Edge computing

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

IoT Stream Processing and Analytics in the Fog

Abstract: The emerging fog paradigm has been attracting increasing interest from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide Many fog applications, such as video mining and event monitoring, rely on data stream processing and analytics, which are very popular in the cloud, but have not been comprehensively investigated in the context of fog architecture In this article, we present the general models and architecture of fog data streaming, by analyzing the common properties of several typical applications We also analyze the design space of fog streaming with the consideration of four essential dimensions (system, data, human, and optimization), where both new design challenges and the issues that arise from leveraging existing techniques are investigated, such as cloud stream processing, computer networks, and mobile computing

Quality of Service Optimization in an IoT-Driven Intelligent Transportation System

Abstract: High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms.

Energy Efficient Optimization for Computation Offloading in Fog Computing System

Abstract: In this paper, we investigate the energy efficient computation offloading scheme in a multi-user fog computing system. We consider the users need to make the decision on whether to offload the tasks to the fog node nearby, based on the energy consumption and delay constraint. In particular, we utilize queuing theory to bring a thorough study on the energy consumption and execution delay of the offloading process. Two queuing models are applied respectively to model the execution processes at the mobile device (MD) and fog node. Based on the theoretical analysis, an energy efficient optimization problem is formulated with the objective to minimize the energy consumption subjects to execution delay constraints. In order to address the formulated problem, an alternating direction method of multipliers (ADMM)-based distributed algorithm is proposed. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over the other existed schemes can be observed.

Indie Fog: An Efficient Fog-Computing Infrastructure for the Internet of Things

Abstract: Fog computing can help with some of the Internet of Things’ limitations but also faces challenges of its own. The Indie Fog infrastructure could be a flexible, cost-effective way to cope with these challenges.

Computation Offloading in LEO Satellite Networks With Hybrid Cloud and Edge Computing

Abstract: Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users