Internet Devices Research Articles

Target set selection in social networks with tiered influence and activation thresholds.

Thanks to the mass adoption of internet and mobile devices, users of the social media can seamlessly and spontaneously connect with their friends, followers and followees. Consequently, social media networks have gradually become the major venue for broadcasting and relaying information, and is casting great influences on the people in many aspects of their daily lives. Thus locating those influential users in social media has become crucially important for the successes of many viral marketing, cyber security, politics, and safety-related applications. In this study, we address the problem through solving the tiered influence and activation thresholds target set selection problem, which is to find the seed nodes that can influence the most users within a limited time frame. Both the minimum influential seeds and maximum influence within budget problems are considered in this study. Besides, this study proposes several models exploiting different requirements on seed nodes selection, such as maximum activation, early activation and dynamic threshold. These time-indexed integer program models suffer from the computational difficulties due to the large numbers of binary variables to model influence actions at each time epoch. To address this challenge, this paper designs and leverages several efficient algorithms, i.e., Graph Partition, Nodes Selection, Greedy algorithm, recursive threshold back algorithm and two-stage approach in time, especially for large-scale networks. Computational results show that it is beneficial to apply either the breadth first search or depth first search greedy algorithms for the large instances. In addition, algorithms based on node selection methods perform better in the long-tailed networks.

Pyroelectric energy harvesting from power electronic substrates

Energy harvesting from available resources can lead to implementation of more controlling and actuating devices for industrial internet of things (IIoT). Energy harvesting from temperature fluctuations in power electronic devices by pyroelectric materials is a key solution to achieve self-powered sensor systems. In this study, performance of a pyroelectric energy harvester (PEH), utilized as a dielectric substrate in power electronic modules, is investigated. Accordingly, a comprehensive analytical model is developed in MATLAB software, and the theoretical results are validated with experimental data. The results of this study show that, the pyroelectric substrate can harvest an average power of 50 µW/cm2. Effect of amplitude and frequency of input heat, cooling density, and load resistance over PEH are studied. The results reveal that, frequency of the input heat rate higher than 2 Hz does not have a sensible impact on the power generation by the pyroelectric module. Convective cooling density with low heat transfer coefficients led to higher power generation. On the contrary, it can cause operating at higher temperature than the Curie temperature. The energy harvesting concept developed, for the first time, in this study shows significant potential of pyroelectric direct bonded copper substrate to provide self-powered sensors for IIoT in a vast range of power electronic applications.

Citizen-centric cybersecurity model for promoting good cybersecurity behaviour

ABSTRACT The ultimate success of cybersecurity partly depends on good behaviours by Internet users. However, good cybersecurity behaviour is a major challenge among citizens because they lack cybersecurity knowledge in the use of the Internet and digital devices and generally, there is a misalignment between attitudes and behaviour of people which creates bristly tension in cyberspace. In an endeavour to promote cybersecurity behaviour, attitudes and behaviour of Internet users should be combined with knowledge. This study builds from the Protection Motivation Theory (PMT) to feed into the development of a citizen-centric cybersecurity model for promoting good cybersecurity behaviour. The theory assumes that threat appraisals and coping appraisals are the two foundational mechanisms underlying behavioural actions when individuals are faced with threatening events; hence, it provided a solid foundation for the conceptual model of the study. The model was tested on data collected from 464 participants. Each of the three independent latent variables (Attitude, Behavioral Intention and Cybersecurity Knowledge) has a P-value less than 0.05 in our situation, indicating that each of the independent latent factors affects Behavior. The findings of this study make a significant contribution both to theory and practice to implement a citizen-centric cybersecurity model.

The Role of Information Technology in Z Generation Behavior Change in Dealing with Conflict

Developments in information technology such as the Internet, social media, and mobile devices have provided broad and immediate access to information and social interaction. However, the impact of information technology on the behavior of the Z Generation in dealing with the conflict still needs to be studied in depth. This study aims to explore the role of information technology in changing the behavior of the Z Generation in dealing with conflict. The Z Generation, which consists of individuals born between 1995 and 2010, grows and develops amidst the rapid advances in information technology. This study will use a qualitative approach by collecting data through in-depth interviews and participatory observation. Research participants will consist of Z Generation individuals who actively use information technology in their daily lives. This study will analyze the role of information technology in changing the behavior of the Z Generation in dealing with conflict, both in personal, social, and environmental contexts. Factors such as fast access to information, the influence of social media, and the ability to communicate virtually will be explored in relation to changes in the behavior of the Z Generation. It is hoped that the results of this research will provide better insight into how information technology influences the way Z Generation deals with conflict, as well as the implications of the change in behavior. The results of this research are expected to contribute to our understanding of how information technology can be used effectively in supporting the social and personal development of the Z Generation in dealing with conflicts in the digital era.

IMPLEMENTASI ALGORITMA TF-IDF DAN SUPPORT VECTOR MACHINE TERHADAP ANALISIS PENDETEKSI KOMENTAR CYBERBULLYING DI MEDIA SOSIAL TIKTOK

Cyberbullying is the act of sending text, images, or videos using the internet, mobile phones, or other devices with the aim of hurting and shaming other people. Cyberbullying is often done through several social media platforms, one of which is through comments on the TikTok application. According to a report by We Are Social, TikTok has 1.4 billion monthly active users aged 18 and above globally. Indonesia currently ranks second in the world in terms of active TikTok users. As a result, the potential for cyberbullying instances will grow as the number of users grows. By using data mining, the public can create a detection system, which can perform analysis on comments in the TikTok application. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The stages passed are to collect comments that are labelled manually. Then, text preprocessing, tokenizing, and weighting were carried out with TF-IDF. Then, implement the Support Vector Machine algorithm to detect cyberbullying comments. This study uses 80% training data and 20% testing data. From the performance results of the Support Vector Machine algorithm, 88% overall accuracy, 88% precision, 96% recall, and 92% f1-score were obtained in detecting cyberbullying comments on social media TikTok.

Digital tools for delivery of dementia education for caregivers of persons with dementia: A systematic review and meta-analysis of impact on caregiver distress and depressive symptoms.

Continuing education for dementia has been shown to be beneficial by improving informal caregiver knowledge, dementia care, management, and caregiver physical and mental health. Technology-based dementia education has been noted to have equivalent effects as in-person education, but with the added benefit of asynchronous and/or remote delivery, which increases accessibility. Using Cochrane review methodology, this study systematically reviewed the literature on technology-based dementia education and its impacts on caregivers. Technology-based delivery included dementia education delivered via the Internet, telephone, telehealth, videophone, computer, or digital video device (DVD). In the review, twenty-eight studies were identified with fourteen included in a meta-analysis, and these data revealed a significant small effect of technologically based dementia education on reducing caregiver depression, and a medium effect on reducing caregiver distress in response to caregivers' observations of behavioral problems displayed by persons with dementia. No evidence was found for a significant effect of the educational intervention on caregiver burden or self-efficacy, which are known to be gendered aspects of caregiving. None of the studies included in the meta-analysis reported separate outcomes for male and female care providers, which has implications for gendered caregiving norms and aspects of care. Registration number: PROSPERO 2018 CRD42018092599.

Clinical Experience of Using Telemedicine for the Management of Patients Using Continuous Subcutaneous Insulin Infusion in a Highly Complex Latin American Hospital

Continuous subcutaneous insulin infusion (CSII) has emerged as a potential solution for diabetes management during the pandemic, as it reduces the need for in-person visits and allows for remote monitoring of patients. Telemedicine has also become increasingly important in the management of diabetes during the pandemic, as it allows healthcare providers to provide remote consultations and support. Here, we discuss the implications of this approach for diabetes management beyond the pandemic, including the potential for increased access to care and improved patient outcomes. We performed a longitudinal observational study between 1 March 2020 and 31 December 2020 to evaluate glycemic parameters in diabetic patients with CSII in a telehealth service. Glycemic parameters were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability control. A total of 36 patients were included in the study, with 29 having type 1 diabetes and 6 having type 2 diabetes. The study found that the proportion of patients achieving target glucose variability and GMI remained unchanged during follow-up. However, in patients with type 2 diabetes, the time in target range increased from 70% to 80%, and the time in hyperglycemia decreased from 2% to 0%. The results of this study suggest that telemedicine is a strategy for maintaining glycemic control in patients using CSII. However, the lack of access to the internet and adequate telemonitoring devices make it difficult to use on a large scale in emerging countries like ours.

The Meaning of Life and its Influence on Adolescent Mobile Phone Addiction: An Exploratory Study

With the popularization of mobile Internet devices, more and more teenagers use smartphones. The use of mobile phones can not only bring convenience to young people, but at the same time, improper use and dependence may also lead to the risk of addiction and harm the physical and mental health of young people. With the development and influence of positive psychology, the meaning of life plays an important role in preventing adolescents' psychological and behavioural problems. Individuals with a high sense of life are less likely to become addicted to mobile phones. Based on this, it is possible to prevent and alleviate the problem of mobile phone addiction by enhancing the sense of life, improving self-control, and paying attention to positive psychology and group counselling interventions.

Implementation of Machine Learning and Deep Learning for Securing the Devices in IOT Systems

Objectives: To fix the security issues of devices in internet of things (IOT) systems that arise when Machine learning (ML) and Deep Learning algorithm (DLA) are implemented in the IOT systems. Methods: Each packet of IoT threats has been filtered by using suitable attack model for primary attack detection. In deep learning, network traffic field’s information is extracted from of packet and is used as the training features. Attack is proposed to be detected by the use of database-based features of attack. If attack is found then packet will be discarded along with update feedback to filtering stage for primary attack detection. Knowledge Discovery and Data Mining (KDD) dataset is used in this study. Different types of Mirai attack are considered for classification. The performance of the proposed method has been compared in terms of accuracy and execution time with earlier work using traditional ML-DL methods. Findings: The proposed technique is seen to achieve high accuracy level at the least computational time, concurrently with much higher recall and G-mean values Several algorithms are used to secure the IOT devices from various types of threats and a comparison is depicted with their accuracy and execution time. Results show that the proposed methodology using Convolution neural network (CNN) for classification, can achieve the accuracy ~0.9976 with execution time1.30 sec and G-mean 0.7865 only. Novelty: The proposed method is essentially a judiciously configured ML- DL technique which is novel and exhibits better performance in terms of mitigating IoT threats. Keywords: IOT devices and threats; Machine Learning; Deep Learning; KDD; Security and privacy

The Effect of The Corona Virus Disease of 2019 (COVID-19) Pandemic on The Learning Situation

The global pandemic of Corona Virus Disease of 2019 (COVID-19) adversely affected the various aspects of human life. There was a severe impact of the COVID-19 pandemic in the academic years 2076, 2077, and 2078 BS in the Nepalese education system. Though other sectors of the nation have been affected by such a pandemic, significant influence of it has been seen in the education sector. In this regard, this paper aims to explore the effect of the Corona Virus Disease of 2019 (COVID-19) pandemic on classroom instruction and to find out the challenges of virtual and face-to-face modes of school education. This research work follows qualitative design. Purposive sampling method was used to select the participants and the data were collected through open ended interviews, observation checklists, and focus group discussion. The analysis has been made in thematic way following the rigor of qualitative research. The study shows a great effects of the Corona Virus Disease of 2019 (COVID-19) pandemic on the school education of Gorkha municipality. The virtual mode of instruction has been seen less effective during COVID-19 pandemic situation in the study area. Physical classes were effectively conducted before and after the pandemic. Several challenges have been noted in the virtual mode of instruction. They include the lack of internet access, limited access to electronic devices, and a lack of electricity in the district. The findings of the study entail that the blended mode could be more suitable to the students studying in the schools of Gorkha Municipality which could create opportunities for face-to-face and virtual learning together if they can be supported with easy access to the internet and electronic devices.

Implementation of IPv6 using 6RD Method on Power Line Communication Network

The development of computer network technology is rapidly advancing in response to society's increasing demand for network services. Currently, the most widely used network protocol is IPv4, but it has some limitations. To address these limitations, a new network protocol, IPv6, has been developed. More and more new network devices are using IPv6 as their IP address, while many existing technologies and internet devices still use IPv4. Therefore, a mechanism is required to transition from IPv4 to IPv6. The 6RD tunneling method is used in Power Line Communication networks to connect two or more network devices using electrical wiring as a data transmission medium. The Power Line is equipped with a charger for a laptop and a drill, creating a load to test the performance of the Quality of Service(QoS) with the throughput parameter. The measured Rerate throughput upload was 11800 Kbit/s without any additional electrical load, 9377.8 Kbit/s with a laptop charger as an additional load, and 6082.2 Kbit/s with a drill as an additional load. The Rerate throughput download was 8098.8 Kbit/s without any additional electrical load, 7782.8 Kbit/s with a laptop charger as an additional load, and 5996 Kbit/s with a laptop charger and a drill as additional loads. With this transition mechanism, IPv4 can communicate with IPv6, allowing for more efficient network communication.

Multilevel Attention-Based Sample Correlations for Knowledge Distillation

Recently, model compression has been widely used for the deployment of cumbersome deep models on resource-limited edge devices in the performance-demanding industrial Internet of Things (IoT) scenarios. As a simple yet effective model compression technique, knowledge distillation (KD) aims to transfer the knowledge (e.g., sample relationships as the relational knowledge) from a large teacher model to a small student model. However, existing relational knowledge distillation methods usually build sample correlations directly from the feature maps at a certain middle layer in deep neural networks, which tends to overfit the feature maps of the teacher model and fails to address the most important sample regions. Inspired by this, we argue that the characteristics of important regions are of great importance and thus introduce attention maps to construct sample correlations for knowledge distillation. Specifically, with attention maps from multiple middle layers, attention-based sample correlations are newly built upon the most informative sample regions, and can be used as an effective and novel relational knowledge for knowledge distillation. We refer to the proposed method as multi-level attention-based sample correlations for knowledge distillation (or MASCKD). We perform extensive experiments on popular knowledge distillation datasets for image classification, image retrieval, and person re-identification, where the experimental results demonstrate the effectiveness of the proposed method for relational knowledge distillation.

ASAA: Multihop and Multiuser Channel Hopping Protocols for Cognitive-Radio-Enabled Internet of Things

The devices of Internet of Things (IoT) are integrated and interconnected by using the traditional wireless communication technology. Unavailability of spectrum sharing occurs in traditional wireless communication due to the presence of a massive number of IoT devices. Thus, Cognitive Radio Network (CRN) is introduced as a promising technology to utilize the spectrum efficiently. Channel Hopping Sequence (CHS) is used to establish communication among CRN users. However, the majority of CHS mechanisms only focus on multi-user single-hop scenarios, which can lead to bottleneck and throughput degradation problems. It is also found that few existing CHS mechanisms still have a low percentage of rendezvous that can cause difficulties for the Secondary Users (SUs) to communicate. Thus, it is a challenge to design efficient CHS for establishing fast communication among SUs under multi-user, multi-hop scenarios and asymmetric asynchronous environments. In this paper, Asymmetric Synchronous and Asymmetric Asynchronous (ASAA) Channel Hopping (CH) algorithms are designed for the multi-user CRN-enabled IoT devices to share the unused spectrum in the multi-hop scenario. The Multi-User Asymmetric Synchronous (MUAS) and Multi-User Asymmetric Asynchronous (MUAA) protocols are designed in the proposed ASAA channel hopping mechanism. The simulation results show that the proposed ASAA CHS algorithms outperform the existing CHS mechanisms in terms of throughput, Channel Loading (CL), Channel Utilization (CU), Maximum Time To Rendezvous (MTTR), Average Time to Rendezvous (ATTR) and Maximum Inter Rendezvous Interval (MIRI).

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.

Internal Factors Influencing Vulnerability to Telecom Fraud

With the popularity of the Internet and electronic devices, the rate of telecommunication fraud is getting higher and higher. Most of the current research focuses on the prediction of telecom fraud through machine learning. This study, on the other hand, focuses on the people who have been defrauded and finds out how people's internal factors "cognitive flexibility", "critical thinking", "material desires" and social attributes influence "vulnerability to Telecom Fraud", trying to find out which mental characteristics make people more vulnerable to telecom fraud. This study uses SPSS 25.0 software for data analysis and draws results and conclusions through the methods of questionnaire survey and hypothesis testing. It discovers both cognitive flexibility and critical thinking are negatively correlated with vulnerability to telecom fraud, meaning that people with more cognitive flexibility and critical thinking were less likely to be telecom scammed. It also finds that people with higher disposable income are also more vulnerable to wire scams. Finally, this study discusses the discovered insights and put forward constructive suggestions for scholars and related departments.

Unplugging for Student Success: Examining the Benefits of Disconnecting from Technology during COVID-19 Education for Emergency Planning

The COVID-19 outbreak revealed the fragility not only of our species but also of society, which has demonstrated its ability to adapt to challenging conditions and to learn from disasters. One of the most pressing issues during the pandemic was the delivery of education during lockdowns. Education in emergency was established using various communication media, hastening the digitalization of education. However, this also highlighted the impact on the mental health of students, who were already experiencing overuse of the internet and electronic devices prior to the pandemic. In response, Tecnologico de Monterrey, a private Mexican university that also offers high school programs, launched the “Unplugged Day” initiative, which encouraged students to disconnect from electronic devices and participate in physical, cultural, creative, or reflective activities. This study applied a voluntary survey to 1850 students from March to May 2021 on a weekly basis, with the aim of analyzing symptoms of insomnia, emotions, perceptions of online education, sources of stress, and the need for professional support to manage their emotions in relation to their participation in Unplugged Day activities. Our results, obtained through a quantitative methodology, confirmed the impact of the emergency, lockdown, and forced remote education on the mental health of students. Furthermore, the results revealed that the Unplugged Day initiative is a strategy that promoted students’ well-being during online education. The respondents also suggested strategies for promoting mental health and well-being of learners, whether in an emergency or not. These findings provide valuable information for governments and educational institutions to implement policies and strategies for planning education in emergency situations and for addressing the ongoing global problems of stress and mental health, which are related to the increasing use of electronic devices and the volatility, uncertainty, complexity, and ambiguity of global society. It is our responsibility to improve education so that it can play its role in preserving cultural heritage, overcoming adversity, rebuilding after catastrophes, and shaping a better future for generations to come.

Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach

As a new programming paradigm, neural-network-based machine learning has expanded its application to many real-world problems. Due to the black-box nature of neural networks, verifying and explaining their behavior are becoming increasingly important, especially when they are deployed in safety-critical applications. Existing verification work mostly focuses on qualitative verification, which asks whether there exists an input (in a specified region) for a neural network such that a property (e.g., local robustness) is violated. However, in many practical applications, such an (adversarial) input almost surely exists, which makes a qualitative answer less meaningful. In this work, we study a more interesting yet more challenging problem, i.e.,quantitativeverification of neural networks, which asks how often a property is satisfied or violated. We target binarized neural networks (BNNs), the 1-bit quantization of general neural networks. BNNs have attracted increasing attention in deep learning recently, as they can drastically reduce memory storage and execution time with bit-wise operations, which is crucial in recourse-constrained scenarios, e.g., embedded devices for Internet of Things. Toward quantitative verification of BNNs, we propose a novel algorithmic approach for encoding BNNs as Binary Decision Diagrams (BDDs), a widely studied model in formal verification and knowledge representation. By exploiting the internal structure of the BNNs, our encoding translates the input-output relation of blocks in BNNs to cardinality constraints, which are then encoded by BDDs. Based on the new BDD encoding, we develop a quantitative verification framework for BNNs where precise and comprehensive analysis of BNNs can be performed. To improve the scalability of BDD encoding, we also investigate parallelization strategies at various levels. We demonstrate applications of our framework by providing quantitative robustness verification and interpretability for BNNs. An extensive experimental evaluation confirms the effectiveness and efficiency of our approach.

Distance learning during COVID-19 school closures

PurposeThe authors aim to analyze the inequality in accessing distance learning during COVID-19 school closures.Design/methodology/approachThe authors use the Household Pulse Survey, which is an effort by the United States (US) government to measure the well-being of American families during the COVID-19 pandemic. The authors employ a regression analysis to estimate the inequality in accessing distance learning by race and household income.FindingsDisadvantaged children from nonwhite and low-income families have much less access to distance learning, including less access to online classes, digital devices and the Internet. Schools are critical providers of the Internet and digital devices to children from disadvantaged households. Schools and parents devote more attention to these nonwhite children by spending extra time on their learning activities.Originality/valueThis paper estimates the inequality in accessing distance learning during the COVID-19 crisis. Also, the authors analyze the responses of schools and parents toward this inequality.

A system model for real-time monitoring and geospatial data for the simulation of surveillance of COVID-19 in Makassar, Indonesia

The rapid spread of COVID-19 requires rapid management. Prompt treatment is needed to prevent the spread of this disease, which could be minimized or isolated in one place so that it does not spread to other places. This study was conducted to discover a model of the surveillance system in real time and to analyze the change in its distribution pattern. This study was conducted in the city of Makassar, South Sulawesi, Indonesia, involving 30 volunteers. Two devices were used, the Internet reverse transcription loop-mediated isothermal amplification (iRTLAMP) and IoT button application, to provide spatial data in the form of patient points exposed to COVID-19. Furthermore, three scenarios were applied to see the pattern of data distribution. The data recorded in the cloud database were retrieved with a created application and then analyzed using Kernel Density Estimation (KDE) and Point Pattern Analysis (PPA) to observe the distribution of patterns in real time. The analysis utilizing KDE with the Gaussian kernel function as the kernel revealed significant changes in the probability distribution, which could be seen from color changes in the map. The centrographic analysis revealed that the mean and median points of the three scenarios changed in various ways within approximately 700 m to 1.7 km. Meanwhile, the radius of minimal bounding circle behaved similarly and appeared to change depending on the scenario, from a radius of 5.57 (initial) km to 6.55 km (scenario 1), 5.57 km (scenario 2) and 6.22 km (scenario 3). The standard distance also showed a change from 4.53 km to 4.60 km (scenario 1), 4.70 km (scenario 2) and 5.40 km (scenario 3). Simulations carried out using the developed system showed that the use of internet devices could help monitor people exposed to COVID-19 by changing patterns and distribution points. Therefore, decision makers could take preventive actions earlier so that this disease does not spread quickly.

HARDWARE SECURITY OVERVIEW OF FOG COMPUTING END DEVICES IN THE INTERNET OF THINGS

The subject of the study is possible means of increasing the hardware security of end devices of fog computing in Internet of Things (IoT) networks, the spread of which is growing rapidly every year and requires a high level of of protection against all types of attacks. The goal of the work is to review available COTS (commercial off-the-shelf) and/or conceptual hardware solutions for protecting low-end devices in Internet of Things networks based on fog technologies. To achieve the goal, the following tasks were solved: the concept of fog computing and the advantages it will bring to IoT networks are presented; cyber threats and hardware attacks on IoT networks are considered; the consequences of using IoT networks based on fog computing are presented; hardware security tools such as TRM, PUF, HSM, etc. are considered. When performing the tasks, such research methods were used as: theoretical analysis of literary sources; comparative analysis of cloud, fog and mobile computing; analysis of existing security hardware. The following results were obtained: fog computing provides most of the advantages of cloud computing by additionally allowing data to be processed on end devices without burdening the central server. Conclusions: hardware security in IoT systems is no less important than software security. This issue is especially important for systems based on fog computing, where data will be processed on the periphery, without being transferred to the cloud. To increase the level of hardware security of fog computing devices, it is suggested to use standard hardware security platforms, such as: Physically Unclonable Functions, Hardware Security Module, System On a Chip, etc. The hardware components of the system using fog computing are less prone to cyber-attacks/hacking/intrusions/manipulation.