High altitude platforms (HAPs) can establish autonomous airborne networks, known as flying ad-hoc networks (FANETs), without relying on terrestrial infrastructure. A crucial aspect of these networks is maintaining robust links between HAPs, achievable through advanced multiple input multiple output (MIMO) techniques like beamforming. However, the unpredictable motion of HAPs necessitates frequent beam alignment. This paper proposes a novel two-step beam alignment method for inter-HAPs networks, leveraging the integrated sensing and communication (ISAC) concept. The first step involves a coarse beam alignment using sensing and triggering signals to extract channel parameters and activate a targeted HAP at low frequency bands. In the second step, a fine beam alignment is performed at high frequency bands to enhance the link connection within the narrowed area defined in the first step. Utilizing an antenna activation controller, the proposed method efficiently uses a single antenna array for dual-purpose signals across different frequency bands, reducing deployment costs and enhancing system efficiency. Mathematical analysis and numerical results demonstrate that this method significantly improves system performance by reducing alignment time overhead, while maintaining high communication capacity, beamforming gain, and detection rates for targeted HAPs.
Telecommunication, Transportation and communications
The catastrophic 2023 train collision in Tempi, Greece, with numerous casualties, has significantly impacted travelers’ interurban travel choices and undermined their trust in the railway system. In response, this paper examines travelers’ attitudes towards resuming railway travel and explores the factors influencing their decisions, by developing two discrete-time hazard-based models based on data from a revealed preference survey of Greek railway travelers. The models are aimed at capturing travelers’ recovery times: (i) until feeling comfortable resuming railway travel, and (ii) until actually resuming railway travel. Findings suggest that travelers are more likely to return to the Greek railway system more than six months after the accident, with socio-demographics, travel characteristics, and perceived safety before the accident playing crucial roles in recovery time. Interestingly, practical aspects, such as car availability and trip purpose, along with family status in terms of parenthood, are more influential in the actual decision to return to railway travel. On the other hand, travelers’ perceptions and beliefs primarily affect the time until they feel comfortable resuming railway trips. Insights from this paper can be leveraged to inform targeted strategies following railway accidents, aimed at both retaining travelers who are more willing to resume railway travel earlier and facilitating the return of those who are less likely to do so.
Transportation engineering, Transportation and communications
Abstract Maritime piracy has severely disrupted Nigeria’s maritime domain, undermining the numerous benefits promised by the African Continental Free Trade Agreement (AfCFTA) and posing a significant threat to its successful implementation. As this study highlights, maritime piracy in Nigeria emerges partly as a deviant response to the socio-economic pressures intensified by neoliberalism and globalization. By examining maritime piracy through the lens of Global Anomie Theory (GAT) and civic governance frameworks – supplemented by insights from interviewees – this research offers a deeper understanding of how such criminal activities could jeopardize Nigeria’s ability to fully realize the objectives of the AfCFTA. In doing so, it contributes to the growing body of literature on the intersection of free trade agreements and maritime security challenges. Given the AfCFTA protocols that emphasize Member States’ responsibilities to safeguard their security interests, this study argues that Nigeria must extend its anti-piracy strategies beyond domestic initiatives. Specifically, it recommends that Nigeria strengthen international cooperation mechanisms as a critical step toward ensuring the effective implementation of the AfCFTA agreement.
Shipment of goods. Delivery of goods, Transportation and communications
Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Abdellah Chehri
et al.
Uncrewed aerial systems (UASs) were popularly used by hobbyists in the past, but they have now become critical enablers for managing disasters, handling emergencies, and so on. For example, one of their most critical applications is to provide seamless wireless communication services in remote rural areas. Thus, it is substantial to identify and consider the different security challenges in the research and development associated with advanced UAS-based B5G/6G architectures. Catering to this requirement, this article conducts a comprehensive review of the security aspects of UASs with respect to the 5G/6G system architecture, its enabling technologies, and privacy issues. It exhibits security integration at all the protocol stack layers and analyzes the existing mechanisms to secure UAS-based B5G/6G communications and its energy and power optimization factors. Last, this article also summarizes modern technological trends for establishing security and protecting UAS-based systems, along with the open challenges and strategies for future research work.
Telecommunication, Transportation and communications
Considering the effect of surrounding rock on lining in the design of tunnel lining within fractured rock masses is challenging, particularly in accurately predicting the reserved deformation of the tunnel. This study bases a rock mass classification method and the established Hoek–Brown (H-B) strength criterion to assess the deformation characteristics of the surrounding rock. It establishes a more scientifically rigorous theoretical calculation method for the reserved deformation of tunnel linings that accounts for the rock–lining interaction. An optimization design approach for the lining structure, based on the synergistic effect and considering the stress safety of the concrete lining and the rock’s displacement release rate, is proposed. Case analysis is utilized to validate the safety of the lining design in the study section through computational verification. The recommended optimized lining parameters are identified: the support time is initiated when the tunnel wall’s surrounding rock deforms by 9 mm, and the lining thickness is optimized to 47 cm, which is approximately 36.5% less than the pre-optimization thickness. This precise optimization of support timing and lining thickness enhances both the safety and economic efficiency of the Wufengshan Tunnel. The method allows for the calculation of the optimal combination of support time and lining thickness tailored to different surrounding rock conditions, offering significant reference value for tunnel lining optimization.
Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions.
The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services.
This study leverages the staggered opening of new Metro stations in a suburb of Washington, DC to estimate the impact of proximity to public rail transit on housing prices. Both hedonic and repeat sales models indicate that housing prices increase as distance increases, suggesting that living near public transportation in Prince George’s County is primarily viewed as a disamenity. For properties at one mile from the nearest station, the preferred repeat sales model estimates a marginal price increase of 4.6 percent for a one-mile increase in distance. I argue that the suburban environment may be key in explaining the results. In the suburbs, a greater share of the population relies on automobiles, and rail stations are typically equipped with large parking lots. The suburban environment allows households the opportunity to both benefit from public transportation access and mitigate the negative externalities associated with living right next to the station.
Transportation engineering, Transportation and communications
The colossal evolution of wireless communication technologies over the past few years has driven increased interest in its integration in a variety of less-explored environments, such as the underwater medium. In this magazine paper, we present a comprehensive discussion on a novel concept of routing protocol known as cross-media routing, incorporating the marine and aerial interfaces. In this regard, we discuss the limitation of single-media routing and advocate the need for cross-media routing along with the current status of research development in this direction. To this end, we also propose a novel cross-media routing protocol known as bubble routing for autonomous marine systems where different sets of AUVs, USVs, and airborne nodes are considered for the routing problem. We evaluate the performance of the proposed routing protocol by using the two key performance metrics, i.e., packet delivery ratio (PDR) and end-to-end delay. Moreover, we delve into the challenges encountered in cross-media routing, unveiling exciting opportunities for future research and innovation. As wireless communication expands its horizons to encompass the underwater and aerial domains, understanding and addressing these challenges will pave the way for enhanced cross-media communication and exploration.
Integrated sensing and communications (ISAC) is an emerging critical technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RIS) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.
The intelligent transportation system (ITS) has been proven capable of effectively addressing traffic congestion issues. For vehicles to perform effectively and improve mobility under the intelligent driving environment, real-time prediction of traffic speed is undoubtedly essential. Considering the complex spatiotemporal dependency inherent in traffic data, conventional prediction models encounter many limitations. To improve the prediction performance and investigate the temporal features, this study focuses on emerging deep neural networks (DNNs) using the Caltrans Performance Measurement System (PeMS) data. This research also establishes an intelligent driving environment in the simulation and compares the traditional car-following model with deep learning methods in terms of multiple performance metrics. The results indicate that both supervised learning and unsupervised learning are superior to the simulation-based model on the freeway, and the two deep learning networks are almost identical to one another. Besides, the result reveals that all models have their latent features for different time dimensions under the low traffic loads, transition states, and heavy traffic loads. This is critical in the application of prediction technologies in ITS. The findings can assist transportation researchers and traffic engineers in both traffic operation and management, such as bottleneck identification, platooning control, and route planning.
Transportation engineering, Transportation and communications
Mahdi Deramnesari, Fahimeh Babalhavaeji, Zahra Abazari
et al.
<p>The main purpose of this study is to analyze the strategic role of public libraries in Iran’s Geopolitics of Information. This is applied research adopting a mixed-method (qualitative-quantitative) approach. A semi-structured interview was used to collect qualitative information. In the quantitative part, a researcher-made questionnaire was designed based on the results of the qualitative part. The statistical population of the research in the qualitative section included 13 experts and university professors who were selected purposefully based on a set of criteria. The qualitative data were analyzed using the qualitative content analysis method. The findings of the quantitative part were analyzed using factor analysis. The population of the quantitative part included the general managers and deputies of the general administrations of Iran's public libraries (70 people) and the librarians of the provinces that had the central public libraries from five clusters of north, south, east, west, and center of Iran, including 10 provinces and 160 librarians. Some 201 completed questionnaires were received. The findings showed that libraries had an implicit role in strengthening cultural identity and increasing public awareness, especially in strengthening Iranian-Islamic identity, as one of the three main components of geopolitics. However, this role is neither deliberated for a specific mission nor clearly explained in the upstream documents. Moreover, based on the findings, the acquisition of the centralized resources by the Iran Public Libraries Foundation for all public libraries is managed in an integrated manner such that there is not any clear role of user needs in the selection process. This is inconsistent with the Free-Flow of Information approach. The findings also suggested that national policymakers should pay attention to the role of public libraries as a public discourse space in transmitting culture and information and a base for strengthening the power of soft defense, training and explaining the specific role for librarians, developing technological infrastructure of libraries, providing resources based on local needs and the audience needs, continuous and purposeful monitoring of activities, and increasing the number of service recipients from public libraries.</p><p><a href="https://dorl.net/dor/%2020.1001.1.20088302.2022.20.1.8.1">https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.8.1</a></p>
Information resources (General), Transportation and communications
<p>Harnessing big data in organizations today realizes benefits for competitive advantage. Generated profound insights are reflected in informed decision making, creating better business plans, and improved service delivery. Yet, organizations are still not recognizing how mature their big data management capabilities are. However, there is no structured approach to assess and build necessary capabilities for valuable big data utilizing, which draws a clear improvement pathway. Existing solutions lack a consistent perception of big data management capabilities, a reliable assessment, and a rigid improvement scheme. This paper contributes in building an analytical study on existing key works in assessing and building big data management capabilities. Drawing upon the results and gaps revealed from this analytical study, the main requirements for building a comprehensive big data management maturity framework are defined. This framework will enable organizations to assess and improve their current capabilities towards effective big data management.</p><p><a href="https://dorl.net/dor/%2020.1001.1.20088302.2022.20.1.13.6">https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.13.6</a></p>
Information resources (General), Transportation and communications
With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move the computation-intensive workloads from low-end devices to a powerful edge server for video analytics, but the device-server communications will remain a bottleneck due to the limited bandwidth. This paper proposes a task-oriented communication framework for edge video analytics, where multiple devices collect the visual sensory data and transmit the informative features to an edge server for processing. To enable low-latency inference, this framework removes video redundancy in spatial and temporal domains and transmits minimal information that is essential for the downstream task, rather than reconstructing the videos at the edge server. Specifically, it extracts compact task-relevant features based on the deterministic information bottleneck (IB) principle, which characterizes a tradeoff between the informativeness of the features and the communication cost. As the features of consecutive frames are temporally correlated, we propose a temporal entropy model (TEM) to reduce the bitrate by taking the previous features as side information in feature encoding. To further improve the inference performance, we build a spatial-temporal fusion module at the server to integrate features of the current and previous frames for joint inference. Extensive experiments on video analytics tasks evidence that the proposed framework effectively encodes task-relevant information of video data and achieves a better rate-performance tradeoff than existing methods.
This article aims at providing insights for a comprehensive understanding of terahertz (THz) propagation channels. Specifically, we discuss essential THz channel characteristics to be well understood for the success of THz communications. The methodology of establishing realistic and 6G-compliant THz channel models based on measurements is then elaborated on, followed by a discussion on existing THz channel measurements in the literature. Finally, future research directions, challenges and measures to enrich the understanding of THz channels are discussed.