Top Research Papers on Edge Computing

A substantive and analytic theory of what companies perceive about IoT edge computing, its benefits and challenges is derived from a survey using a semi-structured in-depth questionnaire and qualitatively analyzed data using the Constructivist Grounded Theory (Charmaz) method.

A virtual edge formation algorithm is designed that considers both the stability of virtual edge and the computational resources available at the vehicles constituting the virtual edge to reduce the number of computation offloading failures caused by unexpected link disconnections.

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.

Urgent Edge Computing (UEC) is introduced as a paradigm shift addressing the evolving demands of time-sensitive applications in distributed edge environments, in time-critical scenarios.

This paper aims to help practitioners and researchers better understand what the industry thinks about what IoT edge computing is, and the expected benefits and challenges associated with this paradigm.

This work first brings a discussion on existing MEC initiatives regarding the use cases they target and their vision, and surveys MEC practical initiatives according to their strategies, including the ETSI MEC standard.

A three-layer architecture of AEC is introduced, which includes the satellite, unmanned aircraft vehicles, and ground terminals, and the advanced technologies applied in AEC management, e.g., artificial intelligence (AI) and the distributed optimization are introduced.

An innovative edge server placement strategy, which combines the advantages of cluster-based method and heuristic schemes, is proposed in the paper and indicates that the proposed strategy can achieve lower communication delay and better workload balance simultaneously.

Two greedy algorithms are developed that outperform uniform strategies in terms of cost-effectiveness and ES utilization and for dynamic relocation of ES, it is shown that the cost required to relocate ES randomly is more as compared to the proposed algorithm Greedy_dynamic.

Simulation results show that the method proposed in this article can achieve higher performance in terms of delay, energy consumption, and price and provide personalized service for users.

A Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm, which makes use of edge devices computing power to fulfil the required computational cost for heavy tasks such as collision detection in virtual universe and computation of 3D physics in virtual simulation.

This paper proposes a dynamic offloading strategy to minimize the overall delay of tasks from terrestrial users in a SEC network, subject to the energy and computing capacity constraints of the LEO satellite.

This paper presents a comprehensive review through the comparative analysis of MCC, MEC, and MECC, focusing on key aspects such as architecture, resource allocation, latency reduction, and scalability.

A comprehensive case study on three typical and promising implementations of edge computing, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge is presented.

A cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable.

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.

An efficient edge-cloud collaborative caching strategy is proposed, which can reduce the delay of data migration while task execution and has better performance in terms of average data migration delay and application QoS.

This paper investigates how to efficiently place the edge servers (or racks) under the constraints of QoS latency, computation demands and site rental costs and proposes an efficient heuristic algorithm, namely, Offloading-Factor based edge Rack Placement (OFRP), which significantly outperforms the greedy-based algorithm.

A novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback is proposed, and it is proved that the algorithms converge to the Nash equilibrium with upper-bounded learning loss.

This paper proposes using edge computing principles to offload a subset of the local computations executed by games to a nearby edge server typically assigned for gaming applications and shows that EPS significantly reduces physics time compared to local execution.

This paper proposes a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further off loaded to the other low- load edge servers.

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.

V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available and acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks.

This paper induced a middleware system that allows an edge server to execute a task on the edge devices instead of offloading it to a cloud server and extends the concept of service-oriented architecture and integrate a decentralized peer-to-peer network architecture to achieve reusability, location-specific security, and reliability.

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.

A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time to optimize the system cost including execution time, energy consumption, and the ID rental price.

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.

This article provides a comprehensive review of intelligent computation offloading with critical issues, metrics and future directions.

Numerical results demonstrate that the distributed algorithm can achieve a guaranteed long-term performance, and also demonstrate the improvement in performance achieved over the case of computation offloading without collaborating edge nodes.

A cloud-edge collaboration hierarchical intelligent-driven VEC network architecture is first proposed, which utilizes the heterogeneous computing capabilities of cloud center, ag-gregation servers and MEC servers to achieve comprehensive collaboration and intelligent management of network re-sources.

This paper proposes a lightweight framework for collaborative nodes with decentralized edge intelligence organized like a swarm, which supports a paradigm shift from programming environments for individual devices to dynamic and cooperating groups of nodes.

An FA-enabled MEC scheme that aims to minimize the total system delay by leveraging the mobility of FA to enhance channel conditions and improve computational offloading efficiency is proposed.

This paper proposes and implements an inspection and corruption localization scheme for EDI named ICL-EDI, which allows service vendors to inspect data integrity and localize corrupted edge data cached on multiple edge servers accurately and efficiently.

This article designs a joint edge server deployment and service placement model with the goal of maximizing the overall profit of all edge servers under the constraints of the number of edge servers, the relationship among edge servers and base stations, the storage capacity, and the computing capacity of each edge server.

This work proposes a novel online service placement algorithm with a theoretical performance guarantee by leveraging Lyapunov optimization and Gibbs sampling that can improve the service coverage by $4.3\times $ compared with the baseline.

Experimental results on the public datasets of UCF-Crime and UR-Fall Detection show that the proposed S3D-2DCNN model outperforms the existing methods and achieves state-of-the-art performance.

An edge computing service architecture based on Information-Centric Networking is proposed, which contains the edge Computing service session model, service request forwarding strategies, and service dynamic deployment mechanism, which can avoid unnecessary network transmissions and reduce the service completion time.

An overview of the methods used in Federated learning is provided with a focus on edge devices with limited computational resources and frameworks that are currently popular and that provide communication between clients and servers are presented.

This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solutions in diverse mobile applications.

This paper designs a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities, and develops a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance.

This paper describes use of edge computing technology in IoT and computer vision applications, construction of computers on edge and also provides insight into the most prominent key players in this domain.

A trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment and a series of well-defined security authentication microservices that use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment.

This work proposes a micro-service deployment strategy based on edgeedge collaboration, which takes the correlation between micro-services into account and models it as Service Function Chain (SFC), aiming to reduce the delay and balance the utilization rate in the edge cluster.

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.

This paper provides a new approach to improve performance of the edge computing by having two identical computing systems in which one system will act as primary and another as reserved or secondary, this system will be available at the local environment of the IoT device and not in cloud.

A computing platform called “NorthPole” is described that facilitates high inference speed and prediction accuracy but with a moderate energy requirement, a promising step toward chip designs that support low-power edge AI inference.

The research issues along this line are elaborated, including basic concepts and performance metrics, killer applications, architectural design, modeling approaches and solutions, and future research directions.

This paper proposes a new network-assisted computing model, namely FPGA-based edge computing, and experimentally demonstrates that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU- based edge/cloud offloading approach.

A thorough overview of current developments in edge computing is provided while emphasising the key applications and in real-world situations where response time is a crucial need for many applications, it also examines the significance of edge computing.

Progress in low-power embedded electronics over the last decade has spawned a new breed of small, low-cost spacecraft, and a burgeoning commercial industry around them, while also looking forward to the tremendous opportunities at the intersection of embedded computing and space exploration.