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Edge Computing and Networked Sensing in 6G Network

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 21744

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Special Issue Editors

Dr. Muddesar Iqbal

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Guest Editor

Senior Lecturer in Mobile Computing at the Division of Computer Science and Informatics (CSI), London South Bank University, London, UK
Interests: mobile security; IoT; privacy requirements
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xinheng Wang

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool Univesity, Suzhou 215123, China
Interests: tactile internet; indoor positioning and data services; acoustic localisation, communications and sensing; condition monitoring of power systems; networked mechatronic systems; navigation and services for group users; industrial IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

6G networks are envisaged to support the requisite resources for the fast-paced digital transformation through industry 5.0 applications including, but not limited to, holographic communication, self-driving vehicles, context-aware infrastructure, and personalized interfaces. These applications require 6G infrastructures to be able to support massive IoT connectivity, with ultra-low latency and high bandwidth. Edge computing is a consolidated paradigm that is widely recognized as a key enabler for future 6G communication networks. By bringing intelligence and resources closer to the end-users and devices, edge computing is the key technology that can help in achieving zero latency dream to support 6G application scenarios.

In addition to that, this trend is moving from edge computing to a wider concept of distributed computing, characterized by the presence of multiple application instances, both at the user and edge sides. The potential of edge computing (and in particular its evolution toward distributed cloud) fits multiple modern communication technologies, ranging from cellular networks (5G, Beyond 5G, and even 6G) to the wireless sensor and actuator networks, and paves the way for novel and even more pervasive IoT applications. As this technology becomes more and more adopted in the above fields, new research challenges are being posed.

Distributed cloud in its various facets is undoubtedly a hot topic in research. Thanks to its interdisciplinary nature, it represents a strong driver for many scientific fields. In this Special Issue, we aim at collecting research contributions from multiple fields, and we invite academic and industry researchers in computer science and engineering, electrical engineering, and communication engineering, as well as ICT industry engineers and practitioners, to contribute with original articles in all aspects of edge-based 6G enabling technologies, having a particular focus on the IoT and Industrial IoT (IIoT) context. The topics of interest include but are not limited to:

Edge-computing based applications for the IoT;
Novel architectures and application for distributed edge computing;
IoT-based service-migration, orchestration and adaptive management at the network edge;
Edge-based support for 6G architecture for industrial IoT applications;
Distributed and federated learning approaches for edge computing;
Artificial intelligence/machine learning to support optimization for network slicing at the network edge;
Edge-based support for Ultra-Reliable Low Latency Communications (URLLC) and critical applications;
Modelling, system architecture and deployment for edge-based 6G applications;
Edge-based mobility as a service;
Edge computing for smart materials;
Edge computing for multi-object tracking;
Edge-based gamming;
Edge computing for supper intelligent IoT;
Edge-based Deep/Reinforcement/AI/ML Model Training;
Edge computing for situational and contextual data processing;
Edge-based industry 5.0 applications and technologies;
Performance evaluation of edge computing systems;
Blockchain and security for edge computing;
Edge computing for embedded systems;
IIoT and intelligent manufacturing.

Dr. Muddesar Iqbal
Prof. Dr. Xinheng Wang
Guest Editors

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Published Papers (5 papers)

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Research

43 pages, 4675 KiB

Open AccessArticle

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

by Moustafa M. Nasralla, Sohaib Bin Altaf Khattak, Ikram Ur Rehman and Muddesar Iqbal

Sensors 2023, 23(13), 5882; https://doi.org/10.3390/s23135882 - 25 Jun 2023

Cited by 30 | Viewed by 11496

Abstract

Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications. Full article

(This article belongs to the Special Issue Edge Computing and Networked Sensing in 6G Network)

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29 pages, 13208 KiB

Open AccessArticle

A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network

by Farhan Javed, Zuhaib Ashfaq Khan, Shahzad Rizwan, Sonia Shahzadi, Nauman Riaz Chaudhry and Muddesar Iqbal

Sensors 2023, 23(13), 5817; https://doi.org/10.3390/s23135817 - 22 Jun 2023

Cited by 5 | Viewed by 1964

Abstract

The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%. Full article

(This article belongs to the Special Issue Edge Computing and Networked Sensing in 6G Network)

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10 pages, 3706 KiB

Open AccessArticle

Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm

by Muhammad Alolaiwy and Mohamed Zohdy

Sensors 2023, 23(3), 1100; https://doi.org/10.3390/s23031100 - 18 Jan 2023

Cited by 1 | Viewed by 1649

Abstract

With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a

90 % +

packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption. Full article

(This article belongs to the Special Issue Edge Computing and Networked Sensing in 6G Network)

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17 pages, 2363 KiB

Open AccessArticle

Multi-Model Running Latency Optimization in an Edge Computing Paradigm

by Peisong Li, Xinheng Wang, Kaizhu Huang, Yi Huang, Shancang Li and Muddesar Iqbal

Sensors 2022, 22(16), 6097; https://doi.org/10.3390/s22166097 - 15 Aug 2022

Cited by 7 | Viewed by 3352

Abstract

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable multiple model inference tasks to be conducted concurrently on resource-constrained edge devices, allowing us to achieve one goal collaboratively rather than getting high quality in each standalone task. However, the high overall running latency for performing multi-model inferences always negatively affects the real-time applications. To combat latency, the algorithms should be optimized to minimize the latency for multi-model deployment without compromising the safety-critical situation. This work focuses on the real-time task scheduling strategy for multi-model deployment and investigating the model inference using an open neural network exchange (ONNX) runtime engine. Then, an application deployment strategy is proposed based on the container technology and inference tasks are scheduled to different containers based on the scheduling strategies. Experimental results show that the proposed solution is able to significantly reduce the overall running latency in real-time applications. Full article

(This article belongs to the Special Issue Edge Computing and Networked Sensing in 6G Network)

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16 pages, 8361 KiB

Open AccessArticle

A Mechanical Modelling and Simulation Method for Resolving PIM Problems in Antennas

by Chen Chen and Yangyang Gu

Sensors 2022, 22(1), 294; https://doi.org/10.3390/s22010294 - 31 Dec 2021

Cited by 1 | Viewed by 2016

Abstract

Passive intermodulation (PIM) generated from antennas is a nonlinear distortion phenomenon and causes serious problems to communication quality. Traditional radio frequency (RF) solutions focus on testing the final product to find the PIM source. However, it cannot solve the stability of PIM after the antenna is vibrated. This paper introduces a new method to improve the stability of PIM in the design phase. By studying the mechanism of PIM generation, a simulation method is proposed in this paper by applying mechanical finite element simulation and simulating the structural design of the device under test. Then, the stress at the PIM source is reduced, thereby the PIM stability of the product is improved. This paper adopts this method by studying a typical product, finding the root cause that affects the product PIM magnitude and stability, and optimizing its design. The PIM value of the new scheme is stable by making a prototype and testing. The method provided in this article can effectively improve product development efficiency and assist designers in avoiding the risks of PIM before the product’s manufacturing. Full article

(This article belongs to the Special Issue Edge Computing and Networked Sensing in 6G Network)

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