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Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions. [ABSTRACT FROM AUTHOR]

The growing technology of the fifth generation (5G) of mobile telecommunications has led to the special attention of cloud service providers (CSPs) to mobile cloud computing (MCC). Due to the limitations in processing power, storage space and energy capacity of mobile devices, cloud resources can be moved to the edge of the network to improve the quality of service (QoS). Server placement is a crucial emerging problem in both typical and edge types of MCC, different proposed methods of which are reviewed and evaluated in this paper. Proper placement of servers leads to more efficient utilization of these servers, reduces their response time and optimizes their energy consumption. A variety of techniques and approaches, including machine learning-based techniques, evolutionary models, optimization algorithms, heuristics and meta-heuristics have been employed by different server placement methods of the literature to find the optimal deployment map of servers. This paper provides a comprehensive analysis of these server placement methods in edge computing, fog computing and cloudlet, investigates their various aspects, dimensions and objectives, and evaluates their strengths and weaknesses. Furthermore, open challenges for server placement in MCC are provided, and future research directions are also explained and discussed. [ABSTRACT FROM AUTHOR]

In cloud storage, attribute-based keyword searchable encryption realizes multi-user fine-grained authorization access control and outsourcing data sharing. However, (1) resisting key regeneration is an urgent problem to be solved in practical applications of attribute-based keyword search; (2) the general attribute-based keyword search scheme ignores the application scenario where multiple data owners jointly manage files. In addition, the interaction of data between intelligent terminals and cloud servers has become more frequent, leading to issues such as central load, transmission efficiency, and data privacy protection in cloud computing. To address the above issues, we propose an attribute-based searchable encryption scheme with the multi-data owner based on edge computing. In this scheme, the Pedersen (k , n) secret sharing protocol and the Reciprocal protocol are used to realize the common management and sharing of data by the multi-data owner to enhance data security. Secondly, the attribute set and identity information of each user are embedded into the private key to prevent key regeneration and resist user collusion attacks. Thirdly, edge computing is introduced, and part of the storage and decryption work is outsourced to edge nodes to reduce the computing load of users and data transmission bandwidth. Security proof and performance analysis show that the proposed scheme has strong security and practicability in multi-user sharing scenarios such as Online Education with multi-data owners. [ABSTRACT FROM AUTHOR]

The study systematically reviews the integration of Fog and Edge Computing within Learning Analytics to enhance data privacy and security in educational settings that use cloud computing. Employing the PRISMA methodology, we analyze current literature from Web of Science and Scopus databases to examine how these decentralized computing technologies can mitigate the risks associated with centralized cloud storage by processing data closer to its source. Our findings highlight the significant potential of Fog and Edge Computing to transform Learning Analytics by enabling real-time, context-aware data analysis that supports personalized learning while ensuring stringent data privacy. This approach challenges conventional data management practices, advocating for privacy by design in developing new strategies and frameworks. The research underscores the need for collaborative efforts in establishing standards and guidelines for secure and effective technology use in education, pointing towards the necessity of addressing technical, operational, and ethical challenges to maximize the benefits of fog and edge computing in Learning Analytics. • Fog and Edge computing improve data privacy for Learning Analytics and minimizes dependence on centralized cloud storage. • Nearer analytical technologies to users promotes a personalized and secure learning environment. • Future research should focus on creating secure architectures and policies to leverage Fog and Edge Computing in education. [ABSTRACT FROM AUTHOR]

In the recent past, many studies have been conducted to elucidate the determinants affecting cloud computing adoption at different institutional levels. The study aims to identify how technological, organizational, environmental, and socio-culture factors influence the adoption of cloud edge technologies. The proposed theoretical framework is based on the main determinants of the TOE theory and extends it by incorporating a socio-cultural predictor. Eighteen core manifests were extracted from previous literature and the standard theories. The researcher used a proper validation process to create a valid questionnaire. Two-stage analytical methods, i.e., SEM and ANN, test the proposed model. SEM is applied to test all core hypotheses, and identified significant predictors are considered input items to the neural network model to ascertain the relative magnitude of each manifest in a subsequent stage. The outcomes of the quantitative investigation outline the relationship between chosen constructs and cloud-edge computing adoption (CECA). The study supports all core hypotheses using SEM. The study is one of the novel attempts to empirically confirm and estimate the synergy of multimodal structure to elucidate the relative importance of manifests to be considered by top management of HEIs. [Display omitted] • This research discussed parameters and techniques used in an integrated model of cloud-edge adoption. • Eighteen core manifests were extracted from previous literature and the standard theories. • SEM identified significant predictors, which are considered inputs to the Artificial Neural Network (ANN). • ANN outlines that in technological factors, security, and privacy of the cloud is the highest explanatory factor. • Organizational constructs denote innovative technology, reduced cost, and robust output of the cloud edge. • Environmental factors consist of geographical reach and vendor support 24x7 vital to adopt cloud edge. • Social values, customs & beliefs, awareness of cloud, collaboration & trust were significant in adoption of cloud edge. • Theoretical implications of the research offer practical insight to stakeholders on cloud-edge computing adoption. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) nowadays represents a fundamental technology for the Industry 4.0 initiatives for connecting devices, operators and processes so as to eventually fuel digital transformation. However, if based exclusively on Cloud Computing, the IIoT faces inevitable limitations and barriers. Therefore, in this paper we deal with the Edge Intelligence (EI), a novel paradigm at the confluence of Edge Computing and artificial Intelligence, and, particularly, we analyze its impact on the processing of the IIoT data in the proximity of the events of interest rather than on Cloud servers. Therefore, a general discussion on the most relevant benefits, limitations and open challenges of the IIoT-EI duo is proposed hereinafter and exemplified through an emblematic IIoT use case: this is related to safety-critical tasks based on video analytics and provides a full-fledged comparison between Cloud-and EI-based IIoT deployments (in terms of reliability, responsiveness, bandwidth usage, energy footprint and usability), finally outlining a general strategy for large-scale EI-aided IIoT systems engineering. [ABSTRACT FROM AUTHOR]

A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While cloud computing offers advantages, it introduces scheduling delays caused by data transmission. To address this issue, edge computing has emerged as an alternative to traditional cloud systems, aiming to minimize latency. While various methods have been proposed to address this challenge, they often prioritize one aspect at the expense of overall system performance. In this paper, we present a novel algorithm utilizing ant colony optimization to compute a fitness function and prioritize multiple objectives in scheduling. The algorithm effectively determines how to distribute applications between edge and cloud servers to enhance computational efficiency. This entails a delicate balance between scheduling delays and energy consumption in two distinct phases. Initially, the algorithm identifies applications sensitive to delays and ensures their execution on local edge servers. Subsequently, it identifies applications that require intensive computation and migrates them to the cloud layer, where cloud servers can process them. The results demonstrate that this approach reduces delay costs by 21.19% and decreases energy consumption by 13.76%. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a hierarchical and multilayered computing framework, specifically tailored to analyze the benefits of Named Data Networking (NDN) enabled microservice-centric in-network computations in autonomous vehicular networks. The proposed framework comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud.Modifications were made to the vanilla NDN codebase to facilitate communication between the vehicular network layer, Edge computing and Cloud layer, utilizing the C++ Boost Asio library and IP tunneling mechanism. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in.NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed system significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems. [ABSTRACT FROM AUTHOR]

The ongoing realization of a transition towards increased reliance on edge computing is evident. This shift not only underscores the importance of interdependence in knowledge sharing but also contributes to a reduction in adaptation and transmission time through cloud computing directly, along with decreased computational demands compared to edge-to-edge computing. A persistent challenge in knowledge transfer stems from the shift in dependence from high-performance computing systems to compact models, making the adjustment cumbersome. Even with cross-edge knowledge transfer, pre-processing remains essential to facilitate seamless adaptation—a necessity that endures despite the escalating volume of data. Consequently, the system tends to become overburdened with instance filtering instead. To address this issue, we present an optimization for small-to-small inter-edge transfer learning of a comparable size. This is achieved through a self-supervised domain-adaptive protocol, utilizing self-target curriculum-based pseudo-labeling creation, new statistical alignment, and lightweight attention. In the multi-view verification using public visual DA datasets, we demonstrate a remarkable improvement of 73.6 (+4.2 %) in Top-1 performance. Our results consistently outperform other methods, particularly when using the latent feature input technique. This advantage is most noticeable at the 10-th layer for latent feature input. Crucially, our approach incurs the modest increase in computational demands, adding just 100 K-units of parameters and MFLOPS of computational power. Furthermore, through model enhancement via acceleration and compression, we achieve significant compression—up to 6.28 times faster at FP32 and 7.18 times faster at FP16 runtimes—resulting in an increased FPS to 124.9 and 142.9, respectively. This demonstration effectively illustrates the feasibility of advancing inter-edge transfer learning with our approach. [ABSTRACT FROM AUTHOR]

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

Energy efficiency is a key area of research aimed at achieving sustainable and environmentally friendly networks. With the rise in data traffic and network congestion, IoT devices with limited computational power and energy resources face challenges in analyzing, processing, and storing data. To address this issue, computing technology has emerged as an effective means of conserving energy for IoT devices by providing high-performance computing capabilities and efficient storage to support data collection and processing. As such, energy-efficient computing, or "green computing," has become a focal point for researchers seeking to deploy large-scale IoT networks. This study provides a comprehensive Survey of recent research efforts aimed at achieving energy-efficient computing and green computing for IoT networks. To the best of our knowledge, none of the studies in the literature have discussed all types of green computing (edge, fog, cloud) and their role in enabling massive IoT networks in terms of energy efficiency. The article starts with an overview of computing technologies and then goes with a discussion of the empowering energy-saving techniques for computing (edge, fog, and cloud) environments including, energy-aware architecture, data aggregation and compression, low-power hardware, energy-aware scheduling, task offloading, switching on/off unused resources, virtualization, energy harvesting, and cooling optimization. This article is an outline of a roadmap toward realizing the vision of a sustainable computing environment for massive IoT networks; in addition, open the door for interested researchers to follow and continue the vision of Energy-Efficient Computing. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) generates a significant volume of geo-tagged images via surveillance sensors in edge–cloud computing environments. Image search is essential to facilitate information sharing, data analysis, and strategic decision-making. However, outsourced images are typically encrypted for privacy protection, posing a challenge in simultaneously searching for visual and geographical relevance on encrypted images. To address this, this paper proposes an edge intelligence empowered privacy-preserving top- k geo-tagged image search scheme for IoT in edge–cloud computing. The scheme presents a novel single-to-multi searchable encryption method for geo-tagged images that enables multiple users to perform secure nearest neighbor queries on a data source. Additionally, an extended anchor-based position determination method and an inner product-based distance calculation method are designed to enable geo-tagged image similarity calculation on ciphertext. Finally, a secure pruning method is introduced to improve query performance. Experiments are conducted to verify the performance of the scheme in terms of high efficiency and accuracy of the search. • Single-to-multi searchable encryption model on geo-tagged images. • Secure nearest neighbor queries on geo-tagged images. • Secure pruning based on the extended locality-sensitive hashing. • Distance calculation from an encrypted data point to an encrypted space. • Extended anchor-based position determination. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoTs) has transformed the digital landscape by interconnecting billions of devices worldwide, paving the way for smart cities, homes, and industries. With the exponential growth of IoT devices and the vast amount of data they generate, concerns have arisen regarding efficient task-offloading strategies. Traditional cloud and edge computing methods, paired with basic Machine Learning (ML) algorithms, face several challenges in this regard. In this paper, we propose a novel approach to task offloading in a Device-to-Device (D2D)-Edge-Cloud computing using the Rainbow Deep Q-Network (DQN), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework. It balances the trade-offs among D2D, Device-to-Edge (D2E), and Device/Edge-to-Cloud (D2C/E2C) communications, benefiting both end users and servers. These networks leverage Deep Learning (DL) to discern patterns, evaluate potential offloading decisions, and adapt in real time to dynamic environments. We compared our proposed algorithm against other state-of-the-art methods. Through rigorous simulations, we achieved remarkable improvements across key metrics: an increase in energy efficiency by 29.8%, a 27.5% reduction in latency, and a 43.1% surge in utility. • We present a method for task offloading across the D2D, Edge, and Cloud spectrum, crucial for elevating operational efficiency in the IoT systems. • We incorporate the Rainbow DQN, an advanced DRL algorithm. • We establish compelling evidence of noteworthy improvements in key performance indicators. [ABSTRACT FROM AUTHOR]

In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio. [ABSTRACT FROM AUTHOR]

The traditional cloud computing paradigm faces challenges with the increasing number of Artificial Intelligence of Things (AIoT) devices generated at the network edge. Edge computing provides a novel approach to overcoming the limitations of cloud computing by delivering lower service latency and higher quality of service (QoS) to AIoT devices. However, edge computing encounters constraints due to scarce resources on edge servers and the long distance between AIoT devices and remote cloud servers. To ensure high QoS for AIoT devices, a balance is required between limited computing resources on the network edge and high latency caused by the geographic distance on the cloud side. In this paper, we propose an edge-cloud architecture that achieves optimal QoS for AIoT devices. We employ a federated learning-based architecture to train AIoT devices' data locally, thereby ensuring privacy. We evaluate the effectiveness and efficiency of our proposed approach by comparing it with the centralized approach from the state-of-the-art using two widely used datasets. The experimental results demonstrate that our architecture achieves higher effectiveness and efficiency in improving AIoT devices' QoS. Overall, our proposed edge-cloud architecture overcomes the limitations of traditional cloud computing, enhances user privacy, and delivers high QoS to AIoT devices. [ABSTRACT FROM AUTHOR]

Electrocardiogram(ECG) monitoring is a critical and intricate task in cardiac healthcare. While large models supported by the remote cloud servers with abundant computational resources offers a feasible solution for this task, which still face challenges related to processing costs, privacy risks, and response time. Efforts have been made to integrate edge computing as a supplementary solution, but the current collaborative computing strategy for ECG monitoring is static and inefficient. To address these shortcomings, this study proposes an innovative confidence constraint cloud–edge collaborative computing(3CE2C) strategy. Firstly, the model implementation processes are illustrated, including an ultra-lightweight model for the edge node and a large model for the cloud server. To enhance classification performance, the personalized strategy is employed, resulting in an accuracy improvement from 0.9849 to 0.9929 in the model-cloud. For edge implementation, the optimal input length and model quantization both are explored to reduce the energy consumption. Based on the given confidence constraint, the models dynamically collaborate, with the low-confidence samples uploaded to the cloud server. This approach can achieve accuracy comparable to cloud computing, transmitting only about 17% low-confidence samples, the accuracy ratio(r A c c) is 0.9985. In addition, the method is validated in SVDB, where 3CE2C outperforms state-of-the art framework with the same uploaded sample ratio, resulting in a 1.54% improvement in classification accuracy. In conclusion, the proposed method provides a practical solution in real-time arrhythmia detection applications. • Proposing a confidence constraint cloud–edge collaborative computing strategy. • Improving the arrhythmia classification performance with a personalized strategy. • Implementing an ultra-lightweight model at edge with minimal performance loss. • Achieving accuracy similar to cloud computing, with uploading only 17% samples. • Demonstrating the superiority of the proposed framework compared to other studies. [ABSTRACT FROM AUTHOR]

The huge prospects of the internet of things (IoT) have led to an ever-growing demand for computing power by IoT users to enable various applications. Multi-access edge computing (MEC) research and development has rapidly gained attention during the last decade. The ability to deploy edge servers at different points across a content delivery network that can offer communication and computing services close to mobile user devices is one of the main factors driving the evolution of MEC. Furthermore, MEC has been considered a potentially transformational approach for fifth-generation (5 G) and beyond 5 G (B5G) networks, as well as a potential improvement to conventional cloud computing. Unmanned aerial vehicles (UAVs) can be used as effective aerial platforms to offer reliable and ubiquitous connections in wireless communication networks due to their distinctive qualities, such as high cruising altitude, on-demand deployment, and three-dimensional (3D) maneuverability. The number of research studies published in this area has dramatically increased due to the growing interest in UAV-enabled MEC. Although UAV-enabled MEC systems have been well studied, the existing models are becoming increasingly heterogeneous and scattered without harmony. This paper provides a comprehensive analysis of the literature on UAV-enabled MEC systems with a special focus on the system modeling, and optimization techniques for five identified domains, such as energy efficiency, resource allocation, trajectory control, latency, and security. For each domain, we have highlighted the recent advances, critical findings, and the advantages and disadvantages. Additionally, the identified proposed techniques were analyzed and discussed, with emphasize on the constraints and performance metrics. We also discuss a general system model for each highlighted domain. Moreover, the lessons are also derived from the study on system optimization and system modeling techniques identified in this paper. Then we discuss open issues related to UAV-enabled MEC systems in each highlighted domain, including problem formulation and optimization techniques. Finally, this paper lay out directions for future research to solve the aforementioned problems associated with UAV-enabled MEC systems. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence techniques have been adopted for many real-time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay. [ABSTRACT FROM AUTHOR]