Energy Conservation in IoT-Based Smart Home and Its Automation

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.

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.

With the rise of Internet of Things (IoT) devices in today’s world, there comes also risks of privacy and security. IoT devices are mostly used in homes and so the idea of ‘smart home’ devices have also spread. While homes are becoming smarter, the user’s privacy including sensitive information and their physical and technological security are being put under risk. This paper takes different categories of smart home devices that are in the market today and looks at various researches that have been completed around its privacy and security. Then we establish a framework with the intention of providing consumers a security rating system from a security and privacy perspective in addition to customer reviews to make decisions regarding buying a smart device. The smart home devices analyzed are then tested against the security controls in the framework. When implemented, we hope that this security-rating framework can help the consumer make smarter choices about the device they bring into their home.

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.

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.

During the last few years, smart homes became reality due to the popularization of Internet of Things (IoT). This kind of environment is composed of several types of IoT heterogeneous wireless devices, where each one has singular characteristics and a particular set of protocols. This fact makes the smart home very complex network to deploy new applications and services. Therefore, a smart home needs an approach to abstract the communication with these IoT devices, improving the flexibility and the manageability of the applications running on the top of the smart home, as well as the network infrastructure as a whole. Within this context, this paper proposes a middleware for communication abstraction of IoT devices in smart homes. The proposed middleware allows the communication with devices through a high-level interface, enabling the management of heterogeneous IoT devices as a generic interface. The experiments performed in a real testbed scenario suggest that the proposed middleware is scalable and improves the management of IoT devices.

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.

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.

Smart home devices are vulnerable to attacks that put their users’ security at risk. Vulnerabilities are discovered very frequently and can expose these devices through unsecured services. Meanwhile, the lack of standardisation in upgrade methods makes smart homes a potentially vulnerable environment. Furthermore, many manufacturers release their products and then abandon them, refusing to support security updates. As a result, security updates are needed to deal with the emergence of new attacks. There are several proposals to promote security in smart homes. However, there are rare solutions where changes for security purposes occur with little or no human intervention. This paper presents UP-Home, a self-adaptive solution that manages the security of smart homes. UP-Home aims to ensure that smart home devices meet the security requirements set by industry standards. The solution can continually identify and mitigate smart home security vulnerabilities. With autonomous computing techniques, UP-Home seeks to ensure the self-protection of devices and, consequently, the entire smart home. With the UP-Home evalu-ation, it was possible to notice significant improvements in the security of the smart home without any human intervention.

The fourth industrial revolution put new processes on the rails of automation in industry, healthcare, home and other areas of human life through the mass integration of the concept of the Internet of Things into these areas. At the same time, these processes are not bypassed by home automation systems or smart homes. A smart home is defined as a system of interconnected sensors, actuators, and other devices that are networked together with computer systems and controlled by appropriate software. This connection allows to collect, share and analyze data, which helps to increase comfort, automation and control over the parameters of the house. However, along with the obvious advantages and conveniences of rooting home automation systems, this concept leaves a number of potential security bottlenecks for attackers. Data collected by smart devices is always point of interest to hackers and hijackers of confidential information.Third-party access to data collected by smart devices can lead to a variety of emergencies, the degree of danger of which will depend solely on the wish of the owner of the intercepted data. The paper proposes an intelligent system for detecting anomalies and identifying smart home devices based on the collective communication of smart homes. The concept of the system is based on the benefits of combining smart homes into a social network in terms of improving the security of both a single smart home and the entire social network of combined smart homes. Detection of anomalies and identification of devices in each of the smart homes is based on monitoring network traffic and forming profiles of smart devices that are present in the network. Profiles consist of a set of features that describe the behavior of smart devices on the network, including the period of activity of the device and the period of its sleep. Based on this, a whitelist of allowed profiles of devices operation in the cluster is formed. To verify the presence of a profile in the whitelist the Random Forest algorithm was used. A key feature of the system is the communication of smart home clusters with each other to exchange information about the available smart device profiles in the whitelists of each cluster.

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%.

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

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.

The proliferation of smart devices in recent years has led to novel smart home applications that upgrade traditional home appliances to intelligent units and automatically adapt their services without human assistance. In a smart home system, a central gateway is required to coordinate the functions of various smart home devices and allow bidirectional communications. However, the gateway may cause leakage of sensitive information unless proper privacy protections are applied. In this work, we first introduce a smart home model based on fog computing and secured by differential privacy. Then, we apply a personalized differential privacy scheme to provide privacy protection. Furthermore, we consider a collusion attack and propose our differential privacy model called APDP based on a modified Laplace mechanism and a Markov process to strengthen privacy protection, thus resisting the attack. Lastly, we perform extensive experiments based on the real-world datasets to evaluate the proposed APDP model.

User-centric approaches to technological interfaces have increasingly surfaced in the recent times. In smart homes, this manifests in the form of how people adapt and interact with devices, and the set up as a whole. This study aims to analyze the behavioral aspects of living in a smart home through interview and structured survey-based interactions with 21 families in the Netherlands who were provided a predefined set of smart devices for about three weeks. We evaluate user experience with smart devices through four stages of smart home adoption. Based on user behavior and motivations, we find four personas emerging, namely convenience-oriented, safety-oriented, cautiously supportive and sustainability-oriented. The study finds a positive experience predominantly with smart home devices when they are delivered and integrated in a manner that serves user's priorities in a home. Future directions incorporating user preferences in smart home design are discussed.