iTETRIS: An Integrated Wireless and Traffic Platform for Real-Time Road Traffic Management Solutions

Wireless vehicular cooperative systems have been identified as an attractive solution to improve road traffic management, thereby contributing to the European goal of safer, cleaner, and more efficient and sustainable traffic solutions. V2V-V2I communication technologies can improve traffic management through real-time exchange of data among vehicles and with road infrastructure. It is also of great importance to investigate the adequate combination of V2V and V2I technologies to ensure the continuous and costefficient operation of traffic management solutions based on wireless vehicular cooperative solutions. However, to adequately design and optimize these communication protocols and analyze the potential of wireless vehicular cooperative systems to improve road traffic management, adequate testbeds and field operational tests need to be conducted. Despite the potential of Field Operational Tests to get the first insights into the benefits and problems faced in the development of wireless vehicular cooperative systems, there is yet the need to evaluate in the long term and large dimension the true potential benefits of wireless vehicular cooperative systems to improve traffic efficiency. To this aim, iTETRIS is devoted to the development of advanced tools coupling traffic and wireless communication simulators

Optimization of GEO Satellite Links Deployment in the Internet

As the satellite technology will be one of the main components of the Next Generation Internet (NGI), a naturally occurring question concerns the feasibility of providing an optimal satellite-based Internet access. In this paper, we are interested in GEO satellite links deployment in the Internet by addressing the problem of terrestrial-satellite hybrid network optimization for which we propose a general architecture. We devide the general optimization problem into several sub-problems which can be resolved separately. Each sub-problem treats a specific type of traffic and it is defined by a set of inputs, variables, goals, and constraints. The global GEO satellite links deployment optimization heuristic that we propose attempts to determine the optimal hybrid topology which minimizes performance metrics such as delay, throughput, call blocking probabilities, and network cost. Two main steps compose the proposed heuristic: performance evaluation step and satellite uplink position finding step. During the first step we try to determine values of performance metrics of studied traffic and in the second one, we look for the optimal position where to add the current satellite uplink in order to optimize performance criteria. We present in details a case study dealing with multicast traffic optimization during the deployment of GEO satellite links in the Internet. We develop a configuration policy of PIM-SM in hybrid networks concerning the choice of the list of Rendezvous Point (RPs) and the switching from the RP-routed tree to the shortest path tree. This policy provides an optimal use of satellite links for multicast transfer. The obtained results demonstrate the ability of the proposed optimization method to improve multicast performan- ce criteria and to determine effectively satellite uplinks positions using PIM-SM combined with UDLR (UniDirectional Link Routing)

Real-time AI-based inference of people gender and age in highly crowded environments

Gender identity is one of the most fundamental aspects of life. Automatic gender identification is increasingly being used in areas such as security, marketing, and social robots. The objective of this paper is to address the challenges of gender and age identification in very crowded/noisy environments where faces are unclear and/or people are moving in relatively random directions. It presents an end-to-end real-time intelligent video analytics solution for instant people counting, gender and age estimation in crowded and open environments. The proposed solution includes a complete pipeline for training vision deep learning models and deploying them to edge devices connected to a distributed streaming analytics server. Our final Deep Learning architecture is an extended version of FairMOT, a multi-object tracking model, with two additional layers for multi-class gender classification and age regression. The training phase is performed using an enhanced and enriched version of the CrowdHuman dataset, a public dataset for human detection, with gender and age annotations added. The overall system has been validated for various movies and has shown state-of-the-art performance in terms of people tracking, gender and age inference. Our code, models, and data can be found at https://github.com/jasseur2017/people_gender_age.This work was made possible by NPRP Grant No.: NPRP12S-0304-190212 from the Qatar National Research Fund (a member of The Qatar Foundation). Open Access funding provided by the Qatar National Library

Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning

Traffic flow, number of vehicles passing a particular point over a given period of time, is an essential indicator for evaluating the performance and condition of road networks, detecting congestion, and predicting traffic trends. Accurate and reliable measurement of traffic flow in urban roads is challenging due to the dynamic nature of intersection signals and comes with high equipment and maintenance cost. WaveTraf is a Bluetooth-based Intelligent Traffic System solution widely deployed in the State of Qatar which detects and monitors the movement of Bluetooth-enabled devices anonymously using their unique MAC addresses. Systems such as WaveTraf allow for real-time, low-cost, scalable and non-intrusive traffic flow measurement; however, they could suffer from low detection and sampling rates leading to uncertain and unreliable estimates. In this research, we investigate various machine learning techniques such as Random Forrest, Support Vector Regression Machines and XGBoost to model the relationship between the ground-truth traffic flow based on video cameras and Bluetooth-based traffic flow. We utilized these techniques to enhance the dependability of Bluetooth-based traffic flow measurements, making it a more desirable and cost-effective solution for real-time traffic flow measurement.This work was supported by National Priorities Research Program through the Qatar National Research Fund (a member of The Qatar Foundation) under Grant NPRP13S-0206-200273. The statements made herein are solely the responsibility of the authors. The publication of this article was funded by Qatar National Library.Scopu

Haris: an Advanced Autonomous Mobile Robot for Smart Parking Assistance

This paper presents Haris, an advanced autonomous mobile robot system for tracking the location of vehicles in crowded car parks using license plate recognition. The system employs simultaneous localization and mapping (SLAM) for autonomous navigation and precise mapping of the parking area, eliminating the need for GPS dependency. In addition, the system utilizes a sophisticated framework using computer vision techniques for object detection and automatic license plate recognition (ALPR) for reading and associating license plate numbers with location data. This information is subsequently synchronized with a back-end service and made accessible to users via a user-friendly mobile app, offering effortless vehicle location and alleviating congestion within the parking facility. The proposed system has the potential to improve the management of short-term large outdoor parking areas in crowded places such as sports stadiums. The demo of the robot can be found on https://youtu.be/ZkTCM35fxa0?si=QjggJuN7M1o3oifx.Comment: Accepted in 2024 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 202

Enabling DSRC and C-V2X Integrated Hybrid Vehicular Networks: Architecture and Protocol

Emerging Vehicle-to-Everything (V2X) applications such as Advanced Driver Assistance Systems (ADAS) and Connected and Autonomous Driving (CAD) requires an excessive amount of data by vehicular sensors, collected, processed, and exchanged in real-time. A heterogeneous wireless network is envisioned where multiple Radio Access Technologies (RATs) can coexist to cater for these and other future applications. The primary challenge in such systems is the Radio Resource Management (RRM) strategy and the RAT selection algorithm. In this article, a Hybrid Vehicular Network (HVN) architecture and protocol stack is proposed, which combines Dedicated Short-Range Communication (DSRC) technology-enabled ad hoc network and infrastructure-based Cellular V2X (C-V2X) technologies. To this end, we address the design and performance evaluation of a distributed RRM entity that manages and coordinates Radio Resources (RR) in both RATs. Central to distributed RRM are adaptive RAT selection and Vertical Handover (VHO) algorithms supported by two procedures. (1) Measurement of Quality of Service (QoS) parameters and associated criteria to select the suitable RAT according to the network conditions. (2) Dynamic communication management (DCM) via implementing RR-QoS negotiation. The simulation results show the effectiveness of the proposed architecture and protocol suite under various parameter settings and performance metrics such as the number of VHOs, packet delivery ratio, and throughput, and latency

QoS-Aware Radio Access Technology (RAT) Selection in Hybrid Vehicular Networks

Política editorial: https://www.springernature.com/gp/open-science/policies/book-policiesThe increasing number of wireless communication technologies and standards bring immense opportunities and challenges to provide seamless connectivity in Hybrid Vehicular Networks (HVNs). HVNs could not only enhance existing applications but could also spur an array of new services. However, due to sheer number of use cases and applications with diverse and stringent QoS performance requirements it is very critical to efficiently decide on which radio access technology (RAT) to select. In this paper a QoS-aware RAT selection algorithm is proposed for HVN. The proposed algorithm switches between IEEE 802.11p based ad hoc network and LTE cellular network by considering network load and application’s QoS requirements. The simulation-based studies show that the proposed RAT selection mechanism results in lower number of Vertical Handovers (VHOs) and significant performance improvements in terms of packet delivery ratio, latency and application-level throughput

Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models

Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city management. However, DL models have been known to perform poorly on inconspicuous adversarial perturbations. Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored. In this paper, we perform a rigorous analysis of the adversarial vulnerabilities of DL-based crowd-flow prediction models under multiple threat settings, making three-fold contributions. (1) We propose CaV-detect by formally identifying two novel properties - Consistency and Validity - of the crowd-flow prediction inputs that enable the detection of standard adversarial inputs with 0% false acceptance rate (FAR). (2) We leverage universal adversarial perturbations and an adaptive adversarial loss to present adaptive adversarial attacks to evade CaV-detect defense. (3) We propose CVPR, a Consistent, Valid and Physically-Realizable adversarial attack, that explicitly inducts the consistency and validity priors in the perturbation generation mechanism. We find out that although the crowd-flow models are vulnerable to adversarial perturbations, it is extremely challenging to simulate these perturbations in physical settings, notably when CaV-detect is in place. We also show that CVPR attack considerably outperforms the adaptively modified standard attacks in FAR and adversarial loss metrics. We conclude with useful insights emerging from our work and highlight promising future research directions