scholarly journals Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective

2019 ◽  
Vol 9 (23) ◽  
pp. 5254 ◽  
Author(s):  
Roberto Hernández-Jiménez ◽  
Cesar Cardenas ◽  
David Muñoz Rodríguez
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Intelligent Transportation Systems ◽  
Information Technologies ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Vehicular Communication ◽  
Hop Count ◽  
Routing Problem ◽  
Packet Delivery

The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning–based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks.

scholarly journals Optimization of Maximum Routing Hop Count Parameter Based on Vehicle Density for VANET

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhonghui Pei ◽  
Wei Chen ◽  
Hongjiang Zheng ◽  
Luyao Du
Keyword(s):  
Intelligent Transportation Systems ◽  
Ad Hoc ◽  
Transportation Systems ◽  
Packet Delivery Ratio ◽  
Distribution Model ◽  
Delivery Ratio ◽  
Communication Quality ◽  
Hop Count ◽  
Packet Delivery

Vehicular Ad Hoc Network (VANET) is the basic technology of intelligent transportation systems for providing reliable and real-time communications between vehicles and vehicles or roadside units. In order to improve the communication quality of VANET, this paper studies the effects of different maximum routing hop count parameters on the performance of the network under different vehicle densities. We establish the mathematical models of node connectivity probability and the packet delivery ratio by using the Poisson distribution model. And the maximum routing hop count selection algorithm (MRHSA) is proposed based on the theoretical model established in the paper. The simulation experiments and statistical analysis on packet delivery ratio, throughput, and end-to-end delay are performed under the straight road and urban road scenes, supported by the Vehicle in Network Simulation (Veins). The results show that the maximum routing hop count parameter is an important impact factor on the communication quality of the network. It is found that MRSHA proposed in this paper can improve the packet delivery ratio by about 9.1% at most in straight road scenarios, which indicates that MRHSA will contribute to the improvement of the communication quality of VANET.

Evaluation of Topology-Based Routing Protocols for Dissemination of Emergency Messages in Urban Vehicular Traffic Scenarios in India

2021 ◽  
pp. 46-74
Author(s):  
Pawan Singh ◽  
Suhel Ahmad Khan ◽  
Pramod Kumar Goyal
Keyword(s):  
Routing Protocols ◽  
Intelligent Transportation Systems ◽  
Road Transport ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
New Delhi ◽  
Network Simulator ◽  
Vehicular Traffic ◽  
Road Accidents ◽  
Packet Delivery

VANET is a subclass of MANET that makes the dream of intelligent transportation systems come true. As per the report of the Ministry of Road Transport and Highways, India, 1.5 million people were killed in road accidents in 2015. To reduce casualty and provide some kind of comfort during the journey, India must also implement VANETs. Applicability of VANET in Indian roads must be tested before implementation in reality. In this chapter, the real maps of Connaught Place, New Delhi from Open Street maps websites is considered. The SUMO for traffic and flow modeling is used. Many scenarios have been used to reflect real Indian road conditions to measure the performance of AODV, DSDV, and DSR routing protocols. The CBR traffic is used for the dissemination of emergency messages in urban vehicular traffic scenarios. The throughput, packet delivery ratio, and end-to-end delay are considered for performance analysis through the NS-2.35 network simulator.

scholarly journals A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication

2021 ◽  
Vol 11 (19) ◽  
pp. 9089
Author(s):  
Radwa Ahmed Osman ◽  
Ahmed Kadry Abdelsalam
Keyword(s):  
Cooperative Communication ◽  
Intelligent Transportation Systems ◽  
Autonomous Vehicle ◽  
Optimization Technique ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Emergency Vehicle ◽  
System Quality ◽  
Vehicular Communication ◽  
Proposed Model

Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL).

scholarly journals Analisis Performansi Protokol Routing Proaktif pada Jaringan Mobile Adhoc

2018 ◽  
Vol 22 (2) ◽  
pp. 120-128
Author(s):  
Rohmah Nur Hidayah ◽  
Indrabayu Indrabayu ◽  
Intan Sari Areni
Keyword(s):  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
End To End Delay ◽  
Overhead Ratio

Intelligent Transportation Systems (ITS) menawarkan paradigma pemodelan baru yang mendukung komunikasi antar kendaraan secara real time menggunakan routing protocol yang disebut Vehicular Ad Hoc Network (VANET). Pada dasarnya kinerja routing protocol dipengaruhi oleh arus dan aturan lalu lintas yang bersifat dinamis sehingga perubahan tersebut akan menyebabkan perubahan pada kinerja routing protocol juga. Untuk itu, penelitian ini mengusulkan rancangan mobilitas realistis berdasarkan data makroskopis dan mikroskopis jalan perkotaan. Rancangan mobilitas dibagi menjadi 2 skenario berdasarkan kepadatan kendaraan, yaitu 125 dan 200 node. Penelitian ini bersifat simulasi dan dibagi menjadi 2 tahap. Tahap pertama yaitu simulasi mobilitas yang menunjukkan pergerakan kendaraan serta aturan lalu lintas yang disesuaikan dengan kondisi realistis. Tahap kedua adalah simulasi jaringan yang bertujuan untuk mengevaluasi kinerja routing protocol DSDV dan OLSR terhadap rancangan model mobilitas. Untuk menguji kinerja kedua  routing protocol, maka digunakan 3 metrik pengujian yaitu Packet Delivery Ratio (PDR), Overhead Ratio (OR) dan End to End Delay (E2ED). Hasil simulasi menunjukkan OLSR unggul pada metrik PDR dan OR, yaitu masing-masing 88.62% dan 57.11%. Sedangkan E2ED terbaik ditunjukkan oleh DSDV dengan nilai 0.523 detik. Kinerja terbaik kedua routing protocol ditunjukkan pada skenario 125 node. Hal ini menunjukkan kedua routing protocol belum mampu mengatasi kondisi lalu lintas perkotaan yang sangat padat.

Modified fuzzy-based greedy routing protocol for VANETs

2020 ◽  
Vol 39 (6) ◽  
pp. 8357-8364
Author(s):  
Thompson Stephan ◽  
Ananthnarayan Rajappa ◽  
K.S. Sendhil Kumar ◽  
Shivang Gupta ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Research Area ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Greedy Routing ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

Vision-based Autonomous Vehicle Recognition

2021 ◽  
Vol 54 (4) ◽  
pp. 1-37
Author(s):  
Azzedine Boukerche ◽  
Xiren Ma
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Comprehensive Evaluation ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Regional Security ◽  
Future Research ◽  
Learning Abilities ◽  
Vehicle Recognition ◽  
Model Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

scholarly journals Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1136
Author(s):  
David Augusto Ribeiro ◽  
Juan Casavílca Silva ◽  
Renata Lopes Rosa ◽  
Muhammad Saadi ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Intelligent Transportation Systems ◽  
Light Field ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Learning Framework ◽  
Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

scholarly journals Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1942
Author(s):  
Rogaia Mhemed ◽  
Frank Comeau ◽  
William Phillips ◽  
Nauman Aslam
Keyword(s):  
Sensor Networks ◽  
Routing Protocol ◽  
Opportunistic Routing ◽  
Packet Delivery Ratio ◽  
Delivery Ratio ◽  
Underwater Sensor Networks ◽  
Network Resources ◽  
Hop Count ◽  
Intermediate Node ◽  
Packet Delivery

Much attention has been focused lately on the Opportunistic Routing technique (OR) that can overcome the restrictions of the harsh underwater environment and the unique structures of the Underwater Sensor Networks (UWSNs). OR enhances the performance of the UWSNs in both packet delivery ratio and energy saving. In our work; we propose a new routing protocol; called Energy Efficient Depth-based Opportunistic Routing with Void Avoidance for UWSNs (EEDOR-VA), to address the void area problem. EEDOR-VA is a reactive OR protocol that uses a hop count discovery procedure to update the hop count of the intermediate nodes between the source and the destination to form forwarding sets. EEDOR-VA forwarding sets can be selected with less or greater depth than the packet holder (i.e., source or intermediate node). It efficiently prevents all void/trapped nodes from being part of the forwarding sets and data transmission procedure; thereby saving network resources and delivering data packets at the lowest possible cost. The results of our extensive simulation study indicate that the EEDOR-VA protocol outperforms other protocols in terms of packet delivery ratio and energy consumption

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