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.