Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.
The primary drivers of agricultural land depletion in Taiz be diagnosed quantitatively in this study, proposing for the first time a replicable conflict-sensitive urban management model. The overarching objective is to bridge the critical gap between sustainable urban expansion and the preservation of agro-ecological systems in fragile, data-scarce contexts. A combination of unplanned sprawl, crisis, and ineffective governance, Taiz City's rapid urbanization between 2000 and 2024 resulted in a 35% loss of agricultural land. This study proposes that governance reduces the primary causes of conflict escalation and the severity of sprawl. This study combines GIS spatial analysis (Landsat 8/9 and support vector machine classification), regression modeling, and global case comparisons (Medellín and Mumbai) to assess land-use trends. The findings indicate that governance diminishes the effects (β = −0.50, p < 0.01), sprawl (β = 0.85, p < 0.01), and conflict (β = 0.002, p < 0.05) explain 85% of the variance in losses. By 2024, 3.2 million residents' food security was at risk because of the urbanization of 60% of peri-urban fertile lands. Vertical expansion, tenure regularization and GIS planning will reclaim 20% of land by 2030.
High accuracy and simultaneous positioning is an essential demand in future Intelligent Transportation Systems (ITS), while the mobility and dynamics of vehicles place great challenges. Single Base Station (BS) positioning has become popular for its fast speed, high convenience, and low cost. With the construction of 5G, the wide bandwidth and high separation capability of millimeter Wave (mmWave) bring more possibilities for vehicle positioning via single BS. However, mmWave signals have high distance attenuation and are easily blocked by obstacles. In urban scenarios, the prevalent None-Line-of-Sight (NLoS) situations have severe impacts on positioning accuracy. The multipath effects, Doppler effects, and tracking lags further degrade the performance. To address these issues, we introduce the Intelligent Reflecting Surface (IRS) to single BS vehicle positioning for beyond Line-of-Sight (LoS) communications. We study the advantages of IRS in urban ITS to alleviate the multipath effects, Doppler effects, and tracking delay. To realize the real-time target tracking for IRS, the Unscented Kalman Filter (UKF) is adopted, for which stable communications between the BS and moving vehicle can be maintained. Simulation results show that the utilization of IRS can significantly improve the positioning accuracy and the adoption of UKF further enhances the performance.
Intelligent Cyber-Physical Transportation Systems (ICTS) rely deeply on data exchange between moving vehicles and fixed infrastructure. While exchanging data between vehicles in the network, security is a main constraint. ICTS encompasses many facilities and claims, such as managing road traffic, traveler data, public transportation systems, and autonomous vehicles. It is anticipated that ICTS will make significant contributions to future smart cities and urban design in road and traffic security, transportation and transport optimization, energy efficiency, and environmental pollution control. Flexibility, variety of quality-of-service needs, and massive volumes of data generated all present challenges when working with ITS. Previous works on cyber-physical attack detection lag in ensuring security at various levels. To monitor In-Vehicle Networks (IVN), Vehicle-to-Vehicle (V2V) Communications, and Vehicle-to-Infrastructure (V2I) Networks for malicious behavior, we introduce a deep learning-based Intrusion Detection System (IDS) for ICTS. In this research, we look into how Deep Learning (DL), a relatively new but rapidly growing subject, could help improve ICTS. This research investigates whether deep learning can enhance the security of autonomous vehicles and other forms of smart transportation. We improve the smart transport network using the deep learning method, simulate it, and statistically study its data transmission economy, accuracy projections of the future, and path-switching strategy. To identify malicious activity in the core networks of autonomous vehicles (AV), we have developed an ensemble Long-Short Term Memory (LSTM) technique based on Deep Learning architecture. The car hacking dataset (used to monitor communications within a vehicle) and the UNSWNB15 dataset (used to monitor communications with the outside world) are used to test and compare the performance of the proposed IDS. The experimental findings showed that our suggested system outperformed other intrusion detection methods by achieving 100% accuracy in detecting all sorts of assaults on the auto-hacking dataset and 99% on the UNSW-NB15 dataset.
Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger public safety and cause social losses. Therefore, this paper proposes a watermarking that can verify the integrity of copyright in response to the needs of ITS, termed ITSmark. ITSmark focuses on functions such as extracting watermarks, verifying permission, and tracing tampered locations. The scheme uses the copyright information to build the multi-bit space and divides this space into multiple segments. These segments will be assigned to tokens. Thus, the next token is determined by its segment which contains the copyright. In this way, the obtained data contains the custom watermark. To ensure the authorization, key parameters are encrypted during copyright embedding to obtain cipher data. Only by possessing the correct cipher data and private key, can the user entirely extract the watermark. Experiments show that ITSmark surpasses baseline performances in data quality, extraction accuracy, and unforgeability. It also shows unique capabilities of permission verification and tampered location tracing, which ensures the security of extraction and the reliability of copyright verification. Furthermore, ITSmark can also customize the watermark embedding position and proportion according to user needs, making embedding more flexible.
Jianghan Ji, Cheng-Xiang Wang, Shuaifei Chen
et al.
This letter investigates channel estimation for ultra-massive multiple-input multiple-output (MIMO) communications. We propose a joint low-rank and sparse Bayesian estimation (LRSBE) algorithm for spatial non-stationary ultra-massive channels by exploiting the low-rankness and sparsity in the beam domain. Specifically, the channel estimation integrates sparse Bayesian learning and soft-threshold gradient descent within the expectation-maximization framework. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art alternatives under different signal-to-noise ratio conditions in terms of estimation accuracy and overall complexity.
Ostonya Thomas, M Sabbir Salek, Jean-Michel Tine
et al.
The transportation industry is experiencing vast digitalization as a plethora of technologies are being implemented to improve efficiency, functionality, and safety. Although technological advancements bring many benefits to transportation, integrating cyberspace across transportation sectors has introduced new and deliberate cyber threats. In the past, public agencies assumed digital infrastructure was secured since its vulnerabilities were unknown to adversaries. However, with the expansion of cyberspace, this assumption has become invalid. With the rapid advancement of wireless technologies, transportation systems are increasingly interconnected with both transportation and non-transportation networks in an internet-of-things ecosystem, expanding cyberspace in transportation and increasing threats and vulnerabilities. This study investigates some prominent reasons for the increase in cyber vulnerabilities in transportation. In addition, this study presents various collaborative strategies among stakeholders that could help improve cybersecurity in the transportation industry. These strategies address programmatic and policy aspects and suggest avenues for technological research and development. The latter highlights opportunities for future research to enhance the cybersecurity of transportation systems and infrastructure by leveraging hybrid approaches and emerging technologies.
O agravamento das desigualdades socioespaciais, de gênero e raciais tem exigido uma revisão teórica e prática profunda no campo do planejamento da mobilidade. O debate crítico revela a marginalização de grupos sociais, territórios e práticas espaciais cotidianas, enraizada em valores patriarcais, racistas e eurocêntricos. Na América Latina, a mobilidade do cuidado é realizada predominantemente por mulheres periferizadas cuja principal forma de deslocamento é a mobilidade a pé, combinada com o transporte público. No entanto, a falta de compreensão sobre como a experiência diferenciada de deslocamento cotidiano desse grupo específico, aliada a pressupostos positivistas e tecnocráticos, desvaloriza formas de ser e existir no espaço que não são hegemônicas. Neste artigo, apresento as características das cuidadoras e pedestres nas periferias do Brasil, destacando as materialidades da caminhabilidade envolvidas na mobilidade do cuidado. Proponho uma discussão sobre a invisibilidade dos deslocamentos relacionados à mobilidade do cuidado nas pesquisas origem-destino e busco explorar outros instrumentos e métodos de planejamento que possam contribuir para um avanço ético, político e teórico-metodológico, ao adotar perspectivas centradas na mobilidade cotidiana a pé, rumo a um urbanismo feminista, situado e transformador, que promova cidades cuidadoras e comprometidas com a garantia da vida.
Дмитро АНРДЄЄВ, Олексій ЛИГУН, Андрій ДРОЗД
et al.
Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide.
The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries.
The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations.
Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets.
The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.
Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing, achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as reinforcement learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we introduce the applications of QC in communications and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally, we discuss remaining open issues before concluding.
Pengfei Wang, Zhaohong Yan, Mohammad S. Obaidat
et al.
Vehicular-to-everything (V2X) communications have gained popularity as a cutting-edge technology in Internet of Vehicles (loV), ensuring low-latency communication for emerging transportation features. Federated learning (FL), a widely-used distributed collaborative AI approach, is transforming edge caching in V2X communications due to its exceptional privacy protection. However, current FL-based edge caching methods can negatively impact communication performance when non-independent and identically distributed (non-IID) data or invalid data, such as poisoned data, are introduced during the training process. In this article, we present FedFilter, an FL-based edge caching solution designed to address these challenges. FedFilter employs a personalized FL method based on model decomposition and hierarchical aggregation, caching content tailored to the diverse preferences of individual users. This enhances the cache hit rate, reducing backhaul load and service latency. Moreover, FedFilter detects and mitigates the adverse effects of invalid data on the global model, ensuring the Quality of Service (QoS) of V2X communications. A case study is introduced to demonstrate the effectiveness of FedFilter, showing that it not only reduces latency but also effectively removes invalid data while maintaining a high cache hit rate.
Jyotsna Ghildiyal Bijalwan, Jagendra Singh, Vinayakumar Ravi
et al.
This study explores the limitations of conventional encryption in real-world communications due to resource constraints. Additionally, it delves into the integration of Deep Reinforcement Learning (DRL) in autonomous cars for trajectory management within Connected And Autonomous Vehicles (CAVs). This study unveils the resource-constrained real-world communications, conventional encryption faces challenges that hinder its feasibility. This introduction sets the stage for exploring the integration of DRL in autonomous cars and the transformative potential of Blockchain technology in ensuring secure data transfer, especially within the dynamic landscape of the transportation industry. The research methodology involves implementing DRL techniques for autonomous car trajectory management within the context of connected and autonomous CAVs. Additionally, a detailed exploration of Blockchain technology deployment, consensus procedures, and decentralized data storage mechanisms. Results showcase the impracticality of conventional encryption in resource-constrained real-world communications. Moreover, the implementation of DRL and Blockchain technology proves effective in optimizing autonomous car subsystems, reducing training costs, and establishing secure, globally accessible government-managed transportation for enhanced data integrity and accessibility. The discussion delves into the implications of the study's findings, emphasizing the transformative potential of DRL in optimizing autonomous car subsystems. Furthermore, it explores the broader implications of Blockchain technology in revolutionizing secure, decentralized data transfer within the transportation industry. In conclusion, the study highlights the impracticality of conventional encryption in real-world communications and underscores the significant advancements facilitated by DRL in autonomous vehicle trajectory management. The integration of Blockchain technology not only ensures secure data transfer but also paves the way for a globally accessible transportation blockchain, reshaping the future landscape of the industry.
Climate change is a challenge for global sustainable development. The transportation sector contributes considerably to global greenhouse gas emissions. In China, it accounts for about 10% of total CO_2 emissions with high mitigation potential. Public transit systems can save energy and reduce carbon emissions. Carbon-inclusive action based on digital technology is becoming the primary measure for promoting green transit in China. To understand the level and nature of the public’s awareness of green transit and identify potential pathways to change their behaviors toward green transit, this study collected 7369 questionnaires nationwide and examined the public’s behavior and preferences regarding green transportation and their attitudes toward digital technologies that support green transportation, in the first such study in the Chinese context. The study found that (1) most respondents understand and accept the concept of green transportation, especially younger respondents, under the age of 39; (2) convenience, safety, and weather are the most important factors considered; (3) digital tools provide innovative and interesting options for public participation in green mobility. The government, social organizations, enterprises, and various stakeholders must strive to foster consensus and collaborative participation. This involves partnering with digital technology enterprises and establishing emission reduction standards for low-carbon living, particularly in the realm of green transportation.
Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide coverage with enhanced gains, and thus are strong candidates for realizing ubiquitous ICAL. In this paper, we develop a wideband massive MIMO LEO satellite system to simultaneously support wireless communications and localization operations in the downlink. In particular, we first characterize the signal propagation properties and derive a localization performance bound. Based on these analyses, we focus on the hybrid analog/digital precoding design to achieve high communication capability and localization precision. Numerical results demonstrate that the proposed ICAL scheme supports both the wireless communication and localization operations for typical system setups.
Suchinthaka Wanninayaka, Achintha Wijesinghe, Weiwei Wang
et al.
The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GO-COM, this work introduces a novel noise-restricted diffusion-based GO-COM (Diff-GO$^\text{n}$) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high-quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO$^\text{n}$ well-suited for real-time communications and downstream applications.