Reducing Message Collisions in Sensing-based Semi-Persistent Scheduling (SPS) by Using Reselection Lookaheads in Cellular V2X

In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent scheduling (SPS) implements a message collision avoidance algorithm to cope with the undesirable effects of wireless channel congestion. Still, the current standard mechanism produces high number of packet collisions, which may hinder the high-reliability communications required in future C-V2X applications such as autonomous driving. In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. Specifically, we propose the use of the "lookahead," which contains the next starting resource location in the time-frequency plane. By exchanging the lookahead information piggybacked on the periodic safety message, vehicular user equipments (UEs) can eliminate most message collisions arising from the ignorance of other UEs' internal decisions. Although the proposed scheme would require the inclusion of the lookahead in the control part of the packet, the benefit may outweigh the bandwidth cost, considering the stringent reliability requirement in future C-V2X applications.Comment: Submitted to MDPI Sensor

Future Trends and Challenges for Mobile and Convergent Networks

Some traffic characteristics like real-time, location-based, and community-inspired, as well as the exponential increase on the data traffic in mobile networks, are challenging the academia and standardization communities to manage these networks in completely novel and intelligent ways, otherwise, current network infrastructures can not offer a connection service with an acceptable quality for both emergent traffic demand and application requisites. In this way, a very relevant research problem that needs to be addressed is how a heterogeneous wireless access infrastructure should be controlled to offer a network access with a proper level of quality for diverse flows ending at multi-mode devices in mobile scenarios. The current chapter reviews recent research and standardization work developed under the most used wireless access technologies and mobile access proposals. It comprehensively outlines the impact on the deployment of those technologies in future networking environments, not only on the network performance but also in how the most important requirements of several relevant players, such as, content providers, network operators, and users/terminals can be addressed. Finally, the chapter concludes referring the most notable aspects in how the environment of future networks are expected to evolve like technology convergence, service convergence, terminal convergence, market convergence, environmental awareness, energy-efficiency, self-organized and intelligent infrastructure, as well as the most important functional requisites to be addressed through that infrastructure such as flow mobility, data offloading, load balancing and vertical multihoming.Comment: In book 4G & Beyond: The Convergence of Networks, Devices and Services, Nova Science Publishers, 201

Per-Priority Flow Control (Ppfc) Framework For Enhancing Qos In Metro Ethernet

Day by day Internet communication and services are experiencing an increase in variety and quantity in their capacity and demand. Thus, making traffic management and quality of service (QoS) approaches for optimization of the Internet become a challenging area of research; meanwhile flow control and congestion control will be considered as significant fundamentals for the traffic control especially on the high speed Metro Ethernet. IEEE had standardized a method (IEEE 802.3x standard), which provides Ethernet Flow Control (EFC) using PAUSE frames as MAC control frames in the data link layer, to enable or disable data frame transmission. With the initiation of Metro Carrier Ethernet, the conventional ON/OFF IEEE 802.3x approach may no longer be sufficient. Therefore, a new architecture and mechanism that offer more flexible and efficient flow and congestion control, as well as better QoS provisioning is now necessary

Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)

In metropolitan areas, the exponential growth in quantity of vehicles has instigated gridlock, pollution, and delays in the transportation of freight. IoT is the modern revolution which pushes the world towards intelligent management systems and automated procedures. This makes a significant contribution to automation and intelligent societies. Traffic regulation and effective congestion management assist conserve many priceless resources. In order to recognize, collect and send data, autonomous vehicles are furnished with IoT powered Intelligent Traffic Management System (ITMS) having a set of sensors.  Moreover, machine learning (ML) algorithms can also be employed to enhance the transportation system.  Traffic jams, delays, and a high death rate are the results of the problems that the current transport management systems face.  In this paper, an active traffic control for VANET is proposed which merges Roaming Agents (RA) with deep neural networks (DNN). The effectiveness of the DNN with RA (RAD2N) routing method in VANETs is evaluated experimentally and compared with the traditional ML and other DL routing algorithms. Several traffic congestion indicators, including delay, packet delivery ratio (PDR) and throughput are used to validate RAD2N. The outcomes demonstrate that the proposed approach delivers lower latency and energy consumption

INTELIGENTNA TECHNIKA WYBORU OPTYMALIZATORA: BADANIE PORÓWNAWCZE ZMODYFIKOWANEGO MODELU DENSENET201 Z INNYMI MODELAMI GŁĘBOKIEGO UCZENIA

The rapid growth and development of AI-based applications introduce a wide range of deep and transfer learning model architectures. Selecting an optimal optimizer is still challenging to improve any classification type's performance efficiency and accuracy. This paper proposes an intelligent optimizer selection technique using a new search algorithm to overcome this difficulty. A dataset used in this work was collected and customized for controlling and monitoring roads, especially when emergency vehicles are approaching. In this regard, several deep and transfer learning models have been compared for accurate detection and classification. Furthermore, DenseNet201 layers are frizzed to choose the perfect optimizer. The main goal is to improve the performance accuracy of emergency car classification by performing the test of various optimization methods, including (Adam, Adamax, Nadam, and RMSprob). The evaluation metrics utilized for the model’s comparison with other deep learning techniques are based on classification accuracy, precision, recall, and F1-Score. Test results show that the proposed selection-based optimizer increased classification accuracy and reached 98.84%.Szybki wzrost i rozwój aplikacji opartych na sztucznej inteligencji wprowadzają szeroki zakres architektur modeli głębokiego uczenia i uczenia transferowego. Wybór optymalnego optymalizatora wciąż stanowi wyzwanie w celu poprawy wydajności i dokładności każdego rodzaju klasyfikacji. W niniejszej pracy proponowana jest inteligentna technika wyboru optymalizatora, wykorzystująca nowy algorytm wyszukiwania, aby pokonać to wyzwanie. Zbiór danych użyty w tej pracy został zebrany i dostosowany do celów kontroli i monitorowania dróg, zwłaszcza w sytuacjach, gdy zbliżają się pojazdy ratunkowe. W tym kontekście porównano kilka modeli głębokiego uczenia i uczenia transferowego w celu dokładnej detekcji i klasyfikacji. Ponadto, warstwy DenseNet201 zostały zamrożone, aby wybrać optymalizatora idealnego. Głównym celem jest poprawa dokładności klasyfikacji samochodów ratunkowych poprzez przeprowadzenie testów różnych metod optymalizacji, w tym (Adam, Adamax, Nadam i RMSprob). Metryki oceny wykorzystane do porównania modelu z innymi technikami głębokiego uczenia opierają się na dokładności klasyfikacji, precyzji, czułości i miarze F1. Wyniki testów pokazują, że zaproponowany optymalizator oparty na wyborze zwiększył dokładność klasyfikacji i osiągnął wynik na poziomie 98,84%