Multi-access Edge Research Articles

Resource management optimisation for federated learning-enabled multi-access edge computing in internet of vehicles

In internet of vehicles (IoVs), an edge server can process related computing tasks offloaded by vehicle-mounted terminals at a close distance based on cached application services. However, if the edge server caches all possible application services, the economic benefit of the server will be reduced owing to a large number of traffic service types. Additionally, vehicle-mounted terminals information may be leaked during data transmission. Therefore, we constructed an integrated framework of edge service and federated learning for IoVs. A resource management approach based on deep reinforcement learning (DRL) is also proposed for the joint optimisation of task offloading decisions, communication and computing resource allocation, as well as edge service caching placement in IoVs. The simulation results indicate that the DRL-based approach can overcome the high complexity of a network system and large space of policy selection and has good convergence performance as well as joint optimisation effect.

Task Offloading in Multi-Access Edge Computing Enabled UAV-Aided Emergency Response Operations

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAVs can perform. The problem is formulated as a non-convex mixed-integer nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush–Kuhn–Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems Under Resource Uncertainty

In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user’s satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework’s consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users’ experienced performance compared to alternative approaches.

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer’s a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoS-aware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedy-policy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

Application of Edge Computing-Based Information-Centric Networking in Smart Cities

Many data resources and network availability are needed for the smart city applications to execute at their highest efficiency level. Demand for these objects is driving up data traffic, which in turn is placing strain on the network. The 5G-enabled Internet of Things applications address these difficulties in smart city applications. This article proposes an Information-centric Networking System using Multiaccess Edge Computing (ICNMEC) to reduce computation offloading and optimize data traffic. This system's 5G network slicing approaches combine edge computing and software characterization. Internet of Things applications have been used to store and analyze the information gathered. In addition, an algorithm known as OMNM (Optimized Memory Network Management) is created to control network traffic better and better use of storage. With minimal delays, network traffic, and storage ratio, the system's modeling tests demonstrate that it is very efficient. This method can progressively enhance the pace at which one can access and use the system. The performance assessment shows that the proposed method can improve the efficiency ratio of 95.141%, storage utilization ratio of 60.1%, and access rate by 0.9, reducing network traffic and delay by 0.6.

Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely <inline-formula><tex-math notation="LaTeX">${{\sf MS-FEDL}}$</tex-math></inline-formula> , regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the block coordinate descent method and a decentralized JP-miADMM algorithm for solving the <inline-formula><tex-math notation="LaTeX">${{\sf MS-FEDL}}$</tex-math></inline-formula> problem. Different from the centralized approach, the decentralized approach requires many iterations to obtain but it allows each learning service to independently manage the local resource and learning process without revealing the learning service information. Our simulation results demonstrate the convergence performance of our proposed algorithms and the superior performance of our proposed algorithms compared to the heuristic strategy.

SPACE: Segment Prefetching and Caching at the Edge for Adaptive Video Streaming

Multi-access Edge Computing (MEC) is a new paradigm that brings storage and computing close to the clients. MEC enables the deployment of complex network-assisted mechanisms for video streaming that improve clients’ Quality of Experience (QoE). One of these mechanisms is segment prefetching, which transmits the future video segments in advance closer to the client to serve content with lower latency. In this work, for HAS-based (HTTP Adaptive Streaming) video streaming and specifically considering a cellular ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., 5G) network edge, we present our approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Segment Prefetching and Caching at the Edge for Adaptive Video Streaming (SPACE)</i> . We propose and analyze different segment prefetching policies that differ in resource utilization, player and radio metrics needed, and deployment complexity. This variety of policies can dynamically adapt to the network’s current conditions and the service provider’s needs. We present segment prefetching policies based on diverse approaches and techniques: past segment requests, segment transrating ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., reducing segment bitrate/quality), Markov prediction model, machine learning to predict future segment requests, and super-resolution.We study their performance and feasibility using metrics such as QoE characteristics, computing times, prefetching hits, and link bitrate consumption. We analyze and discuss which segment prefetching policy is better under which circumstances, as well as the influence of the client-side Adaptive Bit Rate (ABR) algorithm and the set of available representations (“bitrate ladder”) in segment prefetching. Moreover, we examine the impact on segment prefetching of different caching policies for (pre-)fetched segments, including Least Recently Used (LRU), Least Frequently Used (LFU), and our proposed popularity-based caching policy Least Popular Used (LPU).

A Deep Reinforcement Learning-Based Offloading Scheme for Multi-Access Edge Computing-Supported eXtended Reality Systems

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption.

Evaluating the Effectiveness of Machine Learning Algorithms in Detecting Distributed Denial of Service Attacks in Mobile Edge Computing

Mobile Edge Computing (MEC) is the last mile technology in 5G designed to reduce latency and to execute delay-sensitive applications closer to the end-user. MEC is also known as Multi-Access Edge Computing, which extends the capabilities of cloud computing to the network's edge and brings more capabilities closer to the user. By deploying cloud capabilities to the edge servers, MEC reduces latency and delay. As a result, the end-user experience is enhanced. Unfortunately, the MEC is susceptible to security challenges. Therefore, security is a challenge that requires attention. This paper investigates and compares the effectiveness of machine learning algorithms designed to detect distributed denial of service (DDoS) attacks in MEC. DDoS is one of the security concerns that are negating the advantages of MEC. It degrades the performance of the network. In this work, network and application layer DDoS attacks were considered. The study observed that the Support Vector Machine outperformed other machine learning techniques, and the findings are fundamental to the development of our proposed security framework for MEC.

Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner

The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods.

Multi-Task Partial Offloading with Relay and Adaptive Bandwidth Allocation for the MEC-Assisted IoT

The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers. However, the large communication delay at a low transmission rate for nodes far from the access point (AP) makes this offloading less meaningful. This paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, and it uses relay selection to help nodes far from the AP. The computation time of all tasks is minimized by adaptive task division and resource allocation (bandwidth and computation resource), and it is solved with an evolutionary algorithm. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms the methods with neither or only one function.

DRL-Based Dependent Task Offloading Strategies with Multi-Server Collaboration in Multi-Access Edge Computing

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm.

Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a two-staged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, nearly optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility

Multi-access edge computing (MEC) is an emerging technology of 5G that brings cloud computing benefits closer to the user. The current specifications of MEC describe the connectivity of mobile users and the MEC host, but they have issues with application-level security and privacy. We consider how to provide secure and privacy-preserving communication channels between a mobile user and a MEC application in the non-roaming case. It includes protocols for registration of the user to the main server of the MEC application, renewal of the shared key, and usage of the MEC application in the MEC host when the user is stationary or mobile. For these protocols, we designed a privacy-enhanced version of the 5G authentication and key management for applications (AKMA) service. We formally verified the current specification of AKMA using ProVerif and found a new spoofing attack as well as other security and privacy vulnerabilities. Then we propose a fix against the spoofing attack. The privacy-enhanced AKMA is designed considering these shortcomings. We formally verified the privacy-enhanced AKMA and adapted it to our solution.

Federated learning for 5G-enabled infrastructure inspection with UAVs

Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctioning equipment. Scaffolding is time-consuming and incurs accident risks. To tackle this challenges, grid operators are gradually using Unmanned Aerial Vehicles (UAVs). UAV trajectories are observed by a centralized operation center engineers for identifying electrical assets. Moreover, asset identification can be further automated through the use of Artificial Intelligence (AI) models. However, centralized training of AI models with UAV images may cause inspection delays when the network is overloaded and requires Cloud environments with enough processing power for model training on the operation center. This imposes privacy concerns as sensitive data is stored and processed externally from the infrastructure facility. This article proposes a federated learning method for UAV-based inspection that leverages a Multi-access Edge Computing platform installed in edge nodes to train UAV data and improve the overall inspection autonomy. The method is applied for the inspection of the Public Power Corporation’s Innovation Hub. Experiments are performed with the proposed method as well as with a centralized AI inspection method and demonstrate the federated learning benefits in reliability, AI model processing time and privacy conservation.

Resource Provisioning Techniques in Multi-Access Edge Computing Environments: Outlook, Expression, and Beyond

Mobile cloud computing promises a research foundation in information and communication technology (ICT). Multi-access edge computing is an intermediate solution that reduces latency by delivering cloud computing services close to IoT and mobile clients (MCs), hence addressing the performance issues of mobile cloud computing. However, the provisioning of resources is a significant and challenging process in mobile cloud-based environments as it organizes the heterogeneous sensing and processing capacities to provide the customers with an elastic pool of resources. Resource provisioning techniques must meet quality of service (QoS) considerations such as availability, responsiveness, and reliability to avoid service-level agreement (SLA) breaches. This investigation is essential because of the unpredictable change in service demands from diverse regions and the limits of MEC’s available computing resources. In this study, resource provisioning approaches for mobile cloud computing are thoroughly and comparatively studied and classified as taxonomies of previous research. The paper concludes with an insightful summary that gives recommendations for future enhancements.

Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

Billions of Internet of Things (IoT) devices and sensors are expected to be supported by fifth-generation (5G) wireless cellular networks. This highly connected structure is predicted to attract different and unseen types of attacks on devices, sensors, and networks that require advanced mitigation strategies and the active monitoring of the system components. Therefore, a paradigm shift is needed, from traditional prevention and detection approaches toward resilience. This study proposes a trust-based defense framework to ensure resilient IoT services on 5G multi-access edge computing (MEC) systems. This defense framework is based on the trustability metric, which is an extension of the concept of reliability and measures how much a system can be trusted to keep a given level of performance under a specific successful attack vector. Furthermore, trustability is used as a trade-off with system cost to measure the net utility of the system. Systems using multiple sensors with different levels of redundancy were tested, and the framework was shown to measure the trustability of the entire system. Furthermore, different types of attacks were simulated on an edge cloud with multiple nodes, and the trustability was compared to the capabilities of dynamic node addition for the redundancy and removal of untrusted nodes. Finally, the defense framework measured the net utility of the service, comparing the two types of edge clouds with and without the node deactivation capability. Overall, the proposed defense framework based on trustability ensures a satisfactory level of resilience for IoT on 5G MEC systems, which serves as a trade-off with an accepted cost of redundant resources under various attacks.

Providing Aerial MEC Service in Areas Without Infrastructure: A Tethered-UAV-Based Energy-Efficient Task Scheduling Framework

The evolution of information and communication technology, besides the proliferation of mobile devices, pushes the horizon of the Internet of Things. The main challenges for mobile devices are limited battery power and insufficient computational resources. Multiaccess edge computing (MEC) is an emerging paradigm that can provide task offloading services in the proximity of mobile devices to alleviate their load. In addition, mobile devices cannot work in emergency scenarios (e.g., post-earthquake, post-flood, and post-hurricane) or areas without infrastructure. Tethered unmanned aerial vehicles (UAVs) have received widespread attention as an alternative base station because of their line-of-sight solid links status, flexible deployment, and sufficient energy supply. With this in mind, we propose a tethered-unmanned-aerial-vehicle-based aerial MEC network to provide communication and task offloading services in areas without infrastructure. In addition, an energy-efficient task scheduling framework is proposed to achieve more energy-efficient task scheduling. First, we propose a geometry-based placement algorithm to generate optimal placement positions for the UAV placement problem. Then, for the nonconvex task scheduling and the resource allocation problem, we propose a low complexity divide and conquer scheme, which decomposes the original problem into three subproblems and solves them separately. Extensive simulations demonstrate the better energy efficiency of the proposed framework.

Reconfigurable intelligent surfaces-enabled edge computing: A location-aware task offloading framework

In this paper, an energy efficient task offloading mechanism in a Multiaccess Edge Computing (MEC) environment is introduced, based on the principles of contract theory. The technology of Reconfigurable Intelligent Surfaces (RISs) is adopted and serves as the enabler for energy efficient task offloading, from the perspective of location-awareness and improved communication environment. Initially a novel positioning, navigation, and timing solution is designed, based on the RIS technology and an artificial intelligent method that selects a set of RISs to perform the multilateration technique and determine the Internet of Things (IoT) nodes' positions in an efficient and accurate manner is introduced. Being aware of the nodes' positions, a maximization problem of the nodes' sum received signal strength at the MEC server where the nodes offload their computing tasks is formulated and solved, determining each RIS element's optimal phase shifts. Capitalizing on these enhancements, a contract-theoretic task offloading mechanism is devised enabling the MEC server to incentivize the IoT nodes to offload their tasks to it for further processing in an energy efficient manner, while accounting for the improved nodes' communications and computing characteristics. The performance evaluation of the proposed framework is obtained via modeling and simulation under different operation scenarios.

A novel vehicular task deployment method in hybrid MEC

AbstractWith the skyrocketing need for low-latency services on the Internet of Vehicles (IoV) and elastic cross-layer resource provisioning, multi-access edge computing (MEC) is considered a high-potent solution, which evolves from cloud and grid computing to meet the above needs in IoV scenarios. Instead of considering single-point and monolithic IoV tasks, in this paper, we consider the IoV applications to be with structural properties and the supporting environment to be with a hybrid cloud-edge architecture. We develop a scheduling method that offloads tasks to the eNode or cloud according to their estimations of latest starting time. Simulative results clearly demonstrate that our method beat existing solutions in terms of average completion time, average waiting time, and in-time completion rate.