Pub Date : 2024-07-24DOI: 10.3389/frcmn.2024.1439529
Georgios Xylouris, Nikolaos Nomikos, Alexandros Kalafatelis, A. Giannopoulos, S. Spantideas, Panagiotis Trakadas
The maritime domain is a major driver of economic growth with emerging services, comprising intelligent transportation systems (ITSs), smart ports, security and safety, and ocean monitoring systems. Sixth generation (6G) mobile networks will offer various technologies, paving the way for reliable and autonomous maritime communication networks (MCNs), supporting these novel maritime services. This review presents the main enabling technologies for future MCNs and relevant use cases, including ITSs with reduced carbon footprint, ports and maritime infrastructure security, as well as fault detection and predictive maintenance. Moreover, the current trends in integrated satellite-aerial-terrestrial-maritime network architectures are discussed together with the different network segments and communication technologies, and machine learning integration aspects.
{"title":"Sailing into the future: technologies, challenges, and opportunities for maritime communication networks in the 6G era","authors":"Georgios Xylouris, Nikolaos Nomikos, Alexandros Kalafatelis, A. Giannopoulos, S. Spantideas, Panagiotis Trakadas","doi":"10.3389/frcmn.2024.1439529","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1439529","url":null,"abstract":"The maritime domain is a major driver of economic growth with emerging services, comprising intelligent transportation systems (ITSs), smart ports, security and safety, and ocean monitoring systems. Sixth generation (6G) mobile networks will offer various technologies, paving the way for reliable and autonomous maritime communication networks (MCNs), supporting these novel maritime services. This review presents the main enabling technologies for future MCNs and relevant use cases, including ITSs with reduced carbon footprint, ports and maritime infrastructure security, as well as fault detection and predictive maintenance. Moreover, the current trends in integrated satellite-aerial-terrestrial-maritime network architectures are discussed together with the different network segments and communication technologies, and machine learning integration aspects.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"91 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.3389/frcmn.2024.1376191
José Jerovane Da Costa Nascimento, A. G. Marques, Yasmim Osório Adelino Rodrigues, Guilherme Freire Brilhante Severiano, Icaro de Sousa Rodrigues, Carlos Dourado, Luís Fabrício De Freitas Souza
According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence–based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning–based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes.
{"title":"Health of Things Melanoma Detection System—detection and segmentation of melanoma in dermoscopic images applied to edge computing using deep learning and fine-tuning models","authors":"José Jerovane Da Costa Nascimento, A. G. Marques, Yasmim Osório Adelino Rodrigues, Guilherme Freire Brilhante Severiano, Icaro de Sousa Rodrigues, Carlos Dourado, Luís Fabrício De Freitas Souza","doi":"10.3389/frcmn.2024.1376191","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1376191","url":null,"abstract":"According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence–based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning–based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":" 71","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141365272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAV-assisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms.
{"title":"Efficient multiple unmanned aerial vehicle-assisted data collection strategy in power infrastructure construction","authors":"Qijie Lai, Rongchang Xie, Zhifei Yang, Guibin Wu, Zechao Hong, Chao Yang","doi":"10.3389/frcmn.2024.1390909","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1390909","url":null,"abstract":"Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAV-assisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"123 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.3389/frcmn.2024.1332379
Ligia F. Borges, Michael T. Barros, Michele Nogueira
Molecular communication (MC) allows implantable devices to communicate using biological data-transmission principles (e.g., molecules as information carriers). However, MC faces significant challenges due to molecular noise, which leads to increased communication errors. Thus, error control techniques become critical for reliable intra-body networks. The noise management and error control in these networks must be based on the characterization of the environment dynamics, i.e., characteristics that increase noise, such as the stochastic behavior of the intercellular channels and the presence of pathologies that affect communication. This work proposes an adaptive error control technique for cell signaling–based MC channels (CELLECs). Using an information-theoretic approach, CELLEC mitigates errors in cellular channels with varying noise conditions. The characteristics of the cellular environment and different noise sources are modeled to evaluate the proposal. The additive white Gaussian tissue noise (AWGTN) produced by stochastic chemical reactions is theorized for healthy cells. The MC model also considers the noise of cells affected by one pathology that disrupts cells’ molecular equilibrium and causes them to become reactive (i.e., Alzheimer’s disease). Analyses show that reactive cells have a higher signal-to-noise ratio (21.4%) and path loss (33.05%) than healthy cells, highlighting the need for an adaptive technique to deal with cellular environment variability. Results show that CELLEC improves communication channel performance by lowering the bit error rate (18%).
分子通信（MC）允许植入式设备利用生物数据传输原理（如分子作为信息载体）进行通信。然而，由于分子噪声导致通信误差增加，分子通信面临着巨大挑战。因此，错误控制技术对可靠的体内网络至关重要。这些网络中的噪声管理和误差控制必须基于环境动态特性，即增加噪声的特征，如细胞间通道的随机行为和影响通信的病理存在。这项研究为基于细胞信号的 MC 信道（CELLECs）提出了一种自适应误差控制技术。利用信息论方法，CELLEC 可在噪声条件不断变化的蜂窝信道中减少误差。为评估该建议，对蜂窝环境和不同噪声源的特性进行了建模。随机化学反应产生的加性白高斯组织噪声（AWGTN）是健康细胞的理论基础。MC 模型还考虑了受一种病理学影响的细胞噪声，这种病理学破坏了细胞的分子平衡，导致细胞变得反应性（即阿尔茨海默病）。分析表明，与健康细胞相比，反应性细胞具有更高的信噪比（21.4%）和路径损耗（33.05%），这突出表明需要一种自适应技术来应对细胞环境的变化。结果表明，CELLEC降低了误码率（18%），从而改善了通信信道性能。
{"title":"Cell signaling error control for reliable molecular communications","authors":"Ligia F. Borges, Michael T. Barros, Michele Nogueira","doi":"10.3389/frcmn.2024.1332379","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1332379","url":null,"abstract":"Molecular communication (MC) allows implantable devices to communicate using biological data-transmission principles (e.g., molecules as information carriers). However, MC faces significant challenges due to molecular noise, which leads to increased communication errors. Thus, error control techniques become critical for reliable intra-body networks. The noise management and error control in these networks must be based on the characterization of the environment dynamics, i.e., characteristics that increase noise, such as the stochastic behavior of the intercellular channels and the presence of pathologies that affect communication. This work proposes an adaptive error control technique for cell signaling–based MC channels (CELLECs). Using an information-theoretic approach, CELLEC mitigates errors in cellular channels with varying noise conditions. The characteristics of the cellular environment and different noise sources are modeled to evaluate the proposal. The additive white Gaussian tissue noise (AWGTN) produced by stochastic chemical reactions is theorized for healthy cells. The MC model also considers the noise of cells affected by one pathology that disrupts cells’ molecular equilibrium and causes them to become reactive (i.e., Alzheimer’s disease). Analyses show that reactive cells have a higher signal-to-noise ratio (21.4%) and path loss (33.05%) than healthy cells, highlighting the need for an adaptive technique to deal with cellular environment variability. Results show that CELLEC improves communication channel performance by lowering the bit error rate (18%).","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"44 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141010683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.3389/frcmn.2024.1370496
Mohammed Abdrabou, T. A. Gulliver
Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an information-theoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a single-class classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication.
{"title":"Secure authentication in MIMO systems: exploring physical limits","authors":"Mohammed Abdrabou, T. A. Gulliver","doi":"10.3389/frcmn.2024.1370496","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1370496","url":null,"abstract":"Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an information-theoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a single-class classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"97 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.3389/frcmn.2024.1337697
Harris K. Armeniakos, K. Maliatsos, P. Bithas, A. Kanatas
The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.
在超越第五代（5G）通信的背景下，利用无人驾驶飞行器（UAV）提高网络性能已显示出与地面通信相比的各种优势。此外，无人机在很大程度上被认为在向物联网（IoT）设备传播数据方面发挥着核心作用。本文提出了一个新颖的随机几何统一框架，用于研究无人机辅助物联网网络的下行链路性能，该网络同时集成了无人机基站（UAV-BS）和地面物联网接收设备。该框架基于空中无人机走廊的概念，将其建模为物联网网络上方的一条有限线，并采用一维（1D）二叉点过程（BPP）对空中走廊中无人机基站的空间位置进行建模。随后，在第 n 次最近选择方案、随机选择方案和联合传输协调多点（JT-CoMP）方案这三种关联策略下，对覆盖概率、平均速率和能效进行了基于信噪比的综合性能分析。数值结果揭示了有价值的系统级见解和权衡，为无人机辅助物联网网络的设计奠定了坚实的基础。
{"title":"A stochastic geometry-based performance analysis of a UAV corridor-assisted IoT network","authors":"Harris K. Armeniakos, K. Maliatsos, P. Bithas, A. Kanatas","doi":"10.3389/frcmn.2024.1337697","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1337697","url":null,"abstract":"The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"9 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140430446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.3389/frcmn.2024.1286660
K. C. Apostolakis, Barbara Valera-Muros, Nicola di Pietro, Pablo Garrido, Daniel del Teso, Manos N. Kamarianakis, Pedro R. Tomas, Hamzeh Khalili, Laura Panizo, Almudena Díaz Zayas, A. Protopsaltis, G. Margetis, J. Mangues-Bafalluy, M. Requena-Esteso, Andre S. Gomes, Luís Cordeiro, G. Papagiannakis, C. Stephanidis
Low latency and high bandwidth heralded with 5G networks will allow transmission of large amounts of Mission-Critical data over a short time period. 5G hence unlocks several capabilities for novel Public Protection and Disaster Relief (PPDR) applications, developed to support first responders in making faster and more accurate decisions during times of crisis. As various research initiatives are giving shape to the Network Application ecosystem as an interaction layer between vertical applications and the network control plane, in this article we explore how this concept can unlock finer network service management capabilities that can be leveraged by PPDR solution developers. In particular, we elaborate on the role of Network Applications as means for developers to assure prioritization of specific emergency flows of data, such as high-definition video transmission from PPDR field users to remote operators. To demonstrate this potential in future PPDR-over-5G services, we delve into the transfer of network-intensive PPDR solutions to the Network Application model. We then explore novelties in Network Application experimentation platforms, aiming to streamline development and deployment of such integrated systems across existing 5G infrastructures, by providing the reliability and multi-cluster environments they require.
{"title":"A network application approach towards 5G and beyond critical communications use cases","authors":"K. C. Apostolakis, Barbara Valera-Muros, Nicola di Pietro, Pablo Garrido, Daniel del Teso, Manos N. Kamarianakis, Pedro R. Tomas, Hamzeh Khalili, Laura Panizo, Almudena Díaz Zayas, A. Protopsaltis, G. Margetis, J. Mangues-Bafalluy, M. Requena-Esteso, Andre S. Gomes, Luís Cordeiro, G. Papagiannakis, C. Stephanidis","doi":"10.3389/frcmn.2024.1286660","DOIUrl":"https://doi.org/10.3389/frcmn.2024.1286660","url":null,"abstract":"Low latency and high bandwidth heralded with 5G networks will allow transmission of large amounts of Mission-Critical data over a short time period. 5G hence unlocks several capabilities for novel Public Protection and Disaster Relief (PPDR) applications, developed to support first responders in making faster and more accurate decisions during times of crisis. As various research initiatives are giving shape to the Network Application ecosystem as an interaction layer between vertical applications and the network control plane, in this article we explore how this concept can unlock finer network service management capabilities that can be leveraged by PPDR solution developers. In particular, we elaborate on the role of Network Applications as means for developers to assure prioritization of specific emergency flows of data, such as high-definition video transmission from PPDR field users to remote operators. To demonstrate this potential in future PPDR-over-5G services, we delve into the transfer of network-intensive PPDR solutions to the Network Application model. We then explore novelties in Network Application experimentation platforms, aiming to streamline development and deployment of such integrated systems across existing 5G infrastructures, by providing the reliability and multi-cluster environments they require.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"219 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140443762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.3389/frcmn.2023.1280602
Menelaos Zetas, S. Spantideas, A. Giannopoulos, Nikolaos Nomikos, Panagiotis Trakadas
Introduction: Shipping and maritime transportation have gradually gained a key role in worldwide economical strategies and modern business models. The realization of Smart Shipping (SMS) powered by advanced 6G communication networks, as well as innovative Machine Learning (ML) solutions, has recently become the focal point in the maritime sector. However, conventional centralized learning schemes are unsuitable in the maritime domain, due to considerable data communication overhead, stringent energy constraints, increased transmission failures in the harsh propagation environment, as well as data privacy concerns.Methods: To overcome these challenges, we propose the joint adoption of Federated Learning (FL) principles and the utilization of the Over-the-Air computation (AirComp) wireless transmission framework. Thus, this paper initially describes the mathematical considerations of a 6G maritime communication system, focusing on the heterogeneity of the relevant nodes and the channel models, including an Unmanned Aerial Vehicle (UAV)-aided relaying model that is usually required in maritime communications. The communication network, enhanced with the AirComp technique for efficiency purposes, forms the technical basis for the collaborative learning across multiple Internet of Maritime Things (IoMT) nodes in FL tasks. The workflow of the FL/AirComp scheme is illustrated and proposed as a communication-efficient and privacy-aware SMS framework, considering spectrum and energy efficiency aspects under a sum transmitting power constraint.Results: Then, the performance of the proposed methodology is assessed in an important ML task, related to intelligent maritime transportation systems, namely, the prediction of the Cargo Ship Propulsion Power using real data originating from six cargo ships and utilizing long-short-term-memory (LSTM) neural networks. Upon extensive experimentation, FL showed higher prediction accuracy relative to the typical Ensemble Learning technique by a factor of 3.04. The AirComp system performance was evaluated under varying noise conditions and number of IoMT nodes, using simulation data for the channel state information by regulating the power of the transmitting IoMT entities and the scaling factor at the shore base station.Discussion: The results clearly indicate the efficiency of the proposed FL/AirComp scheme in achieving low computation error, collaborative learning, spectrum efficiency and privacy protection in wireless maritime communications, while providing adequate accuracy levels with respect to the optimization objective.
{"title":"Empowering 6G maritime communications with distributed intelligence and over-the-air model sharing","authors":"Menelaos Zetas, S. Spantideas, A. Giannopoulos, Nikolaos Nomikos, Panagiotis Trakadas","doi":"10.3389/frcmn.2023.1280602","DOIUrl":"https://doi.org/10.3389/frcmn.2023.1280602","url":null,"abstract":"Introduction: Shipping and maritime transportation have gradually gained a key role in worldwide economical strategies and modern business models. The realization of Smart Shipping (SMS) powered by advanced 6G communication networks, as well as innovative Machine Learning (ML) solutions, has recently become the focal point in the maritime sector. However, conventional centralized learning schemes are unsuitable in the maritime domain, due to considerable data communication overhead, stringent energy constraints, increased transmission failures in the harsh propagation environment, as well as data privacy concerns.Methods: To overcome these challenges, we propose the joint adoption of Federated Learning (FL) principles and the utilization of the Over-the-Air computation (AirComp) wireless transmission framework. Thus, this paper initially describes the mathematical considerations of a 6G maritime communication system, focusing on the heterogeneity of the relevant nodes and the channel models, including an Unmanned Aerial Vehicle (UAV)-aided relaying model that is usually required in maritime communications. The communication network, enhanced with the AirComp technique for efficiency purposes, forms the technical basis for the collaborative learning across multiple Internet of Maritime Things (IoMT) nodes in FL tasks. The workflow of the FL/AirComp scheme is illustrated and proposed as a communication-efficient and privacy-aware SMS framework, considering spectrum and energy efficiency aspects under a sum transmitting power constraint.Results: Then, the performance of the proposed methodology is assessed in an important ML task, related to intelligent maritime transportation systems, namely, the prediction of the Cargo Ship Propulsion Power using real data originating from six cargo ships and utilizing long-short-term-memory (LSTM) neural networks. Upon extensive experimentation, FL showed higher prediction accuracy relative to the typical Ensemble Learning technique by a factor of 3.04. The AirComp system performance was evaluated under varying noise conditions and number of IoMT nodes, using simulation data for the channel state information by regulating the power of the transmitting IoMT entities and the scaling factor at the shore base station.Discussion: The results clearly indicate the efficiency of the proposed FL/AirComp scheme in achieving low computation error, collaborative learning, spectrum efficiency and privacy protection in wireless maritime communications, while providing adequate accuracy levels with respect to the optimization objective.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.3389/frcmn.2023.1096841
Michele Maasberg, Leslie G. Butler, Ian Taylor
Integration of the Internet of Things (IoT) in the automotive industry has brought benefits as well as security challenges. Significant benefits include enhanced passenger safety and more comprehensive vehicle performance diagnostics. However, current onboard and remote vehicle diagnostics do not include the ability to detect counterfeit parts. A method is needed to verify authentic parts along the automotive supply chain from manufacture through installation and to coordinate part authentication with a secure database. In this study, we develop an architecture for anti-counterfeiting in automotive supply chains. The core of the architecture consists of a cyber-physical trust anchor and authentication mechanisms connected to blockchain-based tracking processes with cloud storage. The key parameters for linking a cyber-physical trust anchor in embedded IoT include identifiers (i.e., serial numbers, special features, hashes), authentication algorithms, blockchain, and sensors. A use case was provided by a two-year long implementation of simple trust anchors and tracking for a coffee supply chain which suggests a low-cost part authentication strategy could be successfully applied to vehicles. The challenge is authenticating parts not normally connected to main vehicle communication networks. Therefore, we advance the coffee bean model with an acoustical sensor to differentiate between authentic and counterfeit tires onboard the vehicle. The workload of secure supply chain development can be shared with the development of the connected autonomous vehicle networks, as the fleet performance is degraded by vehicles with questionable replacement parts of uncertain reliability.
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Pub Date : 2023-11-23DOI: 10.3389/frcmn.2023.1220227
Oumayma Bouchmal, B. Cimoli, Ripalta Stabile, J. V. Vegas Olmos, Idelfonso Tafur Monroy
The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies.
第六代（6G）移动网络将采用按需自我重新配置的方式，以同时满足严格的关键性能指标和网络资源使用的整体优化。软件定义网络（SDN）具有网络全局视图、集中控制和可调整的转发规则，使这种动态灵活的网络管理成为可能。由于 6G 网络的复杂性，人工智能及其与 SDN 和量子计算的整合被认为是解决高动态和复杂网络中优化路由等难题的前瞻性方案。本调查报告的主要贡献在于深入研究和分析了近期有关应用强化学习（RL）、深度强化学习（DRL）和量子机器学习（QML）技术解决 6G 网络中 SDN 路由挑战的研究。此外，本文还确定并讨论了该领域的开放研究问题。总之，我们得出结论：在 SDN 网络中采用基于 RL/DRL 的路由策略是一个重大转变，尤其是在过去 3 年中。此外，人们对整合 QML 技术以解决 6G 网络中路由问题的复杂性兴趣浓厚。不过，这两种方法仍有大量工作要做，以便完成各种方法之间的全面比较和协同作用，并利用开放数据集和不同拓扑结构进行有意义的评估。
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