Frontiers in Communications and Networks最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Key parameters linking cyber-physical trust anchors with embedded internet of things systems","authors":"Michele Maasberg, Leslie G. Butler, Ian Taylor","doi":"10.3389/frcmn.2023.1096841","DOIUrl":"https://doi.org/10.3389/frcmn.2023.1096841","url":null,"abstract":"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.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139207449","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}

{"title":"From classical to quantum machine learning: survey on routing optimization in 6G software defined networking","authors":"Oumayma Bouchmal, B. Cimoli, Ripalta Stabile, J. V. Vegas Olmos, Idelfonso Tafur Monroy","doi":"10.3389/frcmn.2023.1220227","DOIUrl":"https://doi.org/10.3389/frcmn.2023.1220227","url":null,"abstract":"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.","PeriodicalId":106247,"journal":{"name":"Frontiers in Communications and Networks","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244958","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}