APDP: Attack-Proof Personalized Differential Privacy Model for a Smart Home

How are people using current smart home technologies, and how do they conceptualize future ones that are more interconnected and more capable than those available today? We deployed an online survey study to 150 participants to investigate use of and opinions about smart speakers, home robots, virtual assistants, and other smart home devices. We also gauged how impressions of connected smart home devices are shaped by the way the devices interact with one another. Through a mixed-methods qualitative and quantitative approach, we found that people mostly use single devices for single functions, and have simple and brief interactions with virtual assistants. However, they imagine their future devices to have more control over the physical environment (i.e., interact with each other) and envision them interacting with people in more socially complex ways. These findings motivate design considerations and research directions for connected smart home technologies.

The wireless transmission of home area networks contributes to the vulnerabilities of smart homes. As a result, smart homes are vulnerable to malicious attacks like eavesdropping. To secure the transmissions, secret keys should be established for smart home devices. In practical smart home applications, smart home devices might be resource-constrained devices. Thus, the lightweight key establishment schemes are preferred. Typically, lightweight physical layer key extraction schemes can be employed by the smart home devices. However, the key generation rate of the schemes is low. To improve the key generation rate, in this paper we propose an adaptive physical layer key extraction scheme for smart homes by making use of the Received Signal Strength (RSS). Specifically, in our scheme, two smart home devices collect the RSS measurements by probing with each other. Then, the collected measurements are quantized by running the proposed Self-adaptive Lossless Quantization algorithm. Completing these operations, the two smart home devices can obtain the secret key. In our scheme, we design the self-adaptive lossless quantization algorithm to improve the key generation rate. To show the practicality of the new scheme, we implement it using the Ralink WiFi cards. Additionally, we analyse the performance of our scheme and compare it with that of other related works.

Brain computer interface (BCI) technology is a communication and control approach. Up to now many studies have attempted to develop an EEG-based BCI system to improve the quality of life of people with severe disabilities, such as amyotrophic lateral sclerosis (ALS), paralysis, brain stroke and so on. The proposed BCIBSHS could help to provide a new way for supporting life of paralyzed people and elderly people. The goal of this paper is to explore how to set up a cost-effective and safe-to-use online BCIBSHS to recognize multi-commands and control smart devices based on SSVEP. The portable EEG acquisition device (Emotiv EPOC) was used to collect EEG signals. The raw signals were denoised by discrete wavelet transform (DWT) method, and then the canonical correlation analysis (CCA) method was used for feature extraction and classification. Another part is the control of smart home devices. The classification results of SSVEP can be translated into commands to control several devices for the smart home. Here, the Power over Ethernet (PoE) technology was utilized to provide electrical energy and communication for those devices. During online experiments, four different control commands have been achieved to control four smart home devices (lamp, web camera, guardianship telephone and intelligent blinds). Experimental results showed that the online BCIBSHS obtained 86.88 ± 5.30% average classification accuracy rate. The BCI and PoE technology, combined with smart home system, overcoming the shortcomings of traditional systems and achieving home applications management rely on EEG signal. In this paper, we proposed an online steady-state visual evoked potential (SSVEP) based BCI system on controlling several smart home devices.

Environmental saving potentials of a smart home system from a life cycle perspective: How green is the smart home?

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.

Background:Technological advances in the smart home have created new opportunities for supporting digital citizens’ well-being and facilitating their empowerment but have enabled new types of complex online harms to develop. Recent statistics have indicated that ‘smart’ technology ownership increases yearly, driven by lower costs and increased accessibility. Research on smart homes has also grown, focusing on technology perspectives at the expense of a user-centric approach sensitive to the smart home’s harms, risks, and vulnerabilities. Objective:This scoping review addresses the information gap by underscoring the scope of literature that exists regarding complex online harms, vulnerabilities, and risks associated with smart home technologies and citizens’ agency. The goal is to understand the state of knowledge, gaps in the literature, and areas for future study. The importance and originality of this paper lie in its interdisciplinary review and approach. It is hoped that this research will contribute to a deeper understanding of complex online harms in the smart home. Design:Three online databases were utilised to identify papers published between 2017 and 2022, from which we selected 235 publications written in English that addressed harms, risks, vulnerabilities, and agency in the smart home context. This allowed us to map contemporary literature to reveal significant gaps in our understanding of the complex online harms affecting smart home users and identify opportunities for further research. Results:This review identified emerging themes of ‘risks’, ‘vulnerabilities’, and ‘harms’ in that order of frequency within the literature on smart homes. The usage of terms is skewed towards computing science and information security, which comprised the majority of the literature at 54.6%. Human–computer interaction papers contributed 24.4%, while social sciences accounted for 16.2%. Conclusion:Risks, harms and vulnerabilities within smart home ecosystems and IoTs are ongoing issues with complexities that necessitate research. Privacy, security, and well-being are key themes that embody the scope of complex harms affecting smart home devices in the broad literature. This review establishes disciplinary research gaps, especially in user-centred perspectives, due to a heavy technology focus in the existing literature. Therefore, further research is needed to address emergent risks, harms and vulnerabilities of smart home devices and understand how user agency and autonomy can complement the design, interface, and socio-technical aspects of smart home systems.

Since the end of 2000, smartphones have explosively spread and have made people’s lives plentiful. With the start of smartphones, new smart devices, including tablet PCs, smart TVs, smart refrigerators, and smart air conditioners, have emerged, expanding their areas from individuals to business and home. These days, smart home service has drawn a lot of attention as a human-centric service. It is the environment where home appliances and other smart devices are connected to internet in order for user service and experience. The current smart home service is simply based on wireless home network to execute the function of connected home. The service has no smart home security so that it is possible for users to suffer a financial loss caused by information leakage or home appliance hacking. Therefore, to apply smart home service to surroundings, it is necessary to take into account security of smart devices. In this paper, we propose an enhanced security framework for smart devices in a smart home environment. The security framework provides the integrity system using the self-signing and access control techniques for preventing the security threats such as data modification, leakage and code fabrication.

Gesture interaction is a natural interaction method and it has been widely applied in various smart contexts. Smart home system is a promising area to integrate gesture interaction. Under this background, it is necessary to generate a set of gestures that can support users’ intuitive interaction with smart home devices. Gesture elicitation study (GES) is an effective method used for generating gestures. In this study, by following GES, we develop a gesture set for controlling a smart TV via a smart speaker, which was common in smart home contexts. Two studies were conducted. In study 1, we conducted a diary study to generate target tasks, resulting in fifteen most frequent tasks in domestic contexts. In study 2, GES was conducted to generate gestures for each command by involving twelve participants. The generated gestures were analyzed by combining frequency, match, ease of use, learnability, memorability and preference, resulting in a set of gestures for smart home contexts.Keywords: Gesture Interaction, Smart Home System, Gesture Elicitation Study

The popularity of smart home systems has greatly increased over the past several years, which provide convenience, automation, and control over various areas of daily life at home. However, these technologies still have issues with scalable user experience, mainly because there aren’t any engaging and intuitive user interfaces. Incorporating pervasive virtual reality (VR) interfaces into smart home systems is the unique strategy proposed in this study to improve scalable user experience. The study’s goal is to use VR technology to develop engaging and simple user interfaces that seamlessly integrate with the actual surroundings of a smart home. Users can engage with their smart home appliances and services through a mixed-reality experience in which virtual items and information are seamlessly incorporated into their environment by donning portable VR headsets. This application considers various difficulties related to developing Ambient Assisted Living (AAL) solutions, including unique characteristics of each end user, appliance, technology, deployment, and data-sharing problems. The Smart Home Systems take advantage of Semantic Web technologies integration abilities and their capacity facing represent significant information into legal models. A virtual reality application called Smart Home Systems allows for residential settings’ setup and customization in AAL systems. Additionally, it effectively uses VR technology to streamline the creation of specialized AAL settings. The application and underlying framework were evaluated for each through two scenarios: designing a home setting specifically for specific scalable user categories.

Color coded multiple access scheme for bidirectional multiuser visible light communications in smart home technologies

The Internet of Things (IoT) has enabled smart home devices to provide users with the convenience of monitoring and controlling their homes directly from their smartphones. However, connecting these devices to the internet and to each other introduces significant security and privacy challenges, including the need to ensure the confidentiality, integrity, and authenticity of the data they collect and exchange. Due to inherent vulnerabilities, smart home devices are susceptible to various forms of cyberattacks, which can compromise the safety and trustworthiness of the smart home environment. Consequently, assessing and understanding these security risks is essential to determine the resilience of such devices. For smart home technologies to achieve widespread adoption, they must gain user confidence through robust security and privacy safeguards. As with many technology-driven sectors, maintaining security remain a critical and ongoing challenge. As homes become increasingly digitized and the adoption of smart devices continues to grow, the likelihood of targeted attacks has also increased. Therefore, this research proposes a methodology comprising four phases: information gathering to identify the device’s FCC ID, establishing the laboratory environment, conducting the exploitation phase, and analyzing the results of the vulnerability assessment. The main objective is to identify potential weaknesses in selected smart home devices. Experimental results revealed that the tested device is vulnerable to replay attacks, in which retransmission of captured packets prompts the device to execute unintended commands.

The integration of Internet of Things (IoT) devices in smart homes has revolutionized the way we interact with our living spaces. In this paper, we present a comprehensive smart home innovation aimed at enhancing safety and comfort. Our system includes several IoT devices controlled through a mobile application for seamless user interaction. A mobile controlled LED bulb, integrated with the Blynk app, provides users with remote lighting control. Live data from an MQ 5 gas sensor is monitored through the mobile application, triggering the activation of a fan in case of gas leakage, ensuring safety within the home environment. An infrared (IR) sensor detects human presence, enabling an automatic pump system for convenient water usage. Additionally, another IR sensor is employed to detect human presence and illuminate mini lights for safer navigation, particularly on staircases. The system is built using Node MCU, providing a cost effective and efficient solution for smart home automation. Through the integration of these IoT devices, our smart home innovation aims to provide users with a safer, more comfortable, and energy efficient living environment. This paper presents the design, implementation, and performance evaluation of our smart home system, demonstrating its effectiveness in enhancing the quality of life for homeowners.

Typical Internet of Things (IoT) and smart home environments are composed of smart devices that are controlled and orchestrated by applications developed and run in the cloud. Correctness is important for these applications, since they control the home’s physical security (i.e. door locks) and systems (i.e. HVAC). Unfortunately, many smart home applications and systems exhibit poor security characteristics and insufficient system support. Instead they force application developers to reason about a combination of complicated scenarios—asynchronous events and distributed devices. This paper demonstrates that existing cloud-based smart home platforms provide insufficient support for applications to correctly deal with concurrency and data consistency issues. These weaknesses expose platform vulnerabilities that affect system correctness and security (e.g. a smart lock erroneously unlocked). To address this, we present OKAPI, an application-level API that provides strict atomicity and event ordering. We evaluate our work using the Samsung SmartThings smart home devices, hub, and cloud infrastructure. In addition to identifying shortfalls of cloud-based smart home platforms, we propose design guidelines to make application developers oblivious of smart home platforms’ consistency and concurrency intricacies.

The Internet of Things (IoT) presents many possibilities, including security and privacy issues. The Digital Forensics has long been studied in academia and industry, but forensics for smart home device has never been attempted. Smart home forensics deals with tools and techniques for recovering data and evidence from mobile devices. This paper describes a data acquisition, classification and analysis process of smart home devices using the IoT. It also includes analysis based on attack scenarios of collected data and smart home device forensic models suitable for such scenarios.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.