Edge computing

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

Efficient and dynamic scaling of fog nodes for IoT devices

Abstract: It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.

Secure and Energy-Efficient Handover in Fog Networks Using Blockchain-Based DMM

Abstract: Modern fog network architectures, empowered by IoT applications and 5G communications technologies, are characterized by the presence of a huge number of mobile nodes, which undergo frequent handovers, introducing a significant load on the involved network entities. Considering the distributed and flat nature of these architectures, DMM can be the only viable option for efficiently managing handovers in these scenarios. The existing DMM solutions are capable of providing smooth handovers, but lack robustness from the security point of view. Indeed, DMM depends on external mechanisms for handover security and uses a centralized device, which has obvious security and performance implications in flat architectures where hierarchical dependencies can introduce problems. We propose a new DMM schema based on the blockchain, capable of resolving hierarchical security issues without affecting the network layout, and also satisfying fully distributed security requirements with less consumption of energy.

Toward Enabled Industrial Verticals in 5G: A Survey on MEC-Based Approaches to Provisioning and Flexibility

Abstract: The increasing number of heterogeneous devices connected to the Internet, together with tight 5G requirements have generated new challenges for designing network infrastructures. Industrial verticals such as automotive, smart city and eHealthcare (among others) need secure, low latency and reliable communications. To meet these stringent requirements, computing resources have to be moved closer to the user, from the core to the edge of the network. In this context, ETSI standardized Multi-Access Edge Computing (MEC). However, due to the cost of resources, MEC provisioning has to be carefully designed and evaluated. This survey firstly overviews standards, with particular emphasis on 5G and virtualization of network functions, then it addresses flexibility of MEC smart resource deployment and its migration capabilities. This survey explores how the MEC is used and how it will enable industrial verticals.

NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things

Abstract: Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.

Edge computing in smart health care systems: Review, challenges, and research directions

Abstract: Funding information National Science Foundation, Grant/Award Number: 1619346, 1559483, 1718956 and 1730650 Abstract Today, patients are demanding a newer and more sophisticated health care system, one that is more personalized and matches the speed of modern life. For the latency and energy efficiency requirements to be met for a real-time collection and analysis of health data, an edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Previous health care surveys have focused on new fog architecture and sensor types, which leaves untouched the aspect of optimal computing techniques, such as encryption, authentication, and classification that are used on the devices deployed in an edge computing architecture. This paper aims first to survey the current and emerging edge computing architectures and techniques for health care applications, as well as to identify requirements and challenges of devices for various use cases. Edge computing application primarily focuses on the classification of health data involving vital sign monitoring and fall detection. Other low-latency applications perform specific symptom monitoring for diseases, such as gait abnormalities in Parkinson's disease patients. We also present our exhaustive review on edge computing data operations that include transmission, encryption, authentication, classification, reduction, and prediction. Even with these advantages, edge computing has some associated challenges, including requirements for sophisticated privacy and data reduction methods to allow comparable performance to their Cloud-based counterparts, but with lower computational complexity. Future research directions in edge computing for health care have been identified to offer a higher quality of life for users if addressed.