A Conceptual Framework for Security and Privacy in Edge Computing

Load parameter identification is one of the keys and difficult points in power system analysis and research. With the rapid development of the grid scale, the load structure is becoming more and more complex, and the adaptability of the load model structure and typical parameters to the actual power grid has become increasingly prominent. In response to this problem, a parameter identification method for power grid load models in the internet of things (IoT) environment is proposed. Firstly, a load model is established under the power IoT “cloud-network-edge-terminal” architecture. Secondly, the genetic algorithm (GA) is used to identify the parameters of the dynamic load model in the edge layer. At the same time, without changing the structure of the load model, a load parameter security protection method based on edge computing is proposed to ensure the safety and effectiveness of the load parameter identification results. Finally, the effectiveness of the proposed load parameter identification method is tested by comparing response time, load parameter identification accuracy, and edge computing loss.

Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. New projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software before each system can be developed. In addition, the systems should be developed to test them, which implies time, effort and development costs. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Model-driven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.

The Internet of Things (IoT) is expected to grow faster than any other category of connected devices. IoT allows any device with an on-and-off switch to connect to the internet—a concept that has the ability to greatly change our lives and work. These modern systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity of data, which leads to incremental growth in data traffic on networks and in the cloud. To fulfill the requirements of IoT, including geodistribution, low latency, location awareness, and mobility support, a new paradigm is proposed: edge computing. In edge computing, substantial computing and storage resources are placed at the edge of the network in mobile devices or sensors. The term “edge” is taken from network diagrams; normally, the edge of a network diagram represents the point at which data traffic enters or leaves the workable network. Using the concept of edge computing, an organization can shift huge amounts of data into processed data near the data origin, which helps to reduce data traffic in the network’s central repository (called the “cloud”). Edge computing uses a variety of data reduction techniques close to the data source at the network edge, including data pre-processing, local storage, and filtering. This approach can prevent some critical issues, such as I/O bottlenecks, storage and bandwidth limitations, data traffic increments, and high energy costs. A major advantage of edge computing is improvement of the request-response delay to milliseconds. Edge computing also supports security and network challenges. However, two major obstacles exist toward achieving the benefit of network-edge computing. First, the most efficient algorithms for data reduction in time series (one of the most common types of data in IoT) were developed to work posteriori upon big datasets, but they cannot make decisions for each incoming data item. Secondly, the state of the art lacks systems that can apply any of the possible data reduction methods without adding significant delays or major reconfigurations. Edge computing has also inherited some of the challenges of cloud computing, including data abstraction, naming, and programmability. This chapter presents a detailed taxonomic discussion of edge computing, along with its challenges, opportunities, and data reduction methods.

여러 사물 인터넷 기기들을 조합, 활용하여 다양한 서비스를 제공할 수 있게 된 환경에서 추천시스템은 사용자가 원하는 서비스를 선택하는 데 있어 도움을 줄 수 있다. 기존의 추천 시스템에 대한 많은 연구는 주로 개인 사용자 대상의 추천에 집중되어 있으나 사물 인터넷 환경에서는 개인과 그룹 모두 사용자가 될 수 있으므로 그룹에 대한 추천 방법이 필요로 하다. 본 연구는 사물 인터넷 환경에서 그룹사용자들의 서비스 선호도를 분석하고, 이를 바탕으로 특정 장소에서 서비스를 이용한 적이 없는 새로운 그룹 사용자에게 서비스를 추천할 수 있는 방법을 개발하였다. 본 연구에서는 그룹의 구성 정보를 기반으로 그룹들 간의 유사도를 측정하여 사용자 기반 협업 필터링을 적용하였다. 실험에서는 실제 사물 인터넷 테스트 베드 환경에서 수집된 데이터를 사용하였으며 실험 결과를 통해 제안한 서비스 추천 방법이 효과적임을 확인할 수 있었다.

Recently, IETF (Internet Engineering Task Force) presented CoAP (Constrained Application Protocol) for the communication between sensor and actuator nodes in a constrained environment such as small amount of memory and low power. IETF CoAP protocol can convert easily, and can use to monitor or control infrastructure utilities through low-power sensor and actuator networks in IoT (Internet of Things) and M2M (Machine-to-Machine) environment. IETF CoAP protocol provides the special requirements of this constrained environment, especially considering energy, automation, and other IoT applications. In this paper, we design and implement a node registration and discovering based CoAP in IoT environment. The node is a CoAP node which is designed for working in constrained environment. For node registration in IoT, we used CoRE RD (Resource Directory) functionalities to register CoAP node’s information which works in IoT environment.

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments.

Internet of Things (IoT) is a platform where every day devices become smarter, every day processing becomes intelligent, and every day communication becomes informative. Numerous challenges prevent to secure IoT devices and their end-to-end communication in an IoT environment. In fact, the IoT security is still an open challenge. The purpose of this work is to examine a distributed strategy for mitigating Denial of Service (DoS) attacks against the fog node in an edge computing context in which the nodes exchange messages through Message Queue Telemetry Transport (MQTT) protocol. The proposed strategy is based on a dynamic message sending frequency of the lightweight nodes. It is also mitigated data tampering and eavesdropping by using Elliptic Curve Cryptography (ECC).

Edge computing has gradually availed itself as one of the most important technologies in the Internet of Things (IoT) to influence the processing, management, and utilization of data.A major advantage that is offered by edge computing by bringing computation and storage resources closer to IoT devices is that it minimizes latency as well as conserves bandwidth and offers greater capabilities for real time decision making.These changes from massive cloud processing to multiple neighboring edge nodes solve IoT architectural challenges common in earlier types of design, including those with low-latency responses and offline capabilities of applications such as smart cities, self-driving cars, and industrial control systems.The potential advantages of edge computing in IoT include privacy and security, because the collected information can be analyzed on the fly on the endpoint without sending it to the cloud; scalability, given that a great amount of IoT devices can be monitored by edge nodes without putting extra load on the centralized facilities.However, the use of edge computing in IoT also poses some challenges for example restricted resources at edge nodes, issue of the network infrastructure and more so the security that has to be enhanced.This abstract discusses the existing works done on architecture of edge computing IoT, the advantages and the limitation of integrating edge computing IoT system and indicates how it is capable of revolutionizing industries and enable a new IoT generation application system equipped with intelligence and decentralization.

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

Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

Abstract: This book presents a comprehensive exploration of the convergence between Artificial Intelligence (AI) and Edge Computing as a transformative framework for optimizing Internet of Things (IoT) systems. It addresses the core challenge of deploying intelligent decision-making capabilities in latency-sensitive, bandwidth-constrained, and privacy-critical environments where traditional cloud-centric architectures prove insufficient. Through the integration of lightweight AI models, distributed inference, and federated learning, the book develops a conceptual and technical foundation for enabling scalable, real-time analytics at the edge. The methodology combines theoretical modeling with empirical case studies and performance benchmarking across diverse IoT applications, including smart cities, industrial automation, autonomous systems, and healthcare monitoring. Key AI techniques—such as reinforcement learning, spatiotemporal modeling, and knowledge distillation—are evaluated in the context of resource-constrained edge hardware. The book further explores secure and ethical deployment through privacy-preserving learning, encrypted model updates, and explainable AI frameworks aligned with international regulatory standards. Findings highlight significant improvements in system responsiveness, energy efficiency, and data sovereignty when AI is embedded at the edge. The book concludes with an assessment of emerging innovations—neuromorphic computing, quantum edge AI, and 6G-enabled edge-satellite intelligence—and proposes a cross-sectoral roadmap for future research, policy, and system design. This volume offers foundational insights for scholars, engineers, and policymakers navigating the evolution of intelligent, decentralized IoT infrastructures. Keywords Artificial Intelligence, Edge Computing, Internet of Things, IoT Optimization, Federated Learning, Real-Time Analytics, Edge AI, Reinforcement Learning, Spatiotemporal Modeling, Lightweight Neural Networks, Distributed Inference, Privacy-Preserving AI, Explainable AI, Smart Systems, Low-Latency Processing, Secure Edge Intelligence, 6G Networks, Neuromorphic Computing, Quantum AI, Autonomous IoT Systems

This paper presents a dynamic central processing unit (CPU) scheduling algorithm selectionsystem that utilizes machine learning to optimize the process execution within a computer system. Thisstudy evaluated six scheduling algorithms based on key performance indicators such as CPUutilization, turnaround time, waiting time, response time, and throughput. To overcome the limitationsof traditional approaches, the proposed methodology integrates a multilayer neural network (MLNN)to identify patterns and dynamically choose the most appropriate algorithm at runtime. The paperreviews related literature, highlighting efforts to enhance scheduling algorithms through machinelearning and improvements to conventional methods. The methodology includes developing a processdatabase, applying scheduling algorithms, optimizing the outcomes, and training a neural network fordynamic algorithm selection. The results indicate that the proposed approach outperforms existingalgorithms regarding waiting time, turnaround time, throughput, and execution efficiency.Additionally, the methodology offers adaptability to diverse process parameters. The study concludesby underscoring the potential for future advancements, including improvements in algorithmprecision, incorporation of additional process characteristics, exploration of advanced patternrecognition techniques, and integration of security measures for real-world applications such as cloudcomputing, edge computing, and IoT (Internet of things) environments.This paper presents a dynamiccentral processing unit (CPU) scheduling algorithm selection system that utilizes machine learning tooptimize the process execution within a computer system. This study evaluated six schedulingalgorithms based on key performance indicators such as CPU utilization, turnaround time, waitingtime, response time, and throughput. To overcome the limitations of traditional approaches, theproposed methodology integrates a multilayer neural network (MLNN) to identify patterns anddynamically choose the most appropriate algorithm at runtime. The paper reviews related literature,highlighting efforts to enhance scheduling algorithms through machine learning and improvementsto conventional methods. The methodology includes developing a process database, applyingscheduling algorithms, optimizing the outcomes, and training a neural network for dynamic algorithmselection. The results indicate that the proposed approach outperforms existing algorithms regardingwaiting time, turnaround time, throughput, and execution efficiency. Additionally, the methodologyoffers adaptability to diverse process parameters. The study concludes by underscoring the potentialfor future advancements, including improvements in algorithm precision, incorporation of additionalprocess characteristics, exploration of advanced pattern recognition techniques, and integration ofsecurity measures for real-world applications such as cloud computing, edge computing, and IoT(Internet of things) environments.

The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the Internet Technology community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems.