Edge-computing-driven Internet of Things: A Survey

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or “things” that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the “IoT Security Dataset”, a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset “Opiner”, supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security. The dataset can be found at— https://anonymous.4open.science/r/IoT-Security-Dataset-8E35 .

Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.

Investigation on big data evaluation and visualization of internet of things based on edge computing

This paper is a survey about the uprising of the Internet of Things (IoT) technologies, their evolution, their variety of the application domains of the IoT systems, which includes, but not limits to healthcare domain, smart cities, smart objects, integration of smart sensors, smart home appliances control, etc. and all the technology revolution that this evolution implies and forces to happen. The IoT system's revolution and fast-phased evolution will create and/or cause positive effects in the whole society and technology, like in the case when the IoT domain is redesigning the applied modern healthcare systems, promising and later offering technological, economic, and social benefits. This paper also reviews the state-of-the-art network architectures/platforms employed in the IoT systems, presents different applications, and also the future trends. IoT security and privacy features, including security requirements will be also discussed and presented. Ambient intelligence, and wearables can be leveraged in the IoT healthcare context, sensors are also becoming ubiquitous and very important in everyday life and also generates data at an unprecedented, unseen rate and scale. This article focuses on the current/existing trends in the domain of IoT and on the current stage of development of research for hardware and software solutions, possible future communication capabilities between human beings and machines, and any new technologies that will employ IoT. Future researches on IoT-based systems are based on a set of open issues and challenges; also, there is the vital step from current "Big Data" to "Big Information" that can be improved and faster created with the help and implication of the current and future IoT systems/setups. With the proliferation of IoT, humans will be able to interact with machines/computers and also control a variety of networked electronic devices, including sensors and actuators. Also, the emergence and evolution of wearable Brain-Machine Interfaces (BMIs), employing directly Electroencephalographyc (EEG) signals, can be a key catalyser of this interaction and evolution of IoT. As smart home appliances, smart sensors, smart glasses and wearable EEG/EMG headsets and any other smart objects are appearing in our lives every day, all of them connected or being a member of the IoT systems, it is important to be able to foresee what is lying ahead of us, as a society in view of the fact that their number grows every day. As the gathered data is growing each day, the computation power from the available IoT devices and infrastructure are becoming more important and more utilized.

With the proliferation of Internet of Things (IoT) devices, cybersecurity has become an increasingly pressing concern. Due to their inherent vulnerabilities and the severity of the threats posed by malware, robust malware detection strategies for IoT devices are in high demand. However, the constraints imposed by the limited memory and processing power of IoT devices, alongside with the varied nature of these devices, have limited the development of effective malware detection strategies. Existing solutions primarily use either local computation or cloud-based services, each with its own set of challenges. In this paper, we propose a hybrid malware detection method that leverages both local and cloud-based computation to address these challenges. This method, based on similarity computations, optimizes the trade-off between computational resources and latency, offering an effective approach for malware detection in IoT devices. Through experimental evaluation using a substantial IoT malware dataset, we demonstrate that our proposed approach achieves an optimal balance between local and cloud-based computations, providing a promising solution for malware detection in IoT context.

With rapid advancements in the technology, almost all the devices around are becoming smart and contribute to the Internet of Things (IoT) network. When a new IoT device is added to the network, it is important to verify the authenticity of the device before allowing it to communicate with the network. Hence, access control is a crucial security mechanism that allows only the authenticated node to become the part of the network. An access control mechanism also supports confidentiality, by establishing a session key that accomplishes secure communications in open public channels. Recently, blockchain has been implemented in access control protocols to provide a better security mechanism. The foundation of this survey article is laid on IoT, where a detailed description on IoT, its architecture and applications is provided. Further, various security challenges and issues, security attacks possible in IoT and their countermeasures are also provided. We emphasize on the blockchain technology and its evolution in IoT. A detailed description on existing consensus mechanisms and how blockchain can be used to overpower IoT vulnerabilities is highlighted. Moreover, we provide a comprehensive description on access control protocols. The protocols are classified into certificate-based, certificate-less and blockchain-based access control mechanisms for better understanding. We then elaborate on each use case like smart home, smart grid, health care and smart agriculture while describing access control mechanisms. The detailed description not only explains the implementation of the access mechanism, but also gives a wider vision on IoT applications. Next, a rigorous comparative analysis is performed to showcase the efficiency of all protocols in terms of computation and communication costs. Finally, we discuss open research issues and challenges in a blockchain-envisioned IoT network.

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

Chapter 28 - Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

Статья посвящена обзору современных тенденций телекоммуникационной отрасли и показана их реализация в активном сетевом оборудовании компании D-Link. Появление цифровых сервисов способствует разработке и внедрению новых технологий, которые обеспечивают эффективность экономики и улучшают качество жизни. Клиентские приложения и устройства постоянно развиваются. Они позволяют не только просматривать Web-страницы, но и пользоваться сервисами IP-телефонии, видеоконференций, передавать мультимедийные потоки данных. В связи с возрастанием объема трафика и уменьшением его асимметричности, повышаются требования к производительности проводной и беспроводной инфраструктуры сети доступа. Развитие Интернета вещей (IoT) в различных областях жизни позволяет мгновенно получать и обрабатывать огромное количество данных. Это открывает возможности для управления различными ресурсами и предотвращения каких-либо нарушений. В свою очередь, для эффективной работы устройств IoT требуются технологии увеличивающие время жизни батарей, т.к. они, как правило, не оснащены блоком питания. Передача данных от устройств IoT выполняется через беспроводную среду передачи. Технологии 5G и 802.11ax открывают новые возможности для сетей IoT. Для снижения задержек на обработку запросов, нагрузки на каналы связи и вычислительные мощности центров обработки данных, развивается концепция граничных вычислений (Edge Computing). Ее основной идеей является обработка информации не в централизованной вычислительной среде, а в непосредственной близости от источников первичных данных («умных» датчиков, IP-камер, иных устройств IoT). Сеть имеет большое значение при реализации бизнес-процессов и образовательных процессов в организации. Компания D-Link предлагает образовательную программу, которая позволит изучить технологии современных сетей и научиться с ними работать. The paper is devoted to a review of current trends in the telecommunications industry and shows their realization in the active network equipment of D-Link. The emergence of digital services contributes to the development and implementation of new technologies that ensure the efficiency of the economy and improve the quality of life. Client applications and devices are constantly evolving. They allow not only viewing Web pages, but also using the services of IP-telephony, video conferencing, transmitting multimedia data streams. Due to the increase of traffic volume and the decrease of its asymmetry, the performance requirements for wired and wireless access network infrastructure are increasing. Evolving of the Internet of Things (IoT) in various areas of life allows instantly receiving and processing a huge amount of data. This opens up opportunities for managing various resources and preventing any disruption. Consequently, for the effective operation of IoT devices, technologies that increase battery life are required, because they are usually not equipped with a power supply. Data from IoT devices is transmitted through a wireless transmission medium. 5G and 802.11ax technologies open up new possibilities for IoT networks. To reduce delays in processing requests, the load on communication channels and the processing power of data centers, the concept of Edge Computing is being developed. Its main idea is to process information not in a centralized computing environment, but in the immediate vicinity of primary data sources (smart sensors, IP cameras, other IoT devices). The network is important in the implementation of business processes and educational processes in the organization. D-Link Company offers educational programs which will allow learning the technologies of modern networks and studying how to work with them.

ABSTRACTIn recent years, the emerging development of Internet of things (IoT) has created new opportunities for IoT service provider, device manufacturer and customer (end user). In agriculture, employing IoT can help reducing manpower as well as standardizing agricultural processes, thus increasing the benefit for customer who does agricultural business. Beside the opportunities, there are also issues while doing maintenance for an agricultural IoT system. One of them is how to automate the process to detect faults of IoT devices in a large deployment area. While previous studies focus on investigating algorithms as well as theoretical model to detect faults for specific IoT devices, automatically expanding of IoT management process for detecting faults of sensor/actuator has not been investigated yet. To achieve this, we should be able to deal with the relationship between IoT device manufacturer, service provider and customer. In this study, we conceptualize the knowledge of IoT devices, management process and fault detection requirement. To realize this model, we introduce a runtime system to expand the IoT management process for detecting sensor/actuator faults which is able to provide the above features. We do several experiments to show the effectiveness and usefulness of the proposed solution.

Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.