the context aware system in the smart environment.

Unobtrusive and multiple-sensor interfaces enable observation of natural human behavior in smart environments. Being able to detect, analyze and interpret this activity allows for the implementation of various applications, including real-time surveillance and real-time support in smart home and office environments. We focus on smart meeting environments, where nonverbal behavioral cues sometimes tell more about issues such as discussion participation, involvement and contribution, than information obtained from verbal contributions. An important aspect of this behavior is the interaction between meeting participants. We regard the special case where some of the participants are at physically different locations, which hinders natural interaction. We discuss how we can exploit the ability of a sensor-equipped environment to detect nonverbal interaction cues and use these to allow and improve natural interaction between collaborating participants at distributed locations. We focus on research efforts to detect nonverbal interaction cues by looking at various modalities. Also, we discuss how information obtained from the fusion of these modalities allows us to generate and display behavioral cues that allow remote participants to take part in a distributed meeting in a natural way.

Smart environments are becoming ubiquitous despite many potential security and privacy issues. But, do people understand what consequences could arise from using smart environments? To answer this research question, we conducted a survey with 575 participants from three different countries (Germany, Spain, Romania) considering smart home and health environments. Less than half of all participants mentioned at least one security and privacy issue, with significantly more German participants mentioning issues than the Spanish ones and the Spanish participants in turn mentioning significantly more security and privacy issues than the Romanian participants. Using open coding, we find that among the 275 participants mentioning security and privacy issues, 111 only expressed abstract concerns such as “security issues” and only 34 mentioned concrete harms such as “Burglaries (physical and privacy)”, caused by security and privacy violations. The remaining 130 participants who mentioned security and privacy issues named only threats (i.e. their responses were more concrete than just abstract concerns but they did not mention concrete harming scenarios).

In this work we will examine and develop a system that can assist people in Activities of Daily Life (ADL). This study focuses on resolving conflicts for the requests from different users' profiles, for instance - elderly, adult and young. The objective of the system is to present a dialogue manager which is able to detect multi-user semantic conflict and to resolve the conflict for improved dialogue informing about its decisions using a system interface avatar. The system is also able to prioritize requests that occurred among the services of multiple home appliances, as well as to deal with conflicting entities involving a single device. We investigated whether the multi-user context awareness by a Virtual Assistant adds value to the Smart Home concept in recognizing multi-user conflicts dynamically. This work has proposed a preference based method for resolving conflict and evaluated the developed system in a smart home environment.

Stakeholders in disaster management settings often find the effective and efficient utilisation of emerging technologies quite a challenging process but very frequently a critical computational inclusion to the intelligence required for protecting individuals, communities, their environment and critical infrastructures. The special issue is dedicated to the dissemination of original contributions that are related to the theories, practices and concepts of smart environments and emerging collective computational and collaborative technologies for the specific purpose of improving intelligence when managing natural or man-made disasters. In particular, the special issue is focused on the applicability—for the purpose of managing disasters—of technologies that can support autonomous adaptation to complex and messy situations caused from these, highly dynamic contexts. Various methods and technologies including simulations, multi-dimensional decision modeling, data mining, swarm intelligence, smart spaces and sensors, contextaware, situated and pervasive computing, geographical information systems, ad-hoc mobile networks, wireless communications, grid and cloud computing, social networks and crowd sourcing are a smart technology means to enable a more informed decision making to mitigate and prepare for, respond to and recover from growing occurrences of disasters. This special issue comprises six articles, which have been selected after a careful review process. Specifically, this is a collection of extended versions of papers presented either at the Smart Environments for Disaster Management (SEDM-2011) workshop which run in conjunction with the 6th 3PGCIC-2011, held on October 26–28, 2011 in Barcelona, Spain or at the Computational Intelligence for Disaster Management (CIDM-2011) workshop which run in conjunction with the 3rd INCoS 2011, held on November 30–December 2, 2011 in Fukuoka, Japan. In the first article, Li explains that mobility is an important issue that deters people living in remote areas and elderlies to visit health clinics on a regular basis; this is also the case for individuals with disabilities. Advances in information and telecommunication technologies have been made telemedicine possible. By using the latest sensor technologies, a person’s vital data can be collected in a smart home environment. The bio-information can then be transferred wirelessly or via the Internet to medical databases and the healthcare professionals. The article surveys and compiles the state-of-the-art smart home technologies and telemedicine systems. In the second article, Sakanushi et al. discuss concerns with triage tags, which are widely used at disaster scenes. Some of these concerns include the inability to show the current priorities of casualties and a failure to collect the physiological conditions of the casualties. In their article, authors propose an electronic triage which continuously monitor the vital signs of casualties and transmit them to an electronic triage server. Experimental results show that their proposed electronic triage system can save more lives than the paper triage tags version. In the third article, Furey et al. explain that the use of Wi-Fi signals is an attractive and reasonably affordable option to deal with the currently unsolved problem of widespread tracking in an indoor environment. In their article, authors present a system which aims at overcoming N. Bessis (&) E. Asimakopoulou School of Computing and Mathematics, University of Derby, Kedleston Road, Derby DE22 1GB, UK e-mail: n.bessis@derby.ac.uk

Smart homes are an emerging paradigm of Internet of Things (IoT) in which users can remotely control various home devices via the internet anytime and anywhere. However, smart home environments are vulnerable to security attacks because an attacker can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Thus, secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2021, Kaur and Kumar presented a two-factor based user authentication protocol for smart homes using elliptic curve cryptosystems (ECC). Unfortunately, we demonstrate that their scheme cannot resist security attacks such as impersonation and session key disclosure attacks, and also ensure secure user authentication. Moreover, their scheme is not suitable in smart home environments because it utilizes public-key cryptosystems such as ECC. Hence, we design a secure and lightweight three-factor based privacy-preserving authentication scheme for IoT-enabled smart home environments to overcome the security problems of Kaur and Kumar’s protocol. We prove the security of the proposed scheme by using informal and formal security analyses such as the ROR model and AVISPA simulation. In addition, we compare the performance and security features between the proposed scheme and related schemes. The proposed scheme better provides security and efficiency compared with the previous schemes and is more suitable than previous schemes for IoT-enabled smart home environments.

This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

The increasing availability of low-cost smart devices is bringing them to be used more and more in the smart home. However, the development of a smart home environment requires to take into account several aspects. First of all, designers must consider the end user (namely the person that actually uses the smart home), and not only the technology, at the center of any intervention. Another important aspect is the interaction between the smart home and the appliances that are already deployed in the same smart environment. Moreover, most of smart home solutions are static and do not allow end users to customize them according to their real needs and preferences. Finally, not all end users may possess the necessary knowledge and skills to customize a smart home, but someone else, such as an adult child or a caregiver, may be called on to carry out this task for them. In this paper, we analyze all these aspects that can influence the development and evolution of a smart home. We then propose a model supporting developers and software engineers to deploy and evaluate a smart home solution that adopts end-user development techniques. It is based on the International Classification of Functioning scale, which is used to characterize the person that is mainly going to live in the smart home and define a solution suitable to his/her needs.

The Internet of things is a new technological revolution following the computer and Internet. It aims to connect all physical objects existing in the world and forms a network with everything. In recent years, smart home gradually enters into our life. Smart home uses the Internet of things technology to connect all kinds of devices in the home, to achieve a smart home environment. Although the development of smart home has brought a qualitative leap to people’s life, there are many problems in security. Privacy security is one of the challenges to the smart home environment. Attackers can intrude various smart devices in the smart home environment, to achieve the purpose of stealing users’ personal information and privacy. Among these devices, smart cameras are the most intruded frequently. Since many cameras are installed in users’ homes to achieve real-time monitoring of the environment, but the existence of these cameras provides a channel to get information for attackers. In recent years, the leak of video privacy is emerging in an endless stream. According to the researches about privacy protection, this paper proposes a new scheme to selectively encrypt the video captured by the cameras through machine learning technology, so as to protect the personal privacy of users and improve the security of the smart home environment.

Smart homes can provide complementary information to assist home service robots. We present a robotic misplaced item finding (MIF) system, which uses human historical trajectory data obtained in a smart home environment. First, a multi-sensor fusion method is developed to localize and track a resident. Second, a path-planning method is developed to generate the robot movement plan, which considers the knowledge of the human historical trajectory. Third, a real-time object detector based on a convolutional neural network is applied to detect the misplaced item. We present MIF experiments in a smart home testbed and the experimental results verify the accuracy and efficiency of our solution.

Internet of Thing (IoT) is going to make such a world where physical things (smart home appliances, and smart watches etc.) revolutionized the information networks and services providing systems which provide innovative and smart services to human. With smart home technology, our living area is becoming more comfortable and convenient. Smart home technology provides automated, intelligent, smart, innovative and ubiquitous services to residential users through Information Communication Technology (ICT). Due to internet-connected, dynamic and heterogeneous nature of smart home environment creates new security, authentication and privacy challenges. In this paper, we investigate security attacks in smart home and evaluate their impact on the overall system security. We identified security requirements and solutions in the smart home environment. Based on several scenarios, we suggest to set security goals for the smart home environment. Based on historical data, we forecast security attacks (like malware, virus etc.) and estimated that how many attacks are expected to be launched in coming years.

In smart environments, indoor positioning systems provide several options for smart computing users, businesses, and industries, thereby dramatically enhancing human well-being and productivity. Smart homes and smart indoor environments are prominent emerging technologies in the Internet of Things era and future communications, with applications such as providing personalized healthcare to the elderly and those with impairments by connecting them to the world via high-speed wireless communication infrastructure. This study offers a new approach to real-time indoor positioning using passive RFID technology to estimate the real-time location of smart home users based on their movements in smart environment space. An experimental indoor positioning system technique intends to improve assisted living and identify daily activities in a smart environment. To demonstrate this, we conducted a case study on indoor positioning using RFID technology. The experimental investigation is based on a location-based system that leverages the creation of deep learning algorithms in conjunction with radio signal strength indicator (RSSI) measurements of passive RFID-tagged devices. The proposed architecture encourages more precise identification of smart home objects and the ability to precisely locate users in real-time with good measured precision while minimizing technical and technological barriers to the adoption of location-based technologies in the daily lives of smart environment inhabitants. This will eventually facilitate the realization of location-based Internet of Things (IoT) systems.

One of the functionalities a service robot should have, to work in a smart environment, is object recognition and handling of objects. Many researchers have attempted to make service robots recognize objects in natural environments but no conventional vision systems can recognize target objects in complex scenes. We built a prototype smart environment in our research building at Korea Institute of Industrial Technology (KITECH) to demonstrate the feasibility that a service robot with few sensors can provide reliable services by interacting with an environment full of smart devices through wireless sensor network communications. In this paper, we address the issues of how to develop an object recognition system for our RoboMaidHome project. Our object recognition system can not only recognize objects with no need of a previous training stage but make the service robot light-weight with minimum hardware and software requirements while working well in a smart home environment. In addition, the matching process for object recognition is simple since only a few image processing techniques are employed. Our object recognition scheme on the basis of MPEG-7 visual descriptors is under development and includes color, texture, and shape. It will also be incorporated into our mobile service robotics system.

Smart Home research started in 2000. Since then, Smart Home has attracted the attention of many researchers, and several studies have been conducted. This study aims to chart Smart Home research trends using bibliometric analyses from all years up to the publication date indexed in Scopus. We use keywords related to Smart Home in the Scopus database. Analysis techniques that use parameters include the year of publication, Scopus quartile range, year of publication, type of publication, journal publication, the publication of the most frequently cited maps, and density display mapping. The results show that the Q1 journal dominated since the first Smart Home publication in 2000 and peaked in 2013. The journal article type is the most commonly published compared to research conferences and other publications. Journal publications show the majority of IEEE transactions in consumer electronics. Three Smart Home publications have been cited more than 1563 times. This study also found that the words ‘smart home’, ‘technology,’ and ‘management’ are widely used by researchers. For future research, you should use keywords like ‘smart home energy management,’ ‘smart home environment,’ ‘sensor,’ ‘electric vehicle,’ and ‘smart home technology.’

Gesture recognition is a field of research that consists in recognizing patterns of movement performed by a human using his body parts with or without the help of a comprehensive device (a mouse, a laser, etc.). This particular area as attracted a number of researchers over the years that applied such algorithms in a broad range of disciplines. In particular, it was exploited on early research initiatives with pervasive environments to enable simple communication with automation systems. Nowadays, those environments are used for more than automation. Many researchers, in fact, believe it is one of the most promising solutions to the problems related to ageing of the population. Smart homes are seen as an alternative to the full-time support of a semi-autonomous person by healthcare professional and thus also a potentially economically viable solution to the rising cost of such support. However, researchers are still facing many challenges in that regards, such as the comprehension of the context and of the ongoing activity of daily living. In that equation, gesture recognition could help extract more information from the collected data and thus reinforce the context modeling. The knowledge extracted could even help with monitoring of more fine-grained activities and with the understanding of normal or abnormal behaviors. Gesture recognition is often considered as a solved problem since the techniques to perform it work well as soon as we can track accurately. However, in smart environment, tracking is very imprecise and hard to achieve with limited technology (i.e. noninvasive sensors). In this paper, we present a novel gesture recognition algorithm that works under uncertainty, and that is scalable to the precision of the tracking system. The algorithm is based on the tracking of passive RFID tags installed on all everyday life objects in a smart environment. A set of experimentation in simulation and in a real smart home environment is presented. The results are very encouraging despite the very low accuracy of the passive RFID tracking system.