Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management
In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Int...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/7/785 |
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| author | Srihari Kannan Gaurav Dhiman Yuvaraj Natarajan Ashutosh Sharma Sachi Nandan Mohanty Mukesh Soni Udayakumar Easwaran Hamidreza Ghorbani Alia Asheralieva Mehdi Gheisari |
| author_facet | Srihari Kannan Gaurav Dhiman Yuvaraj Natarajan Ashutosh Sharma Sachi Nandan Mohanty Mukesh Soni Udayakumar Easwaran Hamidreza Ghorbani Alia Asheralieva Mehdi Gheisari |
| author_sort | Srihari Kannan |
| collection | DOAJ |
| description | In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions. |
| first_indexed | 2024-03-10T12:53:17Z |
| format | Article |
| id | doaj.art-738e158985c54b879cbd1d8a2b0d1b4a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:53:17Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-738e158985c54b879cbd1d8a2b0d1b4a2023-11-21T12:08:03ZengMDPI AGElectronics2079-92922021-03-0110778510.3390/electronics10070785Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic ManagementSrihari Kannan0Gaurav Dhiman1Yuvaraj Natarajan2Ashutosh Sharma3Sachi Nandan Mohanty4Mukesh Soni5Udayakumar Easwaran6Hamidreza Ghorbani7Alia Asheralieva8Mehdi Gheisari9Department of Computer Science and Engineering, SNS College of Technology, Coimbatore 641035, IndiaDepartment of Computer Science, Government Bikram College of Commerce, Punjabi University, Patiala 147002, IndiaResearch and Development, ICT Academy, Chennai 600096, IndiaInstitute of Computer Technology and Information Security, Southern Federal University, 344006 Rostov-on-Don, RussiaDepartment of Computer Engineering, College of Engineering Pune, Pune 411005, IndiaDepartment of Computer Science and Engineering, Jagran Lakecity University, Bhopal 462044, IndiaDepartment of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore 641402, IndiaDepartment Electrical Engineering and Information Technology, Azad University of Tehran, Tehran, IranDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, ChinaIn this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions.https://www.mdpi.com/2079-9292/10/7/785deep neural networkVANETsroutingIoT agents |
| spellingShingle | Srihari Kannan Gaurav Dhiman Yuvaraj Natarajan Ashutosh Sharma Sachi Nandan Mohanty Mukesh Soni Udayakumar Easwaran Hamidreza Ghorbani Alia Asheralieva Mehdi Gheisari Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management Electronics deep neural network VANETs routing IoT agents |
| title | Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management |
| title_full | Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management |
| title_fullStr | Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management |
| title_full_unstemmed | Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management |
| title_short | Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management |
| title_sort | ubiquitous vehicular ad hoc network computing using deep neural network with iot based bat agents for traffic management |
| topic | deep neural network VANETs routing IoT agents |
| url | https://www.mdpi.com/2079-9292/10/7/785 |
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