Search

This study introduces delay-aware cloudlet placement with a modified k-means (DACP-mk) algorithm to address the complexities of mobility and optimize cloudlet placement in a mobility-enhanced environment. DACP-mk enhances the traditional k-means algorithm by incorporating modified regional and global delay matrices to enable the simulation of a delay-aware cloudlet movement and placement strategy using a cluster-based approach within a smart healthcare platform. An optimized cloudlet position is generated to achieve a uniform distribution of mobile devices among placed cloudlets, maximize device coverage, and minimize network access delay within the cloudlet coverage distance. The proposed algorithm determines the optimal positions for cloudlets at coordinates (161.90, 103.24), (080.22, 139.52), and (080.29, 051.21) with an increase of 02.42%, 00.54%, 38.71%, and 49.00%, respectively, in terms of global mobile device coverage compared to modified k-means Manhattan (mkMAN), k-means, energy-efficient cloudlet placement method (ECPM) and enhanced adaptive cloudlets placement with covering algorithm (EACP-CA), and a decrease of 00.71% and 58.27% in global network access delay compared to mkMAN and k-means algorithms. The proposed algorithm offers enhanced healthcare support and cost-effective services, improving the Quality of Service (QoS) and ultimately contributing to saving lives. [ABSTRACT FROM AUTHOR]

ABSTRACT 6G 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. [ABSTRACT FROM AUTHOR]

In Ultrareliable and Low Latency Communications (URLLC), balancing trade-offs between energy consumption, service availability, and strict reliability and latency requirements is a significant challenge, especially in unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) environments. The constraints imposed by the size, weight and power limitations of UAVs further complicate this task. This study addresses optimizing resource allocation in such environments to meet URLLC demands while minimizing power consumption and maximizing service availability. We explore the virtualization layer of the network function virtualization (NFV)-MEC architecture, incorporating node availability and power consumption alongside conflicting URLLC reliability and latency demands. We introduce an energy-aware model based on continuous-time Markov chain (CTMC) with an embedded virtual resource scaling scheme for Dynamic Resource Allocation (DRA). To solve the optimization problem related to MEC-enabled UAV node dimensioning, we propose a genetic algorithm (GA)-based solution. Our results demonstrate that the proposed GA-based approach achieves a superior balance, with up to a 44% reduction in power consumption compared to the first fit with maximum resources strategy, while also improving service availability and meeting URLLC requirements. This work provides a comprehensive analysis of key virtualization parameters and their impact on critical services within a single NFV-MEC over a UAV node, offering a robust framework for future 6 G network applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks. [ABSTRACT FROM AUTHOR]

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99 % probability. The average AoI quantity is maintained within the 5–10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties. [ABSTRACT FROM AUTHOR]

In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult for terminal devices to obtain high accurate results. Aiming at the problems of limited terminal device resources, low accuracy of data processing results, and unsatisfactory processing speed, a Heterogeneous Multi-access Edge Computing (MEC) Framework based on Transfer Learning (TL) is proposed, abbreviated as HMECF-TL. This framework adopts a cloud-edge-end three-layer architecture. It uses model transfer to optimize the Convolutional Neural Networks (CNN) model at each layer to achieve the goal of improving data processing speed and accuracy. Furthermore, a multi-agent Deep Reinforcement Learning Algorithm having Attention Mechanism (DRLAAM) is designed to further increase the timeliness performance of computation-intensive applications. The performance of HMECSF-TL framework is verified by simulation experiments, which not only reduces the delay by more than 24.66 %, but also improves the accuracy by more than 8.34 %. The framework not only increase the computing capacity to solve the shortage of terminal device resources, but also improve the quality of data processing to solve the problem of data starvation.

Edge computing is a promising technology, especially for offloading users' computationally heavy tasks. The close proximity of edge computing units to users minimizes network latency, thereby enabling delay-sensitive applications. Although optimal resource provisioning and task offloading in edge computing are widely studied in the literature, there are still some critical research gaps. In this study, we propose a traffic-aware optimal association and task-offloading approach. The proposed method does not rely solely on the average rate of offloading requests, which can differ from actual values in real time. Instead, it uses an intelligent, high-precision prediction model to forecast future offloading requests, allowing resource provisioning to be based on future sequences of requests rather than average values. Additionally, we propose an optimization-based approach that can meet task deadlines, which is crucial for mission-critical applications. Finally, the proposed approach distributes the computing load over multiple time steps, ensuring future resource scheduling and task-offloading decisions can be made with a certain level of flexibility. The proposed approach is extensively evaluated under various scenarios and configurations to validate its effectiveness. As a result, the proposed deep learning model has resulted in a request prediction error of 0.0338 (RMSE). In addition, compared to the greedy approach, the proposed approach has reduced the use of local and cloud computing from 0.02 and 18.26 to 0.00 and 0.62, respectively, while increasing edge computing usage from 1.31 to 16.98, which can effectively prolong the lifetime of user devices and reduce network latency. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Things (IoT) technology has led to a significant increase in the computational task load of Terminal Devices (TDs). TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing (MEC). However, existing task-offloading optimization methods typically assume that MEC's computing resources are unlimited, and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted. In addition, existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot, but lack support for multiple retry in subsequent time slots. It is resulting in TD missing potential offloading opportunities in the future. To fill this gap, we propose a Two-Stage Offloading Decision-making Framework (TSODF) with request holding and dynamic eviction. Long Short-Term Memory (LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot. The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology. Simulation results show that TSODF reduces total TD's energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) has attracted the interest of the research and industrial community to support Internet of things (IoT) applications by enabling efficient data processing and minimizing latency. This paper presents significant contributions toward optimizing the resource allocation and enhancing the decision-making process in edge computing environments. Specifically, the TRUST-ME model is introduced, which consists of multiple edge servers and IoT devices, i.e., users, with varied computing tasks offloaded to the MEC servers. A utility function was designed to quantify the benefits in terms of latency and cost for the IoT device while utilizing the MEC servers' computing capacities. The core innovation of our work is a novel trust model that was designed to evaluate the IoT devices' confidence in MEC servers. This model integrates both direct and indirect trust and reflects the trustworthiness of the servers based on the direct interactions and social feedback from other devices using the same servers. This dual trust approach helps with accurately gauging the reliability of MEC services and ensuring more informed decision making. A reinforcement learning framework based on the optimistic Q-learning with an upper confidence bounds action selection algorithm enables the IoT devices to autonomously select a MEC server to process their computing tasks. Also, a multilateral bargaining model is proposed for fair resource allocation of the MEC servers' computing resources to the users while accounting for their computing demands. Numerical simulations demonstrated the operational effectiveness, convergence, and scalability of the TRUST-ME model, which was validated through real-world scenarios and comprehensive comparative evaluations against existing approaches. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods. [ABSTRACT FROM AUTHOR]

With the improvement of service delay and quality requirements for new applications such as unmanned driving, internet of vehicles, and virtual reality, the deployment of network services is gradually moving from the cloud to the edge. This transition has led to the emergence of multi-access edge computing (MEC) architectures such as distributed micro data center and fog computing. In the MEC environment, network infrastructure is distributed around users, allowing them to access the network nearby and move between different service coverage locations. However, the high mobility of users can significantly affect service orchestration and quality, and even cause service interruption. How to respond to user mobility, dynamically migrate user services, and provide users with a continuous and seamless service experience has become a huge challenge. This paper studies the dynamic migration of service function chain (SFC) caused by user mobility in MEC environments. First, we model the SFC dynamic migration problem in mobile scenarios as an integer programming problem with the goal of optimizing service delay, migration success rate, and migration time. Based on the above model, we propose a deep reinforcement learning-driven SFC adaptive dynamic migration optimization algorithm (DRL-ADMO). DRL-ADMO can perceive the underlying network resources and SFC migration requests, intelligently decide on the migration paths of multiple network functions, and adaptively allocate bandwidth, achieving parallel and seamless SFC migration. Performance evaluation results show that compared with existing algorithms, the proposed algorithm can optimize 7% service delay and 20% migration success rate at the cost of sacrificing a small amount of migration time. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user devices, while also protecting user privacy and thereby improving the Quality of Service (QoS). To address this issue, this paper investigates a privacy-preserving computation offloading scheme, designed to maximize QoS by comprehensively considering privacy protection, delay, energy consumption, and the task discard rate of user devices. We first formalize the privacy issue by introducing the concept of privacy entropy. Then, based on quantified indicators, a multi-objective optimization problem is established. To find an optimal solution to this problem, this paper proposes a computation offloading algorithm based on the Twin delayed deep deterministic policy gradient (TD3-SN-PER), which integrates clipped double-Q learning, prioritized experience replay, and state normalization techniques. Finally, the proposed method is evaluated through simulation analysis. The experimental results demonstrate that our approach can effectively balance multiple performance metrics to achieve optimal QoS. [ABSTRACT FROM AUTHOR]

Extended reality (XR) is an immersive technology widely applied in various fields. Due to the real-time interaction required between users and virtual environments, XR applications are highly sensitive to latency. Furthermore, handling computationally intensive tasks on wireless XR devices leads to energy consumption, which is a critical performance constraint for XR applications. It has been noted that the XR task can be decoupled to several subtasks with mixed serial–parallel relationships. Furthermore, the evaluation of XR application performance involves both subjective assessments from users and objective evaluations, such as of energy consumption. Therefore, in edge computing environments, ways to integrate task offloading for XR subtasks to meet users' demands for XR applications is a complex and challenging issue. To address this issue, this paper constructs a wireless XR system based on mobile edge computing (MEC) and conducts research on the joint optimization of multi-user communication channel access and task offloading. Specifically, we consider the migration of partitioned XR tasks to MEC servers and formulate a joint optimization problem for communication channel access and task offloading. The objective is to maximize the ratio of quality of experience (QoE) to energy consumption while meeting the user QoE requirements. Subsequently, we introduce a deep reinforcement learning-based algorithm to address this optimization problem. The simulation results demonstrate the effectiveness of this algorithm in meeting user QoE demands and improving energy conversion efficiency, regardless of the XR task partitioning strategies employed. [ABSTRACT FROM AUTHOR]

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network's edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions. [ABSTRACT FROM AUTHOR]

With the ongoing expansion of user numbers and the diversity of business types, wireless networks are progressing toward broader spatial dimensions and increased functionality. With respect to coverage, the space–air–ground integrated network (SAGIN) is considered a promising solution for providing seamless global connectivity to emerging services. In terms of functionality, wireless networks are expected to offer not only communication services but also sensing and computing services, which are crucial for certain businesses. However, within SAGIN there is a significant variance in performance among different types of radio access networks (RANs). This necessitates a reconsideration of system architecture and handover selection schemes to meet the quality of service (QoS) requirements of various business applications. The main focus of this article is to explore the integration of sensing and mobile edge computing (MEC) with SAGIN and also to examine the system architecture, selection schemes, and handover procedures. We first provide an overview of SAGIN’s capabilities and highlight its integrated architecture. Subsequently, we introduce several handover selection schemes, followed by an elaboration on the redesigned handover procedure for SAGIN. Finally, we present some simulation results and discuss challenges that need to be addressed in practical implementation. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms. [ABSTRACT FROM AUTHOR]

As mobile communications transform, 5G technology can potentially change many industries and businesses. The change will have a great influence across many fields, such as the automotive, healthcare, and manufacturing sectors. This paper aims to review the existing applications of blockchain technology in providing 5G network security and identify new possibilities for such security solutions. We consider different aspects of blockchain in 5G, particularly data transmission, access control, and applications including vertical industry-oriented applications and specific solutions supporting such sectors of economic activity. The paper briefly describes modern technologies in 5G networks and introduces blockchain's properties and different aspects of using such technology in practical applications. It also presents access control management with blockchain applied in 5G and related problems, reviews other blockchain-enforced network technologies, and shows how blockchain can help in services dedicated to vertical industries. Finally, it presents our vision of new blockchain applications in modern 5G networks and beyond. The new-generation networks use two fundamental technologies, slicing and virtualization, and attackers attempt to execute new types of attacks on them. In the paper, we discuss one of the possible scenarios exhibiting the shortcomings of the slicing technology architecture. We propose using blockchain technology to create new slices and to connect new or existing subscribers to slices in the 5G core network. Blockchain technology should solve these architectural shortcomings. [ABSTRACT FROM AUTHOR]

Serverless computing is a recent emerged trending technology in the 5G edge computing landscape. Serverless computing’s on-demand dynamic scalability allows efficient utilization of limited available resources at the edge. To realize the benefit serverless edge computing, several solutions have been recently proposed to optimize serverless service placement, scaling and scheduling at the edge. However, lack of studies consider the involvement of 5G mobile network in orchestrating connection between requests from user equipments (UE) and serverless edge services. 5G control plane need to be aware of the serverless edge service deployment information determined by those optimized algorithms to avoid serverless service cold start problem caused by steering traffic to unoptimized edge sites. To address this issue, this study introduces enhancements to the current 5G architecture and procedures based on computing-aware traffic steering concept. We proposed a new computing-aware traffic steering controller that can manage serverless edge computing deployment information and influence 5G control plane to configure optimal routing path to serverless applications. The proposed solution allows 5G control plane to efficiently co-operate with advanced serverless deployment solutions to orchestrate serverless service serving at the 5G edge. We evaluated our solution against the normal 5G architecture and the 5G architecture enhancements for edge computing standardized by the 3rd Generation Partnership Project (3GPP). Our solution reduced the number of requests affected by cold-start from 8 to 35% and the edge resource consumption amount from 10 to 41%.

This paper designs a novel Hierarchical Federated Learning (HFL) management scheme, enabled by deep reinforcement learning (DRL), for multi-access edge computing (MEC) environments to accelerate convergence. To do this, the proposed scheme controls the number of local updates performed by mobile devices (MDs) and the number of intermediate aggregations at the MEC server before data is transmitted to the cloud for global aggregation. This optimization aims to i) mitigate the straggler effect by balancing training times between MEC and cloud servers, and ii) reduce the risk of overfitting by avoiding excessive reliance on faster MDs. Additionally, to improve the efficiency of DRL, Bayesian optimization is employed to initialize action values, thereby avoiding inefficient exploration of actions. Extensive simulations demonstrate that our proposed scheme outperforms various benchmarks in terms of test accuracy and training time.

This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple network data analytics functions (NWDAFs) are embedded in the unmanned aerial vehicle (UAV)-aided MEC servers according to the framework standardized by the 3rd Generation Partnership Project (3GPP) specification. Here, UAVs can use federated learning (FL) with their part of local datasets, while the remaining local datasets can be offloaded to the centralized server for centralized learning (CL). To achieve such HFCL energy-efficiently for battery-constrained UAVs, we propose an energy-efficient computation offloading (ECO) scheme for the HFCL. We rigorously formulate analytical models for the overall energy consumption at MEC servers on UAVs and total latency for HFCL process based on the amount of offloaded datasets at each UAV. Here the energy consumption includes i) the transmission energy consumption for offloading the local dataset and ii) the energy consumption for FL processing, which includes local training and local model uploading. To balance between the overall energy consumption and total latency during HFCL process while preventing overload at UAVs, we propose a theoretical framework for the ECO problem that optimizes the amount of offloaded local datasets over multiple UAVs on aerial networks. Here, the ECO problem is formulated as the convex optimization problem under the continuous domain of the amount of offloaded datasets. Our numerical and simulation results show that our proposed framework can construct wireless traffic prediction models with an acceptable training accuracy in an energy-efficient manner over various benchmarks by striking a balance between energy consumption and overall training latency.

In modern emergency responses, unmanned aerial vehicles (UAVs) play a crucial role in redefining disaster management through diverse task execution (e.g., object detection). However, UAVs are usually resource-constrained. To address this issue, UAV mother-ships with edge servers (UMECs) can be placed near UAV scouts for remote task processing. UMECs can have a larger scale of energy consumption and battery capacity than UAV scouts. Therefore, minimizing the total or maximum energy consumption may result in ignoring the energy consumption in UAV scouts and focusing on UMECs, consequently reducing the network’s lifetime. Furthermore, UMECs might lack the capacity to process all types of tasks owing to memory or software restrictions, and faster task execution often necessitates both central processing unit (CPU) and graphics processing unit (GPU), which are rarely considered by existing works. Therefore, this paper studies a task offloading and resource (computation capacity and power) allocation (TORA) problem with the goal of minimizing the maximum energy consumption ratio among UMECs and UAV scouts alongside task execution latency ratio and total energy consumption, where tasks are executed using both CPU and GPU, each UMEC can execute a specific set of tasks, and devices have limited resources. We then formulate the problem as a non-convex mixed-integer nonlinear programming problem and decompose it into multiple sub-problems. Finally, a soft-actor-critic (SAC) based TORA algorithm (SATORA) is proposed to address the problem, which can adapt to the time-varying environment scenario. Numerical simulation results show that SATORA outperforms other baseline algorithms.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems have emerged as promising solutions for enhancing the computational capabilities and reducing latency in next-generation wireless networks. However, the finite energy capacity of UAVs presents a significant challenge. In this study, we formulate a joint user association, computing resource allocation, and task offloading problem, called User Association and Computation Offloading (UACO), to minimize the energy consumption of both mobile devices and UAVs by considering the computational resource limitations of UAVs and the minimum user offloading data rate requirements. The UACO problem is a mixed-integer linear programming (MILP) problem that makes it NP-hard. We transform UACO into an Offload Exact Constrained Potential Game (OFECPG) based on game theory, which facilitates distributed execution. We propose the Best Response dynamics based on Local Decomposition (BR-LD) and Better Response dynamics based on the Local Switch operator (BR-LS) to enhance computational efficiency. We prove the existence of a pure strategy Nash Equilibrium (NE) and the convergence of the proposed algorithms. Extensive simulations demonstrated the effectiveness of the OFECPG, BR-LD, and BR-LS, showing significant improvements in system energy consumption compared to the baseline schemes. Our approach offers valuable insights into the design of efficient association and offloading schemes for multi-UAV MEC networks.

Connected and Autonomous Vehicles (CAVs) will be provided with multiple sensing and connectivity options as well as embedded computing and decision-making capabilities. The resulting technological landscape paves the way for the deployment of a plethora of innovative applications involving different stakeholders, such as insurance companies, car repairs, car manufacturers and public authorities. In such a context it is crucial to collect data in an efficient manner, not to burden the vehicle itself and the network infrastructure, while also providing an interoperable data sharing among all the involved players. The Digital Twin (DT) concept can play a key role to properly retrieve, store and share data as well as to exploit them to monitor, predict and improve the vehicle safety and driving experience. This work proposes a comprehensive framework which encompasses the presence of an edge-based DT interacting with the vehicle and the remote applications. It leverages properly specified interfaces and semantic models for different types of data provided by on-board sensing and learning capabilities. A Proof-of-Concept (PoC) has been developed to assess the practicality of the proposal and its performance in terms of communication and computation footprint under a variety of settings.

The domain of UAV-assisted Multi-Access Edge Computing (MEC) emerges as a novel frontier, characterized by the seamless integration of edge computing capabilities with UAV to facilitate advanced computational services for Terminal Devices (TDs). This research tackles two critical aspects in UAV-assisted MEC frameworks: the strategic formulation of UAV flight paths and the refinement of execution latency for Directed Acyclic Graph (DAG) tasks. We introduce an innovative solution employing Deep Reinforcement Learning (DRL), coined as the Twin Delayed Deep Deterministic Policy Gradient for UAV Trajectory Planning and Task Offloading (TD3-TT) algorithm. This algorithm harmonizes UAV flight planning, DAG task delegation, and scheduling hierarchies, thereby enabling UAV to efficiently undertake task offloading and processing concurrently along their designated optimal trajectories. Through this approach, the latency within the computational network is significantly diminished. A thorough examination of simulation outcomes reveals that the TD3-TT algorithm exhibits notable convergence and robustness, surpassing conventional benchmarks and markedly reducing the execution latency of DAG tasks.

The deployment and operation of Multi-Access Edge Computing (MEC) at the network edge provides low latency computing and storage services for end user devices. Support for device mobility is a functional requirement for MEC and a handover strategy that supports the movement of device and application state between MEC nodes is an underlying technology. The handover strategy utilizes an MEC node selection technique and a handover technique to provide a smooth transition between one MEC node to another. Research into MEC handover strategies has focused on algorithms, queuing, and other considerations. The migration of the MEC device and application state is a complex process that requires resource allocation in the destination MEC node that could involve provisioning application instances and, in some scenarios, support for containers or Virtual Machines to be received from the originating MEC node. This paper reviews MEC handover strategies and provides a description of the MEC reference architecture and proposed handover algorithms and techniques found in the literature. MEC handover challenges and gaps in the body of knowledge are discussed to provide guidance for future work.

This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks.

Driven by the demand for ubiquitous connection in the Internet of everything era, this paper introduces a new low earth orbit (LEO) satellite-based multi-access edge computing fusion architecture. The architecture regards LEO satellites deployed with high-performance edge modules as superior nodes, which can provide onboard processing mode edge task offloading service for users in complex regions. At the same time, the superior node also has the offloading decision and can offload some edge user tasks to the edge service center in the ground network. Based on differential game theory, we propose a two-stage computing resource purchase strategy and task-offloading resource pricing strategy to ensure the minimum cost of edge computing services for edge users and the maximum benefit for edge service providers and prove the existence of a unique Nash equilibrium solution using Piccard theorem. An algorithm based on the Runge–Kutta method is designed to solve Nash equilibrium, and the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The emergence of multi-access edge computing (MEC) has brought about significant advancements in application design and deployment by providing computing resources at the network's edge. MEC provides computing resources on the fringes of the network, allowing for near-real-time data processing and fast responses to user requests. In this context, scheduling plays a crucial role in offloading decisions in multi-access edge computing. The motivations for scheduling are to improve the quality of the experience, reduce latency, and increase performance. In this paper, we explore the various scheduling techniques available for MEC systems, including static scheduling, dynamic scheduling, heuristics, meta-heuristics and hybrid scheduling. We analyze the advantages and disadvantages of each technique and discuss how they can be used to optimize the performance of MEC applications. We also present a case study of an MEC system and demonstrate how the various scheduling techniques can be used to maximize its performance. Finally, we address both the challenges and prospects of MEC scheduling and suggest directions for future research. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited number of services that properly fulfill the demands of users. Several static service caching approaches have been proposed. However, the effectiveness of these strategies is constrained by the dynamic nature of the system states and user demand patterns. To mitigate this problem, several investigations have been conducted on dynamic service caching techniques that can be categorized as centralized and distributed. However, centralized approaches typically require gathering comprehensive data from the entire system. This increases the burden on resources and raises concerns regarding data security and privacy. By contrast, distributed strategies require the formulation of complicated optimization problems without leveraging the inherent characteristics of the data. This paper proposes a distributed service caching strategy based on federated learning (SCFL) that works efficiently in a distributed system with user mobility. An autoencoder model is utilized to extract features regarding the service request distribution of individual MEC servers. The global model is then generated using federated learning, which is utilized to make service-caching decisions. Extensive experiments are conducted to demonstrate that the performance of the proposed method is superior to that of other methods. [ABSTRACT FROM AUTHOR]

Nowadays, multi-access edge computing (MEC) has been widely recognized as a promising technology that can support a wide range of new applications for the Internet of Things (IoT). In dynamic MEC networks, the heterogeneous computation capacities of the edge servers and the diversified requirements of the IoT applications are both asymmetric, where and when to offload and schedule the time-dependent tasks of IoT applications remains a challenge. In this paper, we propose a flexible offloading and task scheduling scheme (FLOATS) to adaptively optimize the computation of offloading decisions and scheduling priority sequences for time-dependent tasks in dynamic networks. We model the dynamic optimization problem as a multi-objective combinatorial optimization problem in an infinite time horizon, which is intractable to solve. To address this, a rolling-horizon-based optimization mechanism is designed to decompose the dynamic optimization problem into a series of static sub-problems. A genetic algorithm (GA)-based computation offloading and task scheduling algorithm is proposed for each static sub-problem. This algorithm encodes feasible solutions into two-layer chromosomes, and the optimal solution can be obtained through chromosome selection, crossover and mutation operations. The simulation results demonstrate that the proposed scheme can effectively reduce network costs in comparison to other reference schemes. [ABSTRACT FROM AUTHOR]

Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally aggregate the results, in the vehicular edge computing (VEC) system for a vision of connected and autonomous vehicles. In this article, we review and present the concept of FL and introduce a general architecture of FL-assisted VEC to advance development of FL in the vehicular network. The enabling technologies for designing such a system are discussed and, with a focus on the vehicle selection algorithm, performance evaluations are conducted. Recommendations on future research directions are highlighted as well. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) is a technology that enables devices with limited processing capabilities to handle computationally intensive tasks efficiently. The challenge with MEC is how to schedule multiple tasks rationally and efficiently, mainly when each device generates several tasks. A critical aspect of task scheduling is considering the spatial relationships between devices in the network to reduce long-term losses. To address these challenges, this article proposes a deep reinforcement learning (DRL)-based Mayfly Taylor optimization algorithm (MTOA) that uses graph attention neural networks (GATs). The algorithm creates a planning agent for each end device that collects timing-related characteristics of activities and makes decisions and predictions using a recurrent gated unit and a graph representation agent (GRU). The GRU contains possible spatial elements from the scenario and incorporates them into the decision-making process. A novel approach, the Mayfly Taylor optimization algorithm (MTOA), for addressing the challenges in Multi-access Edge Computing (MEC) task scheduling. The key innovation lies in the integration of deep reinforcement learning (DRL) with graph attention neural networks (GATs) to create an effective scheduling strategy. The proposed algorithm outperforms several baseline methods by exploiting the spatial positional relationships between devices. It significantly reduces average latency, bandwidth, and dropout rates and increases in link usage efficiency. Overall, the proposed approach provides a solution to the task-scheduling problem in the MEC scenario that considers separable and time-sensitive activities while benefiting from the spatial relationships between devices. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks to higher-performance computing servers, thereby solving the problems of insufficient computing capacity and rapid battery consumption of TD. The emergence of Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs to access computing networks through multiple communication technologies and supports more mobility of terminal devices. Review studies on IoT task offloading and MEC have been extensive, but none of them focus on IoT task offloading in MEC. To fill this gap, this paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network. For each paper, the main problems solved by the mechanism, technical classification, evaluation methods, and supported parameters are extracted and analyzed. Furthermore, shortcomings of current research and future research trends are discussed. This review will help potential and new researchers quickly understand the panorama of IoT task offloading approaches in MEC and find appropriate research paths.

A tripartite incentive-enabled edge device resource sharing algorithm was proposed to manage and allocate the shared resources for the “cloud-edge-end” three-tier edge network architecture.Considering the single-point network congestion, network-wide coverage constraint, limited resource constraint and transmission cost constraint, the edge devices were abstracted into different functional nodes to design a convex envelope-based shared node (CESN) selection algorithm to achieve a full-coverage, low-energy shared resource deployment scheme.Secondly, to address the additional cost problem caused by allocating shared resources on edge devices, a tripartite sharing incentive mechanism was designed based on the shared resource leasing model, which guaranteed the long-term and stable participation of edge devices in the sharing mechanism.The simulation results show that the proposed algorithm significantly improves the system energy consumption and node revenue compared to the existing algorithms.