Showing papers on "Smart device published in 2022"

MAGLeak: A Learning-Based Side-Channel Attack for Password Recognition With Multiple Sensors in IIoT Environment

Abstract: As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

PUF-Assisted Lightweight Group Authentication and Key Agreement Protocol in Smart Home

Abstract: Various IoT-based applications such as smart home, intelligent medical, and VANETs have been put into practical utilization. The smart home is one of the most concerned environments, allowing users to access and control smart devices via the public network remotely. The smart home can provide many intelligent services for users through these smart devices. To securely access devices and obtain collected data over the public network, multifactor authentication protocols for smart home have gained wide attention. However, most of these protocols cannot withstand impersonation attack, smart device lost attack, privileged-insider attack, smart card lost attack, and so on. Besides, high communication and computational costs weaken the system performance, which leads to most authentication protocols are not suitable for resource-constrained smart devices. To mitigate the aforementioned drawbacks, we proposed a PUF-assisted lightweight group authentication and key agreement protocol to implement secure access to multiple devices in the smart home simultaneously using the Chinese Remainder Theorem and secret sharing technique. Our protocol also utilizes physical unclonable function (PUF) and fuzzy extractor technique to extract the digital fingerprint of the smart devices, which can uniquely validate smart devices and protect the secrets stored in their memory. Our protocol can support various security features and withstand the many well-known attacks in the smart home. The performance analysis indicates that the proposed protocol can efficiently reduce communication/computational costs when the user simultaneously accesses multiple devices.

Chinese adult segmentation according to health skills and analysis of their use for smart home: a cross-sectional national survey

Abstract: Digital health has become a heated topic today and smart homes have received much attention as an important area of digital health. Smart home is a device that enables automation and remote control in a home environment via the internet. However, most of the existing studies have focused on discussing the impact of smart home on people. Only few studies have focused on relationship between health skills and use of smart home.To analyze the health skills of Chinese adults and segment them to compare and analyze the use of smart home for each group.We used data from 11,031 participants aged 18 and above. The population was clustered based on five health skills factors: perceived social support, family health, health literacy, media use, and chronic diseases self-behavioral management. A total of 23 smart homes were categorized into three sub-categories based on their functions: entertainment smart home, functional smart home, and health smart home. We analyzed demographic characteristics and utilization rate of smart home across different cluster. Each groups' features and the differences in their needs for smart home functions were compared and analyzed.As a result of the survey on health skills, three groups with different characteristics were clustered: good health skills, middle health skills, and poor health skills. The utilization rate of smart home was the highest was good health skills group (total smart home: 92.7%; entertainment smart home: 61.1%, functional smart home: 77.4%, and health smart home: 75.3%; P < 0.001). For entertainment smart home, smart TV had the highest utilization rate (good health skills: 45.7%; middle health skills: 43.5%, poor health skills: 33.4%, P < 0.001). For functional smart home, smart washing machine (good health skills: 37.7%, middle health skills: 35.11%, poor health skills: 26.5%; P < 0.001) and smart air conditioner (good health skills: 36.0%, middle health skills: 29.1%, poor health skills: 24.6%) were higher than other of this category. For health smart home, sports bracelet has the highest utilization rate (good health skills: 37.3%, middle health skills: 24.5%, poor health skills: 22.8%).People can be divided into different categories based on health skill profiles, those with good health skills had a better utilization rate of smart home. The government and smart home companies need to focus on people with poor smart home use in various ways to promote their use of smart homes for personal health management.

Security Perception of IoT Devices in Smart Homes

Abstract: IoT devices are used frequently in smart homes. To better understand how users perceive the security of IoT devices in their smart homes, a model was developed and tested with multiple linear regression. A total of 306 participants participated in the survey with measurement items, out of which 121 had already been using IoT devices in their smart homes. The results show that users’ awareness of data breaches, ransomware attacks, personal information access breaches, and device vulnerabilities have an effect on IoT security importance. On the other hand, users often do not check their security settings and feel safe while using IoT devices. This paper provides an overview of users’ perception of security while using IoT devices, and can help developers build better devices and help raise awareness of security among users.

Privacy Leakage Analysis for Colluding Smart Apps

Abstract: The rapid proliferation of Internet-of-Things (IoT) has advanced the development of smart environments. By installing smart apps on IoT platforms, users can integrate IoT devices for convenient automation. As smart apps are exposed to a myriad of sensitive data from devices, one severe concern is about the privacy of these digitally augmented spaces. The recent work SAINT [1] has been proposed to detect sensitive data flows in individual smart apps using taint analysis. But it has high false positives and false negatives due to inappropriate consideration of taint seeds and taint sinks.One important security issue ignored by existing work is that the IoT platform supports parent-child smart apps. Their ability to communicate, however, has a negative effect on security. We call the parent-child smart apps colluding smart apps. Unfortunately, no tool exists to detect smart app collusion. We propose PDColA, which addresses the limitations of SAINT, and more importantly, can detect privacy leakages by colluding smart apps. The evaluation results show that PDColA achieves higher accuracies than SAINT in detecting privacy leakages by individual smart apps, and is effective to detect privacy leakages by colluding smart apps.

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

Abstract: The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%.

A Multi-Region Investigation of the Perceptions and Use of Smart Home Devices

Abstract: Smart Home Devices are household objects and appliances that are augmented with network connectivity and interactive capabilities. However, the benefits and conveniences of such augmentation are tempered by corresponding increases in privacy and security threats. Studies of user perceptions of these threats and user practices for addressing them are limited mostly to specific devices and/or small samples from a single region. To address this gap, we compared perceptions and practices of people in three geographic regions regarding privacy and security matters related to Smart Home Devices. Across these regions, we found differences in perceived regulatory protection and other regional factors. Our findings suggest that a co-evolution of the design and public policy related to Smart Home Devices could enhance privacy protection and drive increased adoption of these devices.

Information Security and Privacy in Smart Cities, Smart Agriculture, Industry 4.0, Smart Medicine, and Smart Healthcare

Abstract: AbstractInternet of Things (IoT) is used to interconnect various things, devices, technologies in a network in order to perform various tasks at a higher speed, less loss of information as well as for device-cloud/device-device communication. IoT works by transmission of data collected by the large number of sensors,devices which communicate to the cloud through the internet connectivity. Software processing is done after the data gets to the cloud. Some of the IoT devices used are smart mobiles, smart door locks, medical sensors, smart refrigerators, smartwatches, smart bicycles, smart fire alarms, fitness trackers and smart security systems. When a number of smart gadgets are connected with the Internet, there are various security threats, attacks and concerns when a malicious user enters the network. There is a possibility of the data transmitted/received being modified, lost or misused. In order to prevent those threats, there are various solutions introduced by various researchers for securing the networks. In this article, we analyse the importance, characteristics, security issues and privacy concerns involved in various applications such as Smart Agriculture, Smart Cities, Smart Healthcare and Smart Medicine. Frameworks proposed to reduce the impact of security and privacy issues are also discussed along with future research directions and scope.KeywordsInternet of ThingsSecuritySensorsSmart citySmart medicineSmart healthcareSmart agriculture

Smart Farming System Using IoT and Cloud

Abstract: Due to the unprecedented increase in human population, agriculture plays an indispensable role in satisfying their daily needs. Henceforth, improving farm productiveness is indeed a huge challenge in the existing farming industry, which lacks continuous record management to satisfy the constantly emerging food needs. Along with the increasing population, global warming and climate transition also remain as an increasing challenge in the agricultural sector. In this scenario, this research has attempted to develop a smart farm management method, which incorporates cloud as well as the Internet of things (IoT) to take appropriate action. For instance, smart farming helps to provide a variety of important data such as air temperature. The paper has provided a smart device for farm field tracking, which controls dry run, motion detection, soil moisture detection, rainwater detection, humidity, and temperature. Also, this research work implements proper measures for those concepts on receiving the collected information without human input and later the detected quantities are stored for further data analysis within the cloud. Real-time feeds are being supervised upon this webpage as well as on the cell phone messaging. These would encourage farm workers and cultivate agricultural crops in a more modern way.

Toward Smart Home Authentication Using PUF and Edge-Computing Paradigm

Abstract: The smart home is a crucial embodiment of the internet of things (IoT), which can facilitate users to access smart home services anytime and anywhere. Due to the limited resources of cloud computing, it cannot meet users’ real-time needs. Therefore, edge computing emerges as the times require, providing users with better real-time access and storage. The application of edge computing in the smart home environment can enable users to enjoy smart home services. However, users and smart devices communicate through public channels, and malicious attackers may intercept information transmitted through public channels, resulting in user privacy disclosure. Therefore, it is a critical issue to protect the secure communication between users and smart devices in the smart home environment. Furthermore, authentication protocols in smart home environments also have some security challenges. In this paper, we propose an anonymous authentication protocol that applies edge computing to the smart home environment to protect communication security between entities. To protect the security of smart devices, we embed physical unclonable functions (PUF) into each smart device. Real-or-random model, informal security analysis, and ProVerif are adopted to verify the security of our protocol. Finally, we compare our protocol with existing protocols regarding security and performance. The comparison results demonstrate that our protocol has higher security and slightly better performance.

Understanding device integration bugs in smart home system

Abstract: Smart devices have been widely adopted in our daily life. A smart home system, e.g., Home Assistant and openHAB, can be equipped with hundreds and even thousands of smart devices. A smart home system communicates with smart devices through various device integrations, each of which is responsible for a specific kind of devices. Developing high-quality device integrations is a challenging task, in which developers have to properly handle the heterogeneity of different devices, unexpected exceptions, etc. We find that device integration bugs, i.e., iBugs, are prevalent and have caused various consequences, e.g., causing devices unavailable, unexpected device behaviors. In this paper, we conduct the first empirical study on 330 iBugs in Home Assistant, the most popular open source smart home system. We investigate their root causes, trigger conditions, impacts, and fixes. From our study, we obtain many interesting findings and lessons that are helpful for device integration developers and smart home system designers. Our study can open up new research directions for combating iBugs in smart home systems.

Lora Based Safety Smart Device for Woman Protection

Abstract: Women are less secure in today's environment, and they face numerous security concerns. They are confronted with numerous difficult situations. As a result, the government has offered security to the society through rules and regulations in order to ensure their safety and security. Despite the fact that there are numerous security systems available, the need for advanced smart security systems is growing. A smart security system for women has been implemented to address these issues. At times of any danger, the device uses sensor to detect bodily factors like heart rate, temperature change, therefore the victim's movement. IoT device uses an app to monitor sensor levels, and a LoRa transmitter sends sensor data to a LoRa receiver on a distant device. The theme of this project is to help women when they are in risky situation and it is easy to carry anywhere.

Autonomous Smart Device for COVID-19 Detection Using Artificial Intelligence

Abstract: Corona is a pandemic disease and is spreading all over the world. There is also lack of corona virus detection machines. If it is detected at very early stages without pathological intervention, then further spreading of the disease can be controlled, and many of human lives can be saved. So, the proposed biomedical device can be used for fast and accurate prediction of COVID-19 from chest x-rays. X-ray can also be taken from anywhere and sent through any communication medium. Even if error is added, it can be removed using error concealment algorithms. Automated AI-based systems will be used for prediction of normal, COVID-19, and pneumonic cases from x-ray images. It makes detection of COVID-19 infection less costly and portable. This device can be stored in less stringent conditions, making it more effective.

Availability, Readability, and Accessibility: Content Analysis Study of Privacy Information on Smart Device Websites

Abstract: Smart devices can be found almost everywhere. As this technology continues to skyrocket in popularity, privacy issues associated with these devices are growing too. Using content analysis, this study examines 125 smart device websites for privacy concerns, privacy protection measures, and privacy regulations and policies. The research also studies the availability, readability, and accessibility of the privacy information provided by smart device companies on their websites. Findings reveal privacy information can differ depending on the smart device features. While privacy information appears to be accessible for consumer audiences, it is not always comprehensive or easy to understand.

The Prototype of Healthy Smart Building Based on Google Assistant

Abstract: Coronaviruses are a group of viruses from the subfamily Orthocronavirinae in the Coronaviridae family and the order Nidovirales. In general, the transmission of this virus occurs through droplets or body fluids splashed on someone or objects around them within 1-2 meters’ distance away through coughing and sneezing. Office buildings, cafes, and shopping areas are one of the clusters for spreading the coronavirus because the following places lack awareness of health protocols. This research will create a healthy and Smart Building system where this system consists of two devices from the following problems. The first device is a health procedures checker system where using an ultrasonic sensor as an automatic switch can provide a hand sanitizer as the first step to kill viruses. The GY609 sensor is used, which can measure the temperature of visitors without touching it. The second system is the Smart Switch; the doors and lights can control using voice using google assistant to minimize the doorknob’s touch or the light switch. The healthy and Smart building test results are for the intelligent, healthy procedures system, 3-7 cm is the optimal distance from the hand to the sensor, 5cm is the distance from the hand to the temperature sensor, and the motor working time is 1 second. For the Smart switches, the recommended network system is 3G, 4G, or Wi-Fi to avoid excessive delays.

Smart Devices to Detect Health Abnormalities

Abstract: The smart devices gain a lot of attraction in the health care field. Medical sensors, wireless communications, and computational analytics advance medical services in healthcare field. Due to mobile technologies, smart services are becoming part of everyone’s regular life. Even though we had medical advancements, day-to-day disease incidents and death rates are increasing for the people. Lifestyles, food habits, and other simple health concerns are causing major health issues that also lead to death. Continuous monitoring of vital symptoms may allow people to detect abnormal health conditions at an early stage of disease attack. It supports the patient to increase the survival rate through undergone better treatment. For this, smart devices are one of the solutions, to closely monitor human health. Smart devices and their role in the early detection of health abnormal condition are projected in this article by taking the heart disease paradigm.

A Pill to Find Them All: IoT Device Behavior Fingerprinting using Capsule Networks

Abstract: Aim and Background: The aim of this study is the application of novel deep learning technique of capsule networks for device behavior fingerprinting. Device behavior fingerprinting emerged as an important means to characterize the network behavior of connected devices due to the dynamic nature of smart systems. The study of device behavior fingerprinting strategies gave us an insight into the strengths and weaknesses of different machine learning techniques. It also led us to some research questions that we incorporated in the proposed framework. Firstly, we explored the means to improve the efficiency of passive device fingerprinting techniques. Secondly, we needed to address the privacy concerns that arise from the creation and maintenance of device fingerprints. Objective: To our best knowledge, this is the first time that device for fingerprints had been generated in the form of images. The use of device fingerprints in image form best utilized the object recognition capabilities of capsule networks. Method: We designed a novel method to classify and save the network behaviour of IoT devices that are connected to a network. The proposed model was based on a two-fold innovation of the generation of unique images based on packet parameters of device transmissions, and the design of a model that could carry out efficient and accurate classification of device vendors based on their network behavior. Results and Conclusion: The generation of unique images offered a big advantage of saving the memory of the system. While a packet capture file may take around 150kb or more, the generated images were as small as the order of 2kb. For a smart system made up of thousands of devices, the order of memory space saved would become significant. Furthermore, since the algorithm of image generation could be customized by the network administrators, the images cannot be reverse- engineered by potential attackers, thereby assuring a secure way to save device behavior fingerprints. The developed model has compiled over 500 epochs that roughly translated to 100 minutes and gave the accuracy of the order of 92%. This was the first time that device network behaviour has been translated into an image and tested through classification using capsule networks. The translation of captured packet flows to black and white images not only saved on memory space but also provided a safeguard against reverse engineering by potential attackers. There is a vast scope to further use of this strategy to develop more complex device fingerprinting methods.

Inferring Device Interactions for Attack Path Discovery in Smart Home IoT

Abstract: In smart home Internet of Things (IoT) systems, interactions between devices are driven in two ways: automation applications (apps), and the physical channels (e.g., temperature, smoke). Meanwhile, device interactions can be maliciously exploited to launch IoT attacks. However, limited efforts explore whether it is feasible to discover potential exploitable device interactions from IoT deployments. This paper proposes a novel framework to detect interactions among devices (D-interact) from eavesdropped network traffic and device function descriptions, and discover all potential exploitable device interactions (i.t., attack paths). First, we use the packet-level patterns to fingerprint IoT device events and then identify all IoT device events from the eavesdropped traffic. Furthermore, we mine temporal and conditional dependencies of IoT events to infer device interactions introduced by IoT apps. Besides, to identify interactions between devices and physical channels, we use the natural language processing (NLP) technique to analyze device function descriptions. Based on the obtained device interactions, D-interact builds a device interaction graph to discover attack paths. To demonstrate the feasibility of our approach, we implement D-interact in a real-world smart home including 24 devices and 29 apps. The experiment results show that 38 device interactions are identified and 26 device interaction paths could be potentially exploited to impact the safety of the IoT environment.

Забезпечення якості підготовки кваліфікованих робітників з використанням smart-комплексів навчальних дисциплін

Abstract: Обґрунтовано методичні основи підготовки викладачів до створення SMART-комплексів навчальних дисциплін для підготовки кваліфікованих робітників (органічне поєднання гіпертексту та мультимедійної навчальної інформації; взаємодоповнення реальної івіртуальної складових освітнього середовища; відповідність освітнім професійним стандартам).Виявлені сильні (істотне розширення масштабів підготовки кваліфікованих робітників; невеликіфінансові вкладення закладів професійної (професійно-технічнох) освіти; можливістьінклюзивної освіти, персоналізоване навчання; оперативність обліку та контролю навчаннятощо) та слабкі (відсутність стратегії і тактики впровадження SMART-комплексів в освітнійпроцес; недостатній розвиток інформаційної інфраструктури в ряді регіонів; відсутність системинавчання викладачів з використанням SMART-комплексів; недостатнє інвестування в інноваційні інформаційні технології та інше) сторони розроблення й застосування SMART-комплексу у закладах професійної (професійно-технічної) освіти. Розроблено концептуальну модель SMART-комплексу навчальних дисциплін як інформаційно-динамічну системуелектронного освітнього ресурсу навчально-методичного спрямування, побудованої напостійному розвитку та зміні функціональних зв’язків і відношень. SMART-комплекснавчальних дисциплін має статичний, динамічний і середовищний компоненти; містить модулі:навчальний контент, систему його доставки до користувачів, систему підготовки та системуоцінювання учасників освітнього процесу. Створено методику підготовки викладачів дорозроблення SMART-комплексів для підготовки кваліфікованих робітників аграрної,будівельної і машинобудівної галузей, що охоплює шість етапів (організаційно-цільовий,змістовий, структурний, проєктувальний, узагальнювальний, процесуальний). Обґрунтованокритерії та показники оцінювання компетентності викладачів з розроблення та використанняSMART-комплексів.

A comparison on type-II fuzzy logic based data encryption techniques in the Internet of Things

Abstract: Smart gadgets have created a buzz in the market today; you will find everything smart today. Like a smartwatch, smart band, smart led, smart heater, etc., and transmitting data securely between all these devices is necessary as an outcome; IoT devices developed defenseless to numerous devices. Faith replicas were predictable, significant simultaneous approaches to defend a large communication system in contrast to evil virtual outbreaks. In this research paper, the various Type-II fuzzy logic models are evaluated, which provides enhanced data security for IoT devices. Also, compression is applied between all data encryption techniques based on the parameters like Reproduction time (circles), Program series (m), Quantity of device nodes, Number of spiteful nodes, and Total interval.

Key Extraction Using Ambient Sounds for Smart Devices

Abstract: To secure communications, this article presents a key extraction scheme for smart devices using ambient sounds. Specifically, it is designed for the scenario when smart devices do not have any pre-loaded secrets. Moreover, it can be implemented when smart devices have no access to the online trusted third party or the Network Time Protocol server. In our scheme, smart devices achieve synchronization by making use of ambient sounds. Then, they calculate and obtain the pairing distance and secure distance by applying a band-pass filter. Completing these operations, two smart devices can directly extract a communication key. We analyze the security of our scheme and evaluate the performance of it by implementing the scheme using a few off-the-shelf devices. The experimental results indicate that compared with related schemes, the bit generation rate of our scheme has significant improvement (increases at least 80%), and it can reach 312 bit/s.

Smart Living in the Digital Age

Abstract: The digital age is the age of smart systems. Smart living has already emerged as the conceptual hallmark of the digital future. We have we or will soon have smart homes, cities, and all sorts of smart interconnected objects. This paper deals, firstly, with the meaning of smart as related to the Greek concept of metis or cunning intelligence, the contexts of use being not only of human beings but also of gods, animals and artificial devices. The 19th century application of the concept referred to devices in general and in the 20th century to digital devices and systems in particular for which the leading sense is human intelligence. At present, it is not human but digital intelligence that leads the meaning of smart. Artificial smart systems receive their goals from the outside even if they can further develop such goals, giving the impression that they have conceived their goals on their own. They behave as if they were guided by a 'who' while in fact it is just a reified one, or a 'what'. The difference between who and what is the basis of ethical thinking in the age of smart systems.

Fully Decentralized Model by P2P Smart Contract To Achieve High Availability in IoT

Abstract: <p>In recent years, the number of IoT devices has increased substantially. One of the main concerns is the interaction with high availability. While peer-to-peer (P2P) technology has been used to achieve high availability by software running in P2P hardware, a single software still acts as the center for the interaction. If the software has bugs or is out of service, all devices are affected. Aiming at this issue, this paper proposes a fully decentralized model as a high-availability solution, in which P2P software runs on a P2P hardware environment. P2P software is a set of independent software from respective participants (IoT devices) that can work cooperatively without centralized software. We use blockchain as a P2P platform because it has P2P nodes and a trustless environment, so the P2P software in this paper is P2P smart contracts. To pair cooperative smart contracts, each P2P smart contract has requirements on other smart contracts. If several smart contracts matches the requirements mutually, they are treated as a work group (a topic), and then they process each other's requests within that group. When a single smart contract fails, the paired P2P software can be re-selected, resulting in high availability. Simulation results show that the proposed model can effectively eliminate the impact of the failure of any single smart contract.</p>

A Novel Trusted Boot Model for Embedded Smart Device without TPM

Abstract: Embedded smart devices are widely used in people’s life, and the security problems of embedded smart devices are becoming more and more prominent. Meanwhile lots of methods based on software have been presented to boot the system safely and ensure the security of the system execution environment. However, it is easy to attack and destroy the methods based on software, which will cause that the security of the system cannot be guaranteed. Trusted Computing Group proposed the method of using Trusted Platform Module (TPM) to authenticate the credibility of the platform, which can solve the disadvantages of using methods based on software to protect the system. However, due to the limited resource and volume of embedded smart devices, it is impossible to deploy TPM on embedded smart devices to ensure the security of the system operating environment. Therefore, a novel trusted boot model for embedded smart devices without TPM is proposed in this paper, in which a device with TPM provides trusted service to realize the trusted boot of embedded smart devices without TPM through the network and ensure the credibility of the system execution environment.

From Smart Life to Smart Life Engineering: A Systematic Mapping Study and Research Agenda

Abstract: The development of new digital technologies and their omnipresence impact the everyday life of people and intend to contribute to a so-called Smart Life. Numerous Smart Life applications and technologies relate directly to individuals and their life (Smart health, Smart buildings, Smart cities, etc.), but also indirectly through professional applications (Smart manufacturing, Smart grids, Smart farming, etc.). We assert that these applications have common foundations, and several design principles could be established to contextualize the development of Smart Life applications, thus, to introduce the field of Smart Life Engineering. We foresee the domain of Smart Life and Smart Life Engineering as an independent research domain that would contribute to the sharing or reuse of efforts to develop and maintain Smart applications. However, there is no common agreement on the concept of Smart Life and the associated methods and technologies. Thus, this paper provides a systematic view of the existing literature on Smart Life and its decomposition into sub-domains using systematic mapping study and presents a research agenda for Smart Life and Smart Life Engineering.

HomeMonitor: An Enhanced Device Event Detection Method for Smart Home Environment

Abstract: As more and more smart devices are deployed in homes, the communication between these smart home devices and elastic computing services may face some risks of privacy disclosure. Different device events (such as the camera on, video on, etc.) will generate different data traffic during communication. However, the current smart home system lacks monitoring of these device events, which may cause the disclosure of private data collected by these devices. In this paper, we present our device event monitor system, HomeMonitor. HomeMonitor runs in the OpenWRT system and supports complete event monitoring for smart home devices. HomeMoitor solves the problem that machine learning models for detecting device events do not scale flexibly. It uses the network packet size and the direction of the device event for unique identification during training. When detecting, it only needs to get the packet size and timestamp and then query the policy table for signature matching to control the device events. We evaluated the effectiveness of HomeMonitor, and the experiments show that the match rate of our method is 98.8%, the false positive rate is 1.8%, and the detection time is only 16.67% for PINBALL. The results mean that our method achieves the balance of applicable protocol scope, detection performance, and detection accuracy.

On Developing Human Centric Digital Tweens

Abstract: Purpose: The use of smart devices has increased greatly in the last ten years with users reaching out to the possibility to do more with them especially in the networking front. In this context there is a need to understand the connection between users&rsquo; social demographic factors and their way to related to their smart devices. Objective: This study was designed to evaluate the senso of belonging of a community in order to evaluate intangible benefits that employees may gain from a more immerse relationship with their devices. Method: We used a dataset of 586 anonymous respondent of an existing survey designed for capturing the relationships that humans develop with their smart devices. In particular, we investigate the relationships with smart device and particular background variables of the respondents using a chi-square test. Results: The study showed that there is a significant relationship between users&rsquo; sex and smart device type and their dependency on smart device. Male tends to think that smart device (in general) enables them to connect with a larger community. At the same time, female using smart phones feels more connected more to large community than when using other smart devices. Conclusion: This study provided several significant findings that confirm and strength previous literature works on the subject. In addition, socio demographics variables (like gender) as well as the type of smart device present a correlation between the smart device users and their tendency to stay in touch with a larger community.