Edge computing

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

Edge computing in IoT context: Horizontal and vertical Linux container migration

Abstract: In recent years, the development of the Internet of Things (IoT) has largely been supported by the parallel development of the Cloud computing capacities. Cloud computing offered elastic and on-demand computing power able to host and support crucial IoT functions. However, the proliferation of connected objects poses the problem of the confidentiality of the data produced. This proliferation also threatens the performances of the supporting networks. To tackle those problems, the research community has recently turned to Fog Computing. In this paper, we present CIoud4IoT, a platform able to perform horizontal (roaming) and vertical (offloading) migration of IoT functions. We implement our demonstration using a Kubernetes cIuster organised in three tiers: Cloud, Edge and IoT gateways. Our first use cases shows how IoT function roaming can be used in the context of health care data exploitation. The second use case exploits the IoT offloading capacity of our platform to optimize the remote diagnostic of mechanical engines.

QoE-Assured Live Streaming via Satellite Backhaul in 5G Networks

Abstract: Satellite communication has recently been included as one of the key enabling technologies for 5G backhauling, especially for the delivery of bandwidth-demanding enhanced mobile broadband (eMBB) applications in 5G. In this paper, we present a 5G-oriented network architecture that is based on satellite communications and multi-access edge computing to support eMBB applications, which is investigated in the EU 5GPPP phase-2 satellite and terrestrial network for 5G project. We specifically focus on using the proposed architecture to assure quality-of-experience (QoE) of HTTP-based live streaming users by leveraging satellite links, where the main strategy is to realize transient holding and localization of HTTP-based (e.g., MPEG-DASH or HTTP live streaming) video segments at 5G mobile edge while taking into account the characteristics of satellite backhaul link. For the very first time in the literature, we carried out experiments and systematically evaluated the performance of live 4K video streaming over a 5G core network supported by a live geostationary satellite backhaul, which validates its capability of assuring live streaming users’ QoE under challenging satellite network scenarios.

Vehicles as Connected Resources: Opportunities and Challenges for the Future

Abstract: With the introduction of smartphones, cloud and edge computing, and mobile Internet, the automotive ecosystem is shifting toward the Internet of Vehicles (IoV). This article looks at the evolution leading to the IoV and identifies related research and engineering challenges, including 1) coexistence of cloud, edge computing, and data caching strategies at the edge; 2) integration of data processing and management as IoV services; and 3) seamless interoperability among vehicular sensors, computing platforms, and consumer devices. To address these challenges, we present an Internet of Things (IoT) architecture that considers vehicles as IoT resources and provides 1) mechanisms to integrate them in an IoV ecosystem and 2) seamless interoperation among components (e.g., vehicular sensors, computational platform, and consumers). The functional elements and operational stages of the architecture also assist in maintaining interoperability among the components.

Secure Service Offloading for Internet of Vehicles in SDN-Enabled Mobile Edge Computing

Abstract: Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure s ervice o ffloading me thod, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

On the Design of Computation Offloading in Fog Radio Access Networks

Abstract: Based on a hierarchical cloud-fog computing-enabled paradigm, fog radio access networks (F-RANs) can provide abundant resource to support the future mobile artificial intelligent services. However, due to the differences of computation and communication capabilities at the cloud computing center, the fog computing based access points (F-APs), and the user devices, it is challenging to propose efficient computation offloading strategies to fully explore the potential of F-RANs. In this paper, we study the design of computation offloading in F-RANs to minimize the total cost with respect to the energy consumption and the offloading latency. In particular, a joint optimization problem is formulated to optimize the offloading decision, the computation and the radio resources allocation. To solve this non-linear and non-convex problem, an iterative algorithm is designed, which can be proved to converge a stationary optimal solution with polynomial computational complexity. Finally, the simulation results are provided to show the performance gains of our proposed joint optimization algorithm.