Classification of Edge Applications using Decision Tree, K-NN, & SVM Classifier

In recent years, AIoT (Artificial Intelligence of Things) applications have received lots of attention since it exploits widely deployed IoT devices to collect data and perform an action while leveraging AI to obtain knowledge and insights. When deploying AIoT in 5G networks, Multi-access Edge Computing (MEC) is appropriate for enabling local management of IoT devices and computation of machine learning (ML) algorithms. Facing the multitude of threats in the ML data layer, IoT service layer, and 5G communications layer, MEC should enable corresponding detection and mitigation schemes to protect AIoT applications. In this article, we examine the existing security solutions located in each layer and discuss the interrelated challenges. For example, a network-traffic-based IDS solution in MEC might capture IoT malware but fail to identify a growing file-less attack. We suggest that firmware emulation in IoT endpoint should be included to provide system-level behaviors to network-based detectors in MEC so that file-less attacks can be distinguished. The potential of a backdoor attack aiming to poison data or corrupt ML models cannot be ignored in AIoT applications, and a corresponding detector should be implemented in MEC. Due to the rise of low-cost Software-Defined Radio (SDR), malicious attacks using rogue base stations (BSs) have become more popular. It implies that security protection at MEC in the communications layer is necessary. This article, therefore, proposes a novel platform, M3Inspector, where inspectors located in mobiles and AIoT machines collect information from surrounding BSs and provide them to MEC. MEC determines the rogue BS and makes a notification to users subscribing to the local service. A realistic 5G experimental platform with rogue BSs is developed. The results demonstrate that attack detection and mitigation can be implemented in the MEC paradigm to significantly improve the security protection of AIoT from the perspectives of rogue BS attacks and file-less attacks.

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

Mobile Edge Computing (MEC) frameworks improve real-time data processing and system scalability by making networked game AI NPCs more responsive and flexible. MEC-based frameworks are tested for latency reduction and NPC real-time performance in complex and dynamic environments. Simulated and real-life user experiments evaluated the proposed system's response times, accuracy, and latency. Python simulations of network settings with different NPC concentrations and complexity produced massive datasets. NPC behaviour feedback was collected from 100 diverse users of various ages, genders, gaming experiences, and preferences. In low- to medium-density scenarios, the edge computing framework improved NPC responsiveness with low latency and high accuracy, enhancing player immersion. Due to the environment's complexity and NPC density, response times increased and accuracy decreased, requiring further optimisation for harsher conditions. Despite bugs and repetitive behaviours that suggested the Likert scale could be improved, the qualitative results praised the NPCs' lively conversation and realistic movements. Edge computing improves game AI and NPC realism with adaptive responses and real-time data processing. Scaling NPC densities and integrating edge computing with game architectures require more research. Next, improve NPC AI algorithms, reduce computational complexity and scalability, and expand testing environment game scenarios. Edge computing and AI techniques like deep learning and natural language processing can create immersive and engaging gaming experiences. This may present new gaming industry challenges and opportunities for innovation. Edge computing's real-time data processing and adaptive responses may change video game non-player characters.

Health Information System (HIS) is a combination of hardware, software, process, infrastructure, people and protocol which is developed for management of healthcare data including collection, storage, analysis, decision making and transmission. It supports functional management of a hospital as well as provides an infrastructure that support policy decisions over healthcare data. HIS grown decade by decade starting from 1960 to present era and divided into four generation versions, starting from HIS. V.1.0 to HIS V.4.0. HIS V.4.0 is dedicated for smart medical technology with fog-based computing system including disease-oriented sensing system, IoT, artificial intelligence, high speed internet and advanced internet protocols. Fog Computing is a distributed computing framework and provide an extension to cloud computing. It lies between medical sensor (IoT devices) and cloud datacenters. This paper presents various HIS generations and technologies used in HIS V.4.0. Include review of cloud computing, IoT and Fog computing. Further this paper provides a systematic review of existing research in fog computing with various issue and challenges. Finally, paper describes comparison of fog computing with cloud and edge computing and present discussion, open challenges and recommendation in Fog-based HIS.

Данная статья посвящена выявлению актуальной задачи исследования в рамках темы «Краевые вычисления для цифровизации энергетики». Связанность краевых вычислений с задачами энергетики показана на основе краткого библиометрического анализа данных следующих платформ: Scopus, dimensions.ai, IEEE Xplore, The Lens. Доминирующим применением краевых вычислений является цифровизация интеллектуальных энергетических сетей. Краевые технологии направлены на перенос части облачных вычислений в непосредственную близость к краевым устройствам, генерирующим исходные данные. Краевые вычисления могут способствовать улучшению параметров энергопотребления, конфиденциальности и использованию полосы пропускания систем Интернета вещей. Исследования в области краевых вычислений для задач энергетики в основном финансируются китайскими и европейскими организациями. В данной работе выявление перспективной задачи исследования проводилось только по авторским ключевым словам. По результатам анализа публикаций, представленных в Scopus, перспективной задачей применения краевых вычислений является трансактивная энергетика, которая использует экономические и регулятивные механизмы для динамического балансирования спроса и предложения в электрической сети. Нарождающимся трендом является тема краевых энергетических сетей и формирование децентрализованных энергетических сообществ. This paper is dedicated to the identification of the current research problem within the topic "Edge Computing for Energy Digitization". The relevance of Edge Computing to energy challenges is demonstrated by a brief bibliometric analysis of data from the following platforms: Scopus, dimensions.ai, IEEE Xplore, The Lens. The dominant application of edge computing is the digitization of smart energy networks. Edge technologies aim to move some of the cloud computing closer to the edge devices that generate the raw data. Edge computing can help improve energy consumption, privacy, and bandwidth utilization of IoT systems. Research on edge computing for energy problems is mainly funded by Chinese and European organizations. In this paper, the identification of the promising research problem was done only by author keywords. According to the analysis of publications in Scopus, a promising application of edge computing is transactional energy, which uses economic and regulatory mechanisms to dynamically balance supply and demand in the electricity grid. An emerging trend is the topic of edge energy networks and the formation of decentralized energy communities.

This study focuses on the security of 5G mobile network major technology called Multi-Access Edge Computing (MEC) and its susceptibility to distributed denial of service (DDoS) attacks. The goal of the research is to address the effects of DDoS attacks and implement effective mitigation techniques. Several supervised Machine Learning (ML) techniques, which include Random Forest (RF), Decision Tree, Naïve Bayes, K-Nearest Neighbour, Logistics Regression, and Blending/Stack Model, are evaluated using multiple performance metrics such as accuracy, detection/recall, F1-Measure, Matthew’s correlation coefficient, Receiver Operating Characteristic, and Area Under Receiver Operating Characteristic. According to literature, ML algorithms achieve the best performance in mitigating DDoS attacks, therefore, they can be optimized to enhance their effectiveness. The research provides an overview of the existing mitigation schemes in the MEC and proposes a DDoS mitigation scheme. The findings show that hybrid models outperformed traditional ML models. Among the mitigation techniques evaluated, RF proved to be the most effective in mitigating DDoS attacks in MEC.

Mobile devices have certain restrictions such as processing power and network bandwidth. Edge computing can reduce load and delay by migrating the parts of application processing from mobile devices to edge servers. Edge computing is a method that extends cloud computing to the edge of network. Conventional schemes of face recognition based on edge computing can reduce the amount of network transmission data between the edge servers and the cloud servers by preprocessing data on the edge servers. However, the schemes execute each task on a certain resource, thus application response time may increase according to the conditions of computational resources and network. Hence, a task placement system considering the conditions of computational resources and network is necessary. This paper proposes a task placement system for face recognition applications in edge computing. The proposed system calculates estimated response time of possible task placements based on the task placement decision formula. The formula parameterizes power and CPU utilization of computational resources and delay and bandwidth of network. The task placement that minimizes estimated response time is determined. We implemented a prototype of the proposed task placement system and evaluated its performance through experiment.

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.

Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.

Today’s advancements in IoT devices and edge computing platforms have given rise to new scenarios enabling context-aware applications in extremely interconnected environments. To promote the standardization of these platforms the European Telecommunications Standards Institute (ETSI) proposed the Multi-access Edge Computing (MEC) standard, enabling the execution of cloud-like services at the network edge. In this work, we propose the design of a novel MEC-compliant architecture that leverages underutilized far-edge resources to enlarge MEC edge node computational capacity and enhance service availability in highly mobile networks. Our approach allows far-edge devices to participate in a negotiation process embodying a rewarding system while addressing resource volatility as these devices join and leave the edge node resource infrastructure. Furthermore, we developed an original simulation framework to replicate the proposed architecture by using vehicle resources as far-edge devices. Its primary purpose is to demonstrate the viability and flexibility of our proposal, as well as to investigate novel application scenarios using real-world datasets. Our preliminary results show the feasibility and effectiveness of our proposal when using vehicular-based virtual resources in realistically simulated 5G networks.

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.

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

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.

Multi-access Edge Computing (MEC) can provide the intelligent platform or environment for services with the requirements of lower latency, higher bandwidth, and higher computation complexity, thereby attracting more attention in the future network. Intuitively, MEC can be applied to 4G and 5G mobile networks respectively, however the corresponding MEC architectures will be entirely different because of the difference between 4G and 5G network architectures. Therefore, this paper firstly presents the 4G MEC architecture and deeply discusses the existing problems in field trials and future deployment, e.g. charging, lawful interception, etc. Considering the above problems, the 5G MEC integrated architecture is proposed in this paper, which takes the network and service functions into account simultaneously. Meanwhile, the network function, especially for the traffic offloading mechanism, is emphatically analyzed and discussed. In addition, some suggestions are also presented for promotion of 5G MEC.