Top Research Papers on Edge Computing

It is shown quantitatively that nanosatellite constellation capabilities are determined by physical system constraints, and an OEC architecture can reduce ground infrastructure over 24x compared to a bent-pipe architecture, and pipelines can reduce system edge processing latency over 617x.

This work proposes a dependency-aware offloading scheme in MEC with edge-cloud cooperation under task dependency constraints with an efficient greedy algorithm, considering both edge- cloud and edge-edge co-operations.

This work presents some important edge computing architectures and classify the previous works on computation offloading into different categories, and discusses some basic models such as channel model, computation and communication model, and energy harvesting model that have been proposed in offloading modeling.

The former focuses on providing more optimal solutions to key problems in edge computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge.

The concept of edge computing is summarized and compares it with cloud computing, the architecture of edge Computing, keyword technology, security and privacy protection, and the applications are summarized.

Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing optimization problems in CC and EC.

Computation offloading is one of the key technologies in Mobile Edge Computing (MEC), which makes up for the deficiencies of mobile devices in terms of storage resource, computing capacity, and energy efficiency. On one hand, computation offloading of task requests not only relieves the communication pressure on the core networks but also reduces the delay caused by long-distance data transmission. On the other hand, emerging applications in 5/6G also rely on the computation offloading technology for efficient service provisioning to users. At present, the industry and academia have conducted ...

The robustness of the services collectively delivered by the service provider’s applications deployed on the edge servers has not been considered at all, a critical issue, especially in the highly distributed, dynamic and volatile edge computing environment.

In this paper, an in-depth analysis promotes a broad vision for bringing Serverless to the Edge Computing and issues major challenges for serverless to be met before entering Edge computing.

An up-to-date survey of the edge computing research is presented, introducing the definition, model, and characteristics of edge computing, and discussing a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era.

This article analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5Gs and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading.

The architecture of edge computing is presented, under which different collaborative manners for resource scheduling are discussed, and a unified model is introduced before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning are summarized.

The integration of MEC into a current mobile networks’ architecture as well as the transition mechanisms to migrate into a standard 5G network architecture are illustrated and an architectural framework for a MEC-NFV environment based on the standard SDN architecture is proposed.

This article discusses critical challenges in service latency, energy consumption, security, privacy, and reliability during the integration of CPS with edge computing or edge-cloud computing, and gives an overview on the state-of-the-art works tackling different challenges for QoS optimization.

Edge computing is an emerging paradigm for the increasing computing and networking demands from end devices to smart things. Edge computing allows the computation to be offloaded from the cloud data centers to the network edge and edge nodes for lower latency, security and privacy preservation. Although energy efficiency in cloud data centers has been broadly investigated, energy efficiency in edge computing is largely left uninvestigated due to the complicated interactions between edge devices, edge servers, and cloud data centers. In order to achieve energy efficiency in edge computing, a sy...

With the increasing growth of internet-of-things (IoT) devices, effective computation performance has become a critical issue. Many services provided by IoT devices (e.g., augmented reality, location-tracking, traffic systems, and autonomous driving) require intensive real-time data processing, which demands powerful computational resources. Mobile edge computing (MEC) has been introduced to effectively handle this problem reliably over the internet. The inclusion of a MEC server allows computationally intensive tasks to be offloaded from IoT devices. However, communication overhead and delays...

Handling the massive amount of data generated by Smart Mobile Devices (SMDs) is a challenging computational problem. Edge Computing is an emerging computation paradigm that is employed to conquer this problem. It can bring computation power closer to the end devices to reduce their computation latency and energy consumption. Therefore, this paradigm increases the computational ability of SMDs by collaboration with edge servers. This is achieved by computation offloading from the mobile devices to the edge nodes or servers. However, not all applications benefit from computation offloading, whic...

This article establishes an offloading framework with communication and computation for VEC, and proposes a particle swarm optimization based computation offloading (PSOCO) algorithm to obtain the Pareto-optimal solutions to the multi-objective optimization problem.

A new group-agent strategy with trust computing is designed to ensure the reliability of edge devices during interactions and improve transmission efficiency and a stacked task sorting and ranking mechanism which improves resource allocation in each edge device is introduced.

This letter proposes and formulate a practical end-to-end latency minimization problem in the DT-assisted MEC model subject to the constraints of quality-of-services and computation resource at the IoT devices and MEC servers in industrial IoT networks and solves the proposed latency minimizations problem by iteratively optimizing the transmit power of IoT devices, user association, intelligent task offloading, and estimated CPU processing rate of the devices.

This article proposes a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity and shows that the proposed algorithm can effectively reduce the total energy consumption of ground users.

Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness and a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits.

Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

An efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback and an optimal cloud computing resource management to maximize the social welfare is proposed.

A new UAV-assisted edge computing framework is presented, which jointly optimizes the trajectory and CPU frequency of a fixed-wing UAV, and the offloading schedule to minimize the energy consumption of the UAV.

This paper proposes a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC, and shows that the proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

A UAVs-assisted computation offloading paradigm, where a group of Uavs fly around, while providing value-added edge computing services, and proposes multi-agent reinforcement learning (MARL) algorithms, where the target helper and the bandwidth allocation are determined by two agents.

The prototype-based evaluation indicates that the intelligent cooperative edge (ICE) computing architecture enables a benign combination of AI and edge computing, which helps some key issues of edge computing achieve a better solution using the localized AI.

The impact of edge computing on the development of IoT is introduced and why edge computing is more suitable for IoT than other computing paradigms are pointed out and lessons learned are concluded.

A systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems is provided to help researchers better understand the connection between FL and EC enabling technologies and concepts.

The work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages.

An online algorithm, called CEDC-O, is proposed, developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance.

With the advent of Internet of Things (IoT) connecting billions of mobile and stationary devices to serve real-time applications, cloud computing paradigms face some significant challenges such as high latency and jitter, non-supportive location-awareness and mobility, and non-adaptive communication types. To address these challenges, edge computing paradigms, namely Fog Computing (FC), Mobile Edge Computing (MEC) and Cloudlet, have emerged to shift the digital services from centralized cloud computing to computing at edges. In this article, we analyze cloud and edge computing paradigms from f...

A novel learning scheme, edge-assisted federated learning (EAFL), which enables stragglers to offload partial computation to the edge server, and leverages the server's idle computing power to assist clients in model training is proposed.

A novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading is proposed.

A multiuser noncooperative computation offloading game to adjust the offloading probability of each vehicle in vehicular MEC networks and design the payoff function considering the distance between the vehicle and MEC access point, application and communication model, and multivehicle competition for MEC resources is proposed.

This article proposes the network architecture and layered structure of serverless edge computing networks from the perspective of networking, and presents the communication process, as well as the implementation and deployment.

This article presents a wireless powered mobile-edge computing system consisting of a hybrid access point and multiple cooperative fogs, where the users can share communication and computation resources to improve their computation performance.

A two-tier MEC system is studied, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers.

A joint partial offloading and flow scheduling heuristic (JPOFH) that decidespartial offloading ratio by considering both waiting times at the devices and start time of network flows is proposed.