Hasil untuk "Transportation and communications"

Ying Yang, Jiahao Zhan, Yang Liu et al.

Jinpeng Zhang, Yan Xu, Kaiquan Cai et al.

Zain Ul Abideen Tariq, Emna Baccour, Aiman Erbad et al.

Rapid growth in wireless data traffic and the increasing demand for secure, low-latency communication have driven research toward next-generation (6G) technology, which aims to provide ubiquitous, secure wireless connectivity. Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology in 6G, offering a means of combating increasing physical layer security threats by smartly managing wireless channel conditions. This survey offers an in-depth review of RIS-assisted anti-jamming strategies in next-generation wireless communication networks, encompassing threats, solutions, and research challenges. We start by presenting the analysis of existing surveys and their research gaps in the area of the use of RIS for the security of wireless communications. Following this, we present core concepts of jamming, and the threats they pose across various wireless networks, challenges of state-of-the-art anti-jamming techniques, motivation for using RIS for anti-jamming, and its potential applications. The survey also examines state-of-the-art RIS-assisted countermeasures against jamming threats in wireless networks, some key lessons learned, and gaps identified. In conclusion, we identify key technical challenges and propose future research directions for RIS-assisted anti-jamming in next-generation wireless communication networks, underscoring the potential of RIS technology to enhance security and resilience in future wireless systems.

Chuan-Wei Cho, Meng-Shiuan Pan

The integrated access and backhaul (IAB) architecture utilizes wireless backhaul to facilitate the expansion of fifth-generation (5G) New Radio (NR) networks. In an IAB network, intermediate base stations (or say IAB nodes) can be connected in a multi-hop fashion. However, optimizing resource scheduling in such a network remains a critical challenge. In this work, we present a novel method that integrates multi-user multiple-input and multiple-output (MU-MIMO) and non-orthogonal multiple access (NOMA) technologies into IAB networks. The designed two-phase algorithm has the following features: 1) support for multi-path routing and efficient resource utilization through the combined use of MU-MIMO and NOMA, 2) a novel route decision phase that selects optimal paths by considering load balancing among IAB nodes, and 3) a dynamic link scheduling phase that allocates transmission power and schedules links to maximize network capacity. Simulation results demonstrate that the proposed solution achieves significant improvements in throughput, fairness, and latency compared to existing methods.

Dejie Xu, Huilin Geng, Changwu Hui et al.

The rapid expansion of urban rail transit networks has increased their vulnerability to disruptions. When a metro line experiences sudden interruptions, it can severely reduce passenger mobility and degrade the overall transportation system performance. Existing bus feeder programs are often inadequate in responding effectively to dynamic and real-time fluctuations in passenger flow during such disruptions, particularly when combined with complex road traffic conditions. We propose a novel hybrid metaheuristic algorithm for the emergency feeder bus routes with time-window constraints to address this issue. The algorithm combines the Max-Min Ant System (MMAS) and Simulated Annealing (SA) to enhance search performance. A Back Propagation (BP) neural network estimates the emergency demand at each affected station, using historical and structural factors. These estimates are integrated into the hybrid optimization process, improving routing efficiency. The model aims to minimize the total operational time of emergency buses, ensuring timely evacuation. A case study using Beijing Metro validates the model’s effectiveness. Results indicate a 1.7-hour reduction in total passenger travel time and an 84.7% decrease in computation time compared to the Gurobi exact algorithm. These improvements facilitate the identification of optimal feeder paths under varying traffic conditions. The study provides practical guidance for enhancing emergency response strategies in metro systems.

Chong Huang, Gaojie Chen, Zhuoao Xu et al.

In recent years, unmanned aerial vehicles (UAVs) have become a key role in wireless communication networks due to their flexibility and dynamic adaptability. However, the openness of UAV-based communications leads to security and privacy concerns in wireless transmissions. This paper investigates a framework of UAV covert communications which introduces flexible reconfigurable intelligent surfaces (F-RIS) in UAV networks. Unlike traditional RIS, F-RIS provides advanced deployment flexibility by conforming to curved surfaces and dynamically reconfiguring its electromagnetic properties to enhance the covert communication performance. We establish an electromagnetic model for F-RIS and further develop a fitted model that describes the relationship between F-RIS reflection amplitude, reflection phase, and incident angle. To maximize the covert transmission rate among UAVs while meeting the covert constraint and public transmission constraint, we introduce a strategy of jointly optimizing UAV trajectories, F-RIS reflection vectors, F-RIS incident angles, and non-orthogonal multiple access (NOMA) power allocation. Considering this is a complicated non-convex optimization problem, we propose a deep reinforcement learning (DRL) algorithm-based optimization solution. Simulation results demonstrate that our proposed framework and optimization method significantly outperform traditional benchmarks, and highlight the advantages of F-RIS in enhancing covert communication performance within UAV networks.

Ioannis Papoutsidakis, George C. Alexandropoulos

This paper considers the achievable rate-exponent region of integrated sensing and communication systems in the presence of variable-length coding with feedback. This scheme is fundamentally different from earlier studies, as the coding methods that utilize feedback impose different constraints on the codewords. The focus herein is specifically on the Gaussian channel, where three achievable regions are analytically derived and numerically evaluated. In contrast to a setting without feedback, we show that a trade-off exists between the operations of sensing and communications.

Qingliang Li, Bo Chang, Weidong Mei et al.

In the upcoming industrial internet of things (IIoT) era, a surge of task-oriented applications will rely on real-time wireless control systems (WCSs). For these systems, ultra-reliable and low-latency wireless communication will be crucial to ensure the timely transmission of control information. To achieve this purpose, we propose a novel time-sequence-based semantic communication paradigm, where an integrated sensing, computing, communication, and control (ISC3) architecture is developed to make sensible semantic inference (SI) for the control information over time sequences, enabling adaptive control of the robot. However, due to the causal correlations in the time sequence, the control information does not present the Markov property. To address this challenge, we compute the mutual information of the control information sensed at the transmitter (Tx) over different time and identify their temporal semantic correlation via a semantic feature extractor (SFE) module. By this means, highly correlated information transmission can be avoided, thus greatly reducing the communication overhead. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to reconstruct the control information based on the previously received one if the information transmission is not activated at the Tx. Furthermore, a control gain policy is also employed at the Rx to adaptively adjust the control gain for the controlled target based on several practical aspects such as the quality of the information transmission from the Tx to the Rx. We design the neural network structures of the above modules/policies and train their parameters by a novel hybrid reward multi-agent deep reinforcement learning framework. On-site experiments are conducted to evaluate the performance of our proposed method in practice, which shows significant gains over other baseline schemes.

Frank Ngeni, Judith Mwakalonge, Gurcan Comert et al.

Pediatric Vehicular Heatstroke (PVH) has been among the leading cause of non-traffic deaths among kids in the US. According to NHTSA, more than 900 children have died since 1998 because of being left in cars or accessing the cars without notice by adults. As of 2020, 40% of the 50 US states and Washington DC have policies on hot car deaths. This paper aims to answer the following research questions a) what role and statistical significance do socioeconomic and environmental factors play in PVH deaths? b) effectiveness of the policies enacted, and c) public opinions and perceptions on the PVH deaths? The paper utilized data from different sources to examine the influence of socioeconomic and environmental factors on PVH deaths in 3,012 randomly sampled cities. Findings showed that a) average high temperature and people living in poverty in the city have a significant influence on PVH deaths b) median income had an insignificant effect on PVH deaths however further studies will be needed. However, a median income ratio to the number of deaths suggests states with higher ratios have the lowest PVH death cases c) Texas, Florida, Alabama, and California had a significant number of deaths per year after policy implementation. In contrast, California and Illinois had a significant number of deaths per year before the policy implementation periods. The paper is aimed to stimulate public awareness campaigns, safety advocates, and policy decision-makers to implement effective measures to reduce PVH-related deaths.

Fei Dai, Yawen Chen, Zhiyi Huang et al.

In the realm of parallel and distributed computation, All-gather operation, a process where each node in a distributed system gathers data from all others, is pivotal. This operation underpins various high-performance computing (HPC) applications, notably in distributed deep learning (DL), by enabling model and hybrid parallelisms. Although optical interconnection networks promise unmatched bandwidth and reliability for data transfers between distributed nodes, most current All-gather algorithms remain optimized for electrical interconnects, leading to suboptimal performance in optical contexts. This paper proposes &#x201C;OpTree&#x201D;, an advanced scheme distinctly designed for All-gather operation in optical interconnect systems. OpTree constructs an optimal <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-ary tree that minimizes communication time by determining the optimal number of communication stages. A comprehensive comparison between OpTree&#x2019;s communication steps and existing All-gather algorithms is provided. Theoretical insights reveal that OpTree substantially curtails communication steps within optical interconnects. Constraints imposed by OpTree on optical communication are also elaborated. Empirical evaluations, through rigorous simulations, establish that: 1) OpTree is effective in generating an optimal m-ary tree for minimizing communication time. 2) For a 1024-node optical ring system, OpTree cuts communication time by 72.97&#x0025;, 93.15&#x0025;, and 86.32&#x0025; against WRHT, Ring, and Neighbor Exchange (NE) schemes, respectively, tested over different message sizes. 3) With varying node counts, the reductions stand at 42.27&#x0025;, 92.74&#x0025;, and 85.49&#x0025; against the same counterparts. 4) As the number of wavelengths increases, communication time further diminishes.

Amr E. Aboeleneen, Alaa A. Abdellatif, Aiman M. Erbad et al.

Recent advancements in Software Defined Networks (SDN), Open Radio Access Network (O-RAN), and 5G technology have significantly expanded the capabilities of wireless networks, extending beyond mere data transmission. This progression has led to the emergence of Virtual Networks (VN) and Network Slicing, enabling industries to enhance their services and applications by establishing virtual networks that utilize shared physical infrastructure. Many works in the literature have considered optimizing the allocation of on-demand slices, assuming the absolute availability of resources and their accurate load. However, accurately allocating future network slices remains challenging due to the error in load prediction, diverse Key Performance Indicators (KPIs), resource price variations, and the potential for over- or under-provisioning. This study presents a two-phase intelligent approach to address these challenges. The framework proactively predicts different slice loads while considering prediction errors in optimizing future slices with varied KPIs in a cost-efficient manner. Specifically, our method utilizes historical load data per service and employs AI-based forecasts for service load prediction. Subsequently, it employs a Deep Reinforcement Learning (DRL) agent on O-RAN&#x2019;s virtual Control Unit (vCU) and virtual Distributed unit (vDU) to correct errors in prediction and optimize the cost of slice allocation based on service KPI requirements, ultimately pre-allocating future network slices at reduced costs. Through experimental validation against various baselines and state-of-the-art solutions, we demonstrate the efficacy of our proposed solution, achieving a notable reduction (37-51%) in the average cost of allocated slices while inquiring about (1.5-7%) of additional resources compared to the state-of-the-art..

Roya Alizadeh, Yvon Savaria, Chahe Nerguizian

Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate estimated from CSI information as a feature, we achieve precision &#x003D; 83&#x0025;, True Positive <inline-formula> <tex-math notation="LaTeX">$(TP)=94\%$ </tex-math></inline-formula>, True Negative <inline-formula> <tex-math notation="LaTeX">$(TN)= 91\%$ </tex-math></inline-formula> and F1-score &#x003D; 87&#x0025;. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision &#x003D; 100&#x0025;, <inline-formula> <tex-math notation="LaTeX">$TP=97\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$TN = 95\%$ </tex-math></inline-formula> and F1-score &#x003D; 95&#x0025;.

Abimbola Ogungbire, Panick Kalambay, Srinivas S. Pulugurtha

Mountainous areas pose unique challenges to transportation safety, with their complex terrain and harsh weather conditions. The presence of steep terrain can lead to winding roads with a high degree of azimuth change per mile, thereby increasing the potential for hazardous driving conditions. Additionally, steep terrain is often associated with fog formation, which can obscure roads by clouds. Thus, there is a need to study weather-related crashes and identify associated risk factors in mountainous areas. To address this issue, the Tobit, latent class Tobit (LCT), and two-part truncated log normal (TPTLN) models were employed to investigate the potential interactions between topography and weather-related crash rates, while accounting for the censored nature of crash data. Data from the western region of North Carolina, which is home to numerous mountains experiencing a wide range of weather conditions was used for this purpose. This region comprises a 3570-mile road network across seven cities, with elevations ranging from 1000 ft. to >6158 ft. above mean sea level. Crash data from 2015 to 2017 was obtained from the Highway Safety Information System (HSIS). The results revealed that topography significantly affect weather-related crashes in mountainous areas. Higher elevations and steeper slopes are associated with lower crash rates. Also, the study highlights the importance of considering topography when assessing transportation safety in mountainous areas. The findings help develop transportation safety policies and interventions aimed at improving safety in these areas.

Xinze Lyu, Sundar Aditya, Bruno Clerckx

A canonical use case of Integrated Sensing and Communications (ISAC) in multiple-input multiple-output (MIMO) systems involves a multi-antenna transmitter communicating with $K$ users and sensing targets in its vicinity. For this setup, precoder and multiple access designs are of utmost importance, as the limited transmit power budget must be efficiently directed towards the desired directions (users and targets) to maximize both communications and sensing performance. This problem has been widely investigated analytically under various design choices, in particular (a) whether or not a dedicated sensing signal is needed, and (b) for different MIMO multiple access techniques, such as Space Division Multiple Access (SDMA) and Rate-Splitting Multiple Access (RSMA). However, a conclusive answer on which design choice achieves the best ISAC performance, backed by experimental results, remains elusive. We address this vacuum by experimentally evaluating and comparing RSMA and SDMA for communicating with two users $(K = 2)$ and sensing (ranging) one target. Over three scenarios that are representative of \emph{vehicular} ISAC, covering different levels of inter-user interference and separation/integration between sensing and communications, we show that RSMA without a dedicated sensing signal achieves better ISAC performance -- i.e., higher sum throughput (up to $50\%$ peak throughput gain) for similar radar SNR (between $20$ to $24{\rm dB}$) -- than SDMA with a dedicated sensing signal. This first-ever experimental study of RSMA ISAC demonstrates the feasibility and the superiority of RSMA for future multi-functional wireless systems.

Wen-Yu Dong, Shaoshi Yang, Ping Zhang et al.

Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Mat{é}rn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.

Xiaoyi Wu, Nisrine Mouhrim, Andrea Araldo et al.

Conventional Public Transport (PT) is based on fixed lines, running with routes and schedules determined a-priori. In low-demand areas, conventional PT is inefficient. Therein, Mobility on Demand (MoD) could serve users more efficiently and with an improved quality of service (QoS). The idea of integrating MoD into PT is therefore abundantly discussed by researchers and practitioners, mainly in the form of adding MoD on top of PT. Efficiency can be instead gained if also conventional PT lines are redesigned after integrating MoD in the first or last mile. In this paper we focus on this re-design problem. We devise a bilevel optimization problem where, given a certain initial design, the upper level determines stop selection and frequency settings, while the lower level routes a fleet of MoD vehicles. We propose a solution method based on Particle Swarm Optimization (PSO) for the upper level, while we adopt Large Neighborhood Search (LNS) in the lower level. Our solution method is computationally efficient and we test it in simulations with up to 10k travel requests. Results show important operational cost savings obtained via appropriately reducing the conventional PT coverage after integrating MoD, while preserving QoS.

Noel Farrugia, Daniel Bonanno, Nicholas Frendo et al.

There is mounting evidence that a second quantum revolution based on the technological capabilities to detect and manipulate single quantum particles (e.g., electrons, photons, ions, etc), a feat not achieved during the first quantum revolution, is progressing fast. It is expected that in less than 10 years, this second quantum revolution shall have a significant impact over numerous industries, including finance, medicine, energy, transportation, etc. Quantum computers threaten the status quo of cybersecurity, due to known quantum algorithms that can break asymmetric encryption, which is what gives us the ability to communicate securely using a public channel. Considering the world's dependence on digital communication through data exchange and processing, retaining the ability to communicate securely even once quantum computers come into play, cannot be stressed enough. Two solutions are available: Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC); which, we emphasise, are not mutually exclusive. The EuroQCI initiative, of which EQUO is a part of, focuses on QKD and aims to build a network whereby EU countries can communicate securely through QKD. To this aim, the DEP (Digital Europe Programme) project aims to bring technological matureness to QKD by deploying a QKD test network and, through this exercise, understand what is lacking from an operator's point of view when the time to integrate QKD in their network comes.

Pankaj Kant, Pavan Kumar Machavarapu, Anagha R Natha

ABSTRACTThe monsoon pattern has shifted across Kerala. This, combined with significant deforestation and hill denuding, has resulted in catastrophic floods and landslides, particularly during the Southwest monsoon. Urgent relief services were to be delivered in a timely and accurate manner in order to sustain the lives of the impacted people. Even though resources were sent to several relief camps, they were either in excess or shortage on multiple instances. The conditions that prevailed during the monsoon time, as well as concerns and challenges during disaster relief efforts, must first be investigated for the effective operation of these supply chain activities. This research aims to develop a disaster logistics hub location selection decision support system, based on the Fuzzy Analytic Hierarchy Process (FAHP) and Best Worst Method (BWM), to meet the needs of disaster victims and rescue teams in the event of flooding, and to implement the proposed systems in Kuttanad, Kerala. Initially, the criteria are determined and structured in the hierarchy, and the weightage for the criteria is done via a questionnaire technique. Expert opinion on nine points scale was gathered from a set of experts working under different levels of positions during disaster management. The weights obtained by the FAHP and BWM methods were statistically analysed and compared for the reliability of the two techniques. The suggested model and application results may throw light on future work, particularly in the realm of disaster logistics management.

Shuai Ma, Ruixin Yang, Chun Du et al.

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

Chang Cai, Xiaojun Yuan, Ying-Jun Angela Zhang

In task-oriented communications, most existing work designed the physical-layer communication modules and learning based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimizing conventional communication metrics, such as throughput maximization, delay minimization, or bit error rate minimization. The inconsistency between the design objectives may hinder the exploitation of the full benefits of task-oriented communications. In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i.e., feature encoding and classification) and communication (i.e., precoding) modules are designed with the same goal of inference accuracy maximization. Instead of end-to-end learning which involves both the task dataset and wireless channel during training, we advocate a separate design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR2) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem. We cast valuable insights into this formulation and develop a block coordinate ascent (BCA) algorithm for efficient problem-solving. Moreover, the MCR2 objective serves the loss function for feature encoding and guides the classification design. Simulation results on the synthetic features explain the mechanism of MCR2 precoding at different SNRs. We also validate on the CIFAR-10 and ModelNet10 datasets that the proposed design achieves a better latency-accuracy tradeoff compared to various baselines. As such, our work paves the way for further exploration into the synergistic alignment of learning and communication objectives in task-oriented communication systems.