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3 pages, 160 KiB

Open AccessEditorial

Edge/Fog Computing Technologies for IoT Infrastructure II

by Taehong Kim, Seong-eun Yoo and Youngsoo Kim

Sensors 2023, 23(8), 3953; https://doi.org/10.3390/s23083953 - 13 Apr 2023

Viewed by 1166

Abstract

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices [...] Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

Research

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13 pages, 2335 KiB

Open AccessArticle

Local Scheduling in KubeEdge-Based Edge Computing Environment

by Seong-Hyun Kim and Taehong Kim

Sensors 2023, 23(3), 1522; https://doi.org/10.3390/s23031522 - 30 Jan 2023

Cited by 8 | Viewed by 3575

Abstract

KubeEdge is an open-source platform that orchestrates containerized Internet of Things (IoT) application services in IoT edge computing environments. Based on Kubernetes, it supports heterogeneous IoT device protocols on edge nodes and provides various functions necessary to build edge computing infrastructure, such as [...] Read more.

KubeEdge is an open-source platform that orchestrates containerized Internet of Things (IoT) application services in IoT edge computing environments. Based on Kubernetes, it supports heterogeneous IoT device protocols on edge nodes and provides various functions necessary to build edge computing infrastructure, such as network management between cloud and edge nodes. However, the resulting cloud-based systems are subject to several limitations. In this study, we evaluated the performance of KubeEdge in terms of the computational resource distribution and delay between edge nodes. We found that forwarding traffic between edge nodes degrades the throughput of clusters and causes service delay in edge computing environments. Based on these results, we proposed a local scheduling scheme that handles user traffic locally at each edge node. The performance evaluation results revealed that local scheduling outperforms the existing load-balancing algorithm in the edge computing environment. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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19 pages, 3127 KiB

Open AccessArticle

Autonomous Mutual Authentication Protocol in the Edge Networks

by Ruey-Kai Sheu, Mayuresh Sunil Pardeshi and Lun-Chi Chen

Sensors 2022, 22(19), 7632; https://doi.org/10.3390/s22197632 - 8 Oct 2022

Cited by 4 | Viewed by 2242

Abstract

A distinct security protocol is necessary for the exponential growth in intelligent edge devices. In particular, the autonomous devices need to address significant security concern to function smoothly in the high market demand. Nevertheless, exponential increase in the connected devices has made cloud [...] Read more.

A distinct security protocol is necessary for the exponential growth in intelligent edge devices. In particular, the autonomous devices need to address significant security concern to function smoothly in the high market demand. Nevertheless, exponential increase in the connected devices has made cloud networks more complex and suffer from information processing delay. Therefore, the goal of this work is to design a novel server-less mutual authentication protocol for the edge networks. The aim is to demonstrate an autonomous mutual authentication amongst the connected smart devices within the edge networks. The solution addresses applications of autonomous cars, smart things, and Internet of Things (IoT) devices in the edge or wireless sensor networks (WSN), etc. In this paper, the design proposes use of a public-key system, octet-based balanced-tree transitions, challenge–response mechanism, device unique ID (UID), pseudo-random number generator (PRNG), time-stamps, and event specific session keys. Ultimately, server-less design requires less infrastructure and avoids several types of network-based communication attacks, e.g., impersonating, Man in the middle (MITM), IoT-DDOS, etc. Additionally, the system overhead is eliminated by no secret key requirements. The results provide sufficient evidence about the protocol market competitiveness and demonstrate better benchmark comparison results. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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12 pages, 2853 KiB

Open AccessArticle

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

by JongBeom Lim

Sensors 2022, 22(19), 7326; https://doi.org/10.3390/s22197326 - 27 Sep 2022

Cited by 9 | Viewed by 1880

Abstract

The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per [...] Read more.

The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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13 pages, 1927 KiB

Open AccessArticle

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach

by Hongrok Choi and Sangheon Pack

Sensors 2022, 22(18), 6853; https://doi.org/10.3390/s22186853 - 10 Sep 2022

Cited by 5 | Viewed by 2409

Abstract

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites’ high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose [...] Read more.

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites’ high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes inter-satellite communication links (ISLs) to fully utilize satellites’ downloading capabilities. To this end, we formulate a Markov decision problem (MDP) with the objective to maximize the amount of downloaded data. To learn the optimal approach to the formulated problem, we adopt a soft-actor-critic (SAC)-based DRL algorithm in discretized action spaces. Moreover, we design a novel neural network consisting of a graph attention network (GAT) layer to extract latent features from the satellite network and parallel fully connected (FC) layers to control individual satellites of the network. Evaluation results demonstrate that the proposed DRL-based cooperative downloading scheme can enhance the average utilization of contact time by up to 17.8% compared with independent downloading and randomly offloading schemes. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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18 pages, 1457 KiB

Open AccessArticle

Collaborative Task Offloading and Service Caching Strategy for Mobile Edge Computing

by Xiang Liu, Xu Zhao, Guojin Liu, Fei Huang, Tiancong Huang and Yucheng Wu

Sensors 2022, 22(18), 6760; https://doi.org/10.3390/s22186760 - 7 Sep 2022

Cited by 8 | Viewed by 1892

Abstract

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge [...] Read more.

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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20 pages, 4255 KiB

Open AccessArticle

Distributed Agent-Based Orchestrator Model for Fog Computing

by Agnius Liutkevičius, Nerijus Morkevičius, Algimantas Venčkauskas and Jevgenijus Toldinas

Sensors 2022, 22(15), 5894; https://doi.org/10.3390/s22155894 - 7 Aug 2022

Cited by 4 | Viewed by 1726

Abstract

Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource [...] Read more.

Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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18 pages, 1745 KiB

Open AccessArticle

Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach

by Md. Delowar Hossain, Tangina Sultana, Md. Alamgir Hossain, Md. Abu Layek, Md. Imtiaz Hossain, Phoo Pyae Sone, Ga-Won Lee and Eui-Nam Huh

Sensors 2022, 22(10), 3678; https://doi.org/10.3390/s22103678 - 12 May 2022

Cited by 7 | Viewed by 2506

Abstract

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, [...] Read more.

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme. Full article

(This article belongs to the Special Issue Edge/Fog Computing Technologies for IoT Infrastructure II)

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