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Emerging Technologies in Edge Computing and Networking

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 22661

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Special Issue Editors

Dr. Javier Prieto

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Guest Editor

BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial intelligence; blockchain; deep learning; satellite systems; robot vision; cognitive robotics; sensor fusion; data fusion; mobile robotics; wireless networks; robotics; security; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. Ramón J. Durán Barroso

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Guest Editor

Paseo de Belén, Universidad de Valladolid, 15, 47011 Valladolid, Spain
Interests: optical networks; IoT; artificial intelligence; SDN; network virtualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global evolution of the Internet is experiencing a notable and inevitable change towards a convergent scenario known as the Internet of Things (IoT), where a large number of devices with heterogeneous characteristics and requirements have to be interconnected to serve different verticals, such as smart cities, intelligent transportation systems (ITS) or e-health. In order to satisfy the strict requirements related to end-to-end latency and the scalability of new services, as well as the processing of the massive volume of data that they generate, it is necessary to design and deploy a decentralized service platform architecture based on MEC (multi-access edge computing) and/or fog computing, with the aim of reducing the distance between the source of data and the processing resources. Moreover, 5G, and especially 6G, have been proposed to fulfil the strict latency and scalability constraints, providing cell-less multiaccess networks controlled by efficient artificial intelligence techniques and supported by MEC. All these elements are forcing a holistic redesign of communication networks and distributed computing and storage architectures to maximize the performance/cost ratio, meeting the demanding challenges of latency, scalability, bandwidth, availability and flexibility that future services will require. The softwarization and virtualization of networks using SDN and NFV paradigms are essential technologies for that convergence. The implementation of an open control layer based on them will facilitate the management of the multiple computing and networking technologies that will coexist and will make it possible to face the needs and requirements of 5G/6G associated with future applications and services. Moreover, they eliminate the so-called vendor lock-in in favor of a vendor-independent programmable network environment that allows the assembly of different devices (white-boxes) manufactured by multiple providers.

Researchers from edge computing and networking communities are encouraged to submit papers to this Special Issue with their contributions, exploring emerging technologies that allow the integration of future edge computing and networks.

Dr. Javier Prieto
Dr. Ramón J. Durán Barroso
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

multi-access edge computing
5G/6G
distributed computing
orchestration and management
MEC-enabled networks
control and planning
convergence of wireless/optical infrastructures
network virtualization
software-defined networking
network slicing
computation offloading
power consumption models
techno-economic models
life cycle model for data
artificial intelligence for IoT (AIoT)
Industrial IoT (IIoT)
security and/or privacy

Published Papers (12 papers)

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Editorial

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5 pages, 193 KiB

Open AccessEditorial

Emerging Technologies in Edge Computing and Networking

by Javier Prieto and Ramón J. Durán Barroso

Sensors 2024, 24(4), 1271; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041271 - 17 Feb 2024

Viewed by 757

Abstract

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

Research

Jump to: Editorial

22 pages, 9504 KiB

Open AccessArticle

A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing

by Jiancai Leng, Xinyi Chen, Jinzhao Zhao, Chongfeng Wang, Jianqun Zhu, Yihao Yan, Jiaqi Zhao, Weiyou Shi, Zhaoxin Zhu, Xiuquan Jiang, Yitai Lou, Chao Feng, Qingbo Yang and Fangzhou Xu

Sensors 2023, 23(21), 8913; https://0-doi-org.brum.beds.ac.uk/10.3390/s23218913 - 02 Nov 2023

Cited by 1 | Viewed by 1185

Abstract

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network’s total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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20 pages, 6643 KiB

Open AccessArticle

Simplification of Deep Neural Network-Based Object Detector for Real-Time Edge Computing

by Kyoungtaek Choi, Seong Min Wi, Ho Gi Jung and Jae Kyu Suhr

Sensors 2023, 23(7), 3777; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073777 - 06 Apr 2023

Cited by 7 | Viewed by 1944

Abstract

This paper presents a method for simplifying and quantizing a deep neural network (DNN)-based object detector to embed it into a real-time edge device. For network simplification, this paper compares five methods for applying channel pruning to a residual block because special care must be taken regarding the number of channels when summing two feature maps. Based on the comparison in terms of detection performance, parameter number, computational complexity, and processing time, this paper discovers the most satisfying method on the edge device. For network quantization, this paper compares post-training quantization (PTQ) and quantization-aware training (QAT) using two datasets with different detection difficulties. This comparison shows that both approaches are recommended in the case of the easy-to-detect dataset, but QAT is preferable in the case of the difficult-to-detect dataset. Through experiments, this paper shows that the proposed method can effectively embed the DNN-based object detector into an edge device equipped with Qualcomm’s QCS605 System-on-Chip (SoC), while achieving a real-time operation with more than 10 frames per second. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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11 pages, 380 KiB

Open AccessArticle

Sharding-Based Proof-of-Stake Blockchain Protocols: Key Components & Probabilistic Security Analysis

by Abdelatif Hafid, Abdelhakim Senhaji Hafid and Dimitrios Makrakis

Sensors 2023, 23(5), 2819; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052819 - 04 Mar 2023

Cited by 3 | Viewed by 1741

Abstract

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from a limited ability to scale (i.e., low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding has proved to be one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e., good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. We then briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. We achieve a number of years to fail of approximately 4000 in a network of 4000 nodes, 10 shards, and a shard resiliency of 33%. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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14 pages, 807 KiB

Open AccessArticle

Robust Multi-Sensor Consensus Plant Disease Detection Using the Choquet Integral

by Cedric Marco-Detchart, Carlos Carrascosa, Vicente Julian and Jaime Rincon

Sensors 2023, 23(5), 2382; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052382 - 21 Feb 2023

Cited by 3 | Viewed by 1606

Abstract

Over the last few years, several studies have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food industry. One of the application areas has been the automatic detection of plant diseases. These techniques, mainly based on deep learning models, allow for analysing and classifying plants to determine possible diseases facilitating early detection and thus preventing the propagation of the disease. In this way, this paper proposes an Edge-AI device that incorporates the necessary hardware and software components for automatically detecting plant diseases from a set of images of a plant leaf. In this way, the main goal of this work is to design an autonomous device that allows the detection of possible diseases that can detect potential diseases in plants. This will be achieved by capturing multiple images of the leaves and implementing data fusion techniques to enhance the classification process and improve its robustness. Several tests have been carried out to determine that the use of this device significantly increases the robustness of the classification responses to possible plant diseases. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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14 pages, 2019 KiB

Open AccessArticle

by Mingfeng Chen, Qiyong Chen, Zhaoyu Su, Shaohua Sun and Chunhai Li

Sensors 2023, 23(4), 2259; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042259 - 17 Feb 2023

Viewed by 1056

Abstract

In a harsh environment, function aggregation of air–ground integrated network service function chaining (SFC) deployment can easily cause network load imbalance, which affects the network security and reliability. In this study, a task-similarity-based virtual network function (VNF) aggregation scheme was proposed. It considered air–ground network resource consumption and load balance before SFC mapping. A model for selecting VNFs to be aggregated based on task similarity was built. The tasks were classified based on their similarity. Furthermore, the VNFs to be aggregated were selected within the class under the constraints of the underlying physical resources. Load balancing was achieved by adjusting the similarity threshold. Moreover, an SFC mapping selection scheme based on network resource awareness was used to obtain the most suitable physical nodes for single-chain and multi-chain mapping according to various attributes of physical network nodes. The simulation results indicated that the proposed scheme with a better load balance design outperformed existing works on VNF aggregation. We also demonstrated that the task-similarity-based scheme was resource-consumption efficient and effective. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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12 pages, 308 KiB

Open AccessArticle

Strategic Bidding of Retailers in Wholesale Markets: Continuous Intraday Markets and Hybrid Forecast Methods

by Hugo Algarvio and Fernando Lopes

Sensors 2023, 23(3), 1681; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031681 - 03 Feb 2023

Viewed by 1258

Abstract

The deregulation process of the electricity sector has led to competition in wholesale and retail markets. In particular, retailers submit bids to wholesale markets to satisfy the energy needs associated with portfolios of end-use customers. This paper describes a strategic process for retailers bidding in a wholesale market composed of a day-ahead market, an intraday market, and a balancing market. It considers a market design that involves a hybrid model for the intraday market, based on daily auctions and a continuous procedure. The paper also presents a computational study to illustrate and test both the market design and the strategic bidding process of retailers. The results confirm the advantages of considering a continuous intraday market, show that bidding in short-term markets is more beneficial than bidding in medium-term markets, and indicate important aspects to consider when selecting customers to add to the portfolios of retailers. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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20 pages, 995 KiB

Open AccessArticle

Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing

by Ralf Bruns, Jeremias Dötterl, Jürgen Dunkel and Sascha Ossowski

Sensors 2023, 23(2), 614; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020614 - 05 Jan 2023

Cited by 1 | Viewed by 957

Abstract

Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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12 pages, 3529 KiB

Open AccessCommunication

Image Generation from Text Using StackGAN with Improved Conditional Consistency Regularization

by Rihito Tominaga and Masataka Seo

Sensors 2023, 23(1), 249; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010249 - 26 Dec 2022

Cited by 1 | Viewed by 3875

Abstract

Image generation from natural language has become a very promising area of research on multimodal learning in recent years. In recent years, the performance of this theme has improved rapidly, and the release of powerful tools has caused a great response in various places. The Stacked Generative Adversarial Networks (StackGAN) model is a representative method to generate images from text descriptions. Although it can generate high-resolution images, it involves several limitations; some of the images generated are typically unintelligible, and mode collapse may occur. Therefore, in this study, we aim to solve these two problems to generate images that follow a given text description more closely. First, we incorporate a new consistency regularization technique for conditional generation tasks into StackGAN, called Improved Consistency Regularization or ICR. The ICR technique learns the meaning of data by matching the semantic information of input data before and after data augmentation, and can also stabilize learning in adversarial networks. In this research, this method mainly suppresses mode collapse by expanding the variation of generated images. However, this method may lead to excessive variations in the generated images, which may result in images that do not match the meaning of the input text or that are ambiguous. Therefore, we further propose a new regularization method called ICCR as a modification of ICR, which is designed to perform conditional generation tasks and eliminate the negative impacts of the generator. This method realized the generation of various images along the input text. The proposed StackGAN with ICCR performed 16% better than StackGAN and 4% better than StackGAN with ICR and AttnGAN on the Inception Score using the CUB dataset. AttnGAN, similar to StackGAN, is a GAN-based text-to-image model that incorporates the attention mechanism, which has achieved great results in recent years. It is very important that our proposed model, which incorporates ICCR into a simple model, obtained better results than AttnGAN. In addition, StackGAN with ICCR was effective in eliminating mode collapse. The probability of mode collapse in the original StackGAN was 20%, while in StackGAN with ICCR the probability was 0%. In the questionnaire survey, our proposed method was rated 18% higher than StackGAN with ICR. This indicates that ICCR is more effective for conditional tasks than ICR. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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25 pages, 7321 KiB

Open AccessArticle

Holistic Security and Safety for Factories of the Future

by Eva Maia, Sinan Wannous, Tiago Dias, Isabel Praça and Ana Faria

Sensors 2022, 22(24), 9915; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249915 - 16 Dec 2022

Cited by 3 | Viewed by 2109

Abstract

The accelerating transition of traditional industrial processes towards fully automated and intelligent manufacturing is being witnessed in almost all segments. This major adoption of enhanced technology and digitization processes has been originally embraced by the Factories of the Future and Industry 4.0 initiatives. The overall aim is to create smarter, more sustainable, and more resilient future-oriented factories. Unsurprisingly, introducing new production paradigms based on technologies such as machine learning (ML), the Internet of Things (IoT), and robotics does not come at no cost as each newly incorporated technique poses various safety and security challenges. Similarly, the integration required between these techniques to establish a unified and fully interconnected environment contributes to additional threats and risks in the Factories of the Future. Accumulating and analyzing seemingly unrelated activities, occurring simultaneously in different parts of the factory, is essential to establish cyber situational awareness of the investigated environment. Our work contributes to these efforts, in essence by envisioning and implementing the SMS-DT, an integrated platform to simulate and monitor industrial conditions in a digital twin-based architecture. SMS-DT is represented in a three-tier architecture comprising the involved data and control flows: edge, platform, and enterprise tiers. The goal of our platform is to capture, analyze, and correlate a wide range of events being tracked by sensors and systems in various domains of the factory. For this aim, multiple components have been developed on the basis of artificial intelligence to simulate dominant aspects in industries, including network analysis, energy optimization, and worker behavior. A data lake was also used to store collected information, and a set of intelligent services was delivered on the basis of innovative analysis and learning approaches. Finally, the platform was tested in a textile industry environment and integrated with its ERP system. Two misuse cases were simulated to track the factory machines, systems, and people and to assess the role of SMS-DT correlation mechanisms in preventing intentional and unintentional actions. The results of these misuse case simulations showed how the SMS-DT platform can intervene in two domains in the first scenario and three in the second one, resulting in correlating the alerts and reporting them to security operators in the multi-domain intelligent correlation dashboard. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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21 pages, 1185 KiB

Open AccessArticle

QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing

by Chenhong Cao, Meijia Su, Shengyu Duan, Miaoling Dai, Jiangtao Li and Yufeng Li

Sensors 2022, 22(23), 9340; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239340 - 30 Nov 2022

Cited by 3 | Viewed by 1695

Abstract

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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24 pages, 4683 KiB

Open AccessArticle

MEC/Cloud Orchestrator to Facilitate Private/Local Beyond 5G with MEC and Proof-of-Concept Implementation

by Jin Nakazato, Zongdian Li, Kazuki Maruta, Keiichi Kubota, Tao Yu, Gia Khanh Tran, Kei Sakaguchi and Soh Masuko

Sensors 2022, 22(14), 5145; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145145 - 08 Jul 2022

Cited by 6 | Viewed by 2881

Abstract

The emergence of 5G-IoT opens up unprecedented connectivity possibilities for new service use cases and players. Multi-access edge computing (MEC) is a crucial technology and enabler for Beyond 5G, supporting next-generation communications with service guarantees (e.g., ultra-low latency, high security) from an end-to-end (E2E) perspective. On the other hand, one notable advance is the platform that supports virtualization from RAN to applications. Deploying Radio Access Networks (RAN) and MEC, including third-party applications on virtualization platforms, and renting other equipment from legacy telecom operators will make it easier for new telecom operators, called Private/Local Telecom Operators, to join the ecosystem. Our preliminary studies have discussed the ecosystem for private and local telecom operators regarding business potential and revenue and provided numerical results. What remains is how Private/Local Telecom Operators can manage and deploy their MEC applications. In this paper, we designed the architecture for fully virtualized MEC 5G cellular networks with local use cases (e.g., stadiums, campuses). We propose an MEC/Cloud Orchestrator implementation for intelligent deployment selection. In addition, we provide implementation schemes in several cases held by either existing cloud owners or private and local operators. In order to verify the proposal’s feasibility, we designed the system level in E2E and constructed a Beyond 5G testbed at the Ōokayama Campus of the Tokyo Institute of Technology. Through proof-of-concept in the outdoor field, the proposed system’s feasibility is verified by E2E performance evaluation. The verification results prove that the proposed approach can reduce latency and provide a more stable throughput than conventional cloud services. Full article

(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)

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