An Effective Data Dissemination Using Multi Objective Congestion Metric Based Artificial Ecosystem Optimization for Vehicular Ad-Hoc Network

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Author(s)

Nagaraj B. Patil 1 Shaeista Begum 2,*

1. Government Engineering College, Gangavathi-583227, India

2. Government Engineering College, Raichur-584135, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2023.01.05

Received: 7 Jan. 2022 / Revised: 15 Apr. 2022 / Accepted: 23 Jun. 2022 / Published: 8 Feb. 2023

Index Terms

Ad-Hoc On-Demand Distance Vector Routing Protocol, Multi Objective Congestion Metric based Artificial Ecosystem Optimization, Packet Delivery Ratio, Vehicular Ad-Hoc Network, Visible Light Communication

Abstract

Vehicular Ad-hoc Network (VANET) is a growing technology that utilizes moving vehicles as mobile nodes for exchanging essential information between users. Unlike the conventional radio frequency based VANET, the Visible Light Communication (VLC) is used in the VANET to improve the throughput. However, the road safety is considered as a significant issue for users of VANET. Therefore, congestion-aware routing is required to be developed for enhancing road safety, because it creates a collision between the vehicles that causes packet loss. In this paper, the Multi Objective Congestion Metric based Artificial Ecosystem Optimization (MOCMAEO) is proposed to enhance road safety. The MOCMAEO is used along with the Ad hoc On-Demand Distance Vector (AODV) routing protocol for generating the optimal routing path between the source node to the Road Side Unit (RSU). Specifically, the performance of the MOCMAEO is improved using the multi-objective fitness functions such as congestion metric, residual energy, distance, and some hops. The performance of the MOCMAEO is analyzed by means of Packet Delivery Ratio (PDR), throughput, delay, and Normalized Routing Load (NRL). The PSO based geocast routing protocols such as LARgeoOPT, DREAMgeoOPT, and ZRPgeoOPT are used to evaluate the performance of the MOCMAEO method. The PDR of the MOCMAEO method is 99.92 % for 80 nodes, which is high when compared to the existing methods.

Cite This Paper

Nagaraj B. Patil, Shaeista Begum, "An Effective Data Dissemination Using Multi Objective Congestion Metric Based Artificial Ecosystem Optimization for Vehicular Ad-Hoc Network", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.1, pp.54-63, 2023. DOI:10.5815/ijcnis.2023.01.05

Reference

[1]M. Shelke, A. Malhotra, and P. N. Mahalle, “Fuzzy priority based intelligent traffic congestion control and emergency vehicle management using congestion-aware routing algorithm,” J. Ambient Intell. Hum. Comput., October 2019.
[2]K. Satheshkumar and S. Mangai, “EE-FMDRP: energy efficient-fast message distribution routing protocol for vehicular ad-hoc networks,” J. Ambient Intell. Hum. Comput., vol. 12, no. 3, pp. 3877–3888, January 2020.
[3]Ramesh B. Koti, Mahabaleshwar S. Kakkasageri, "Multi Agent Assisted Safety Information Dissemination Scheme for V2V Communication in VANETs: Intelligent Agent Approach", International Journal of Intelligent Systems and Applications, Vol.13, No.4, pp.49-62, 2021.
[4]S. Jobaer, Y. Zhang, M. A. Iqbal Hussain, and F. Ahmed, “UAV-Assisted Hybrid Scheme for Urban Road Safety Based on VANETs,” Electronics, vol. 9, no. 9, p. 1499, September 2020.
[5]W. Ahsan, M. F. Khan, F. Aadil, M. Maqsood, S. Ashraf, Y. Nam, and S. Rho, “Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms,” Electronics, vol. 9, no. 3, p. 394, February 2020.
[6]K. K. Rana, S. Tripathi, and R. S. Raw, “Link reliability-based multi-hop directional location routing in vehicular ad hoc network,” Peer-to-Peer Networking and Applications, vol. 13, no. 5, pp. 1656–1671, September 2020.
[7]M. A. Gawas and S. S. Govekar, “A novel selective cross layer based routing scheme using ACO method for vehicular networks,” Journal of Network and Computer Applications, vol. 143, pp. 34–46, October 2019.
[8]Akanksha Choudhary, Rajeev Pandey, Anjna Deen,"Location Based Data Aggregation with Energy Aware Scheduling at RSU for Effective Message Dissemination in VANET", International Journal of Engineering and Manufacturing, Vol.7, No.3, pp.49-57, 2017.
[9]D. L. Msongaleli and K. Kucuk, “Optimal resource utilisation algorithm for visible light communication‐based vehicular ad‐hoc networks,” IET Intel. Transport Syst., vol. 14, no. 2, pp. 65–72, February 2020.
[10]Vikram Dhiman, Ikjot Saini, Manoj Kumar,"A Comprehensive Survey of Location Based Routing in Vehicular Networks", International Journal of Wireless and Microwave Technologies, Vol.7, No.1, pp.40-48, 2017.
[11]B. Suganthi and P. Ramamoorthy, “An Advanced Fitness Based Routing Protocol for Improving QoS in VANET,” Wireless Personal Communications, vol. 114, no. 1, pp. 241–263, September 2020.
[12]K. K. Rana, S. Tripathi, and R. S. Raw, “Opportunistic Directional Location Aided Routing Protocol for Vehicular Ad-Hoc Network,” Wireless Personal Communications, vol. 110, no. 3, pp. 1217–1235, February 2020.
[13]X. Bao, H. Li, G. Zhao, L. Chang, J. Zhou, and Y. Li, “Efficient clustering V2V routing based on PSO in VANETs,” Measurement, vol. 152, p. 107306, February 2020.
[14]A. Paranjothi, M. S. Khan, R. Patan, R. M. Parizi, and M. Atiquzzaman, “VANETomo: A congestion identification and control scheme in connected vehicles using network tomography,” Comput. Commun., vol. 151, pp. 275–289, February 2020.
[15]J. Aznar-Poveda, A.-J. Garcia-Sanchez, E. Egea-Lopez, and J. Garcia-Haro, “MDPRP: A Q-Learning Approach for the Joint Control of Beaconing Rate and Transmission Power in VANETs,” IEEE Access, vol. 9, pp. 10166–10178, January 2021.
[16]M. J. Ahmed, S. Iqbal, K. M. Awan, K. Sattar, Z. A. Khan, and H. H. R. Sherazi, “A Congestion Aware Route Suggestion Protocol for Traffic Management in Internet of Vehicles,” Arabian Journal for Science and Engineering, vol. 45, no. 4, pp. 2501–2511, April 2020.
[17]B.-M. Cho, M.-S. Jang, and K.-J. Park, “Channel-Aware Congestion Control in Vehicular Cyber-Physical Systems,” IEEE Access, vol. 8, pp. 73193–73203, April 2020.
[18]V. Kovtun and I. Izonin, “Study of the Operation Process of the E-Commerce Oriented Ecosystem of 5Ge Base Station, Which Supports the Functioning of Independent Virtual Network Segments,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 7, pp. 2883–2897, October 2021.
[19]V. Kovtun, I. Izonin, and M. Gregus, “Mathematical models of the information interaction process in 5G-IoT ecosystem: Different functional scenarios,” ICT Express, November 2021.
[20]V. Kovtun, I. Izonin, and M. Gregus, “Formalization of the metric of parameters for quality evaluation of the subject-system interaction session in the 5G-IoT ecosystem,” Alexandria Eng. J., vol. 61, no. 10, pp. 7941–7952, October 2022.
[21]A. Srivastava, A. Prakash, and R. Tripathi, “An adaptive intersection selection mechanism using ant Colony optimization for efficient data dissemination in urban VANET,” Peer-to-Peer Networking and Applications, vol. 13, no. 5, pp. 1375–1393, September 2020.
[22]A. Husain, S. P. Singh, and S. C. Sharma, “PSO Optimized Geocast Routing in VANET,” Wireless Personal Communications, vol. 115, no. 3, pp. 2269–2288, December 2020.
[23]T. S. Gnanasekar and D. Samiappan, “Optimal routing in VANET using improved meta‐heuristic approach: a variant of Jaya,” IET Commun., vol. 14, no. 16, pp. 2740–2748, October 2020.
[24]M. A. Gawas and S. S. Govekar, “A novel selective cross layer based routing scheme using ACO method for vehicular networks,” Journal of Network and Computer Applications, vol. 143, pp. 34–46, October 2019.
[25]C. Jose and K. S. V. Grace, “Optimization based routing model for the dynamic path planning of emergency vehicles,” Evolutionary Intelligence, July 2020.