Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing

Abstract: As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.

Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing

Abstract: Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

Resource Provisioning for IoT Services in the Fog

Abstract: The advent of the Internet of Things (IoT) leadsto the pervasion of business and private spaces with ubiquitous, networked computing devices. These devices do not simply actas sensors, but feature computational, storage, and networkingresources. These resources are close to the edge of the network, and it is a promising approach to exploit them in order to executeIoT services. This concept is known as fog computing.Despite existing theoretical foundations, the adoption of fogcomputing is still at its very beginning. Especially, there is alack of approaches for the leasing and releasing of resources. Toresolve this shortcoming, we present a conceptual framework forfog resource provisioning. We formalize an optimization problemwhich is able to take into account existing resources in fog/IoTlandscapes. The goal of this optimization problem is to providedelay-sensitive utilization of available fog-based computationalresources. We evaluate the resource provisioning model to showthe benefits of our contributions. Our results show a decrease indelays of up to 39% compared to a baseline approach, yieldingshorter round-trip times and makespans.

Interaction of Edge-Cloud Computing Based on SDN and NFV for Next Generation IoT

Abstract: In order to improve the stability of mobile network system for application of the next generation of Internet of Things (IoT), balance the network load and guarantee the quality of user service experience, this article first introduces the computing migration framework for the network of the next generation, and summarizes the concept and content of mobile edge computing (MEC) using software-defined network (SDN) and network function virtualization (NFV). And then, this article proceeds to introduce the MEC strategy based on SDN and NFV technology as well as multiattribute decision making, computing migration, multiattribute decision, the MEC decision model based on SDN and NFV technology and the solving process of the MEC decision model based on SDN and NFV. Finally, the three sets of simulation experiments based on MATLAB are designed to validate the multiattribute decision of MEC migration strategy based on SDN and NFV. The results show that the multiattribute decision making based on SDN and NFV can select the appropriate MEC center, further reduce the server response time and improve the quality of user service experience. This article is of great significance to the application of IoT terminal in the next generation of network environment.