Smart City Gnosys

Smart city article details

Title	Gratree: A Gradient Boosting Decision Tree Based Multimetric Routing Protocol For Vehicular Ad Hoc Networks
ID_Doc	28309
Authors	Cárdenas L.L.; León J.P.A.; Mezher A.M.
Year	2022
Published	Ad Hoc Networks, 137
DOI	http://dx.doi.org/10.1016/j.adhoc.2022.102995
Abstract	The Intelligent Transport System (ITS) is increasingly becoming a reality thanks to the incorporation of modern vehicles that, together with artificial intelligence techniques, can bring novel solutions to the smart city. Today, vehicles are embedded with different types of wireless devices that allow them to communicate with a wide range of telecommunication infrastructures located on streets, avenues, or roads. In this context, valuable data related to traffic management is transmitted and can be used for different purposes, such as assisting other vehicles on the road or improving the decisions made by traffic engineers. Vehicular Ad hoc NETworks (VANETs) enable wireless communications between vehicles, between vehicles and infrastructure, and also vehicles-to-everything, with the aim of generating smart mobility solutions and contributing to improve city services. However, routing information in VANETs is a challenging task, as vehicles are highly mobile, and their trajectories are uncertain and highly constrained by the city's road map. In this regard, as the application of machine learning (ML) algorithms in wireless networks is increasing, the performance of vehicular networks can be improved by applying prediction-based techniques. In this work, we propose a multimetric ML-based routing protocol to select the best next-hop node for forwarding warning messages. For this purpose, we have considered the CatBoost framework, a gradient boosting algorithm on decision trees. Furthermore, we studied the importance of each routing metric and selected only the most relevant ones in our routing decisions. The performance evaluation shows the significant improvements obtained from our ML-based approach in terms of packet losses. © 2022 Elsevier B.V.
Author Keywords	Feature importance; Machine learning; Multimetric routing protocol; Vehicular networks

Similar Articles

Id	Similarity	Authors	Title	Published
21132	0.928	Cárdenas L.L.; Mezher A.M.; León J.P.A.; Igartua M.A.	Dtmr: A Decision Tree-Based Multimetric Routing Protocol For Vehicular Ad Hoc Networks	PE-WASUN 2021 - Proceedings of the 18th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (2021)
40685	0.874	Peyman M.; Fluechter T.; Panadero J.; Serrat C.; Xhafa F.; Juan A.A.	Optimization Of Vehicular Networks In Smart Cities: From Agile Optimization To Learnheuristics And Simheuristics	Sensors, 23, 1 (2023)
5762	0.873	Prakash J.; Murali L.; Manikandan N.; Nagaprasad N.; Ramaswamy K.	A Vehicular Network Based Intelligent Transport System For Smart Cities Using Machine Learning Algorithms	Scientific Reports, 14, 1 (2024)
784	0.872	Sepasgozar S.S.; Pierre S.	A Comparative Study Of Artificial Intelligence Algorithms For Network Traffic Prediction In Vanet	International Conference on Wireless and Mobile Computing, Networking and Communications, 2022-October (2022)
40735	0.868	Saravanan M.; Devipriya R.; Sakthivel K.; Sujith J.G.; Saminathan A.; Vijesh S.	Optimized Load Balancing And Routing Using Machine Learning Approach In Intelligent Transportation Systems: A Survey	Lecture Notes in Networks and Systems, 647 LNNS (2023)
44899	0.866	Bugarčić P.; Jevtić N.; Malnar M.	Reinforcement Learning-Based Routing Protocols In Vehicular And Flying Ad Hoc Networks – A Literature Survey; [Protokoli Rutiranja Bazirani Na Učenju Potkrepljivanjem Za Bežične Ad Hoc Mreže Za Vozila I Bespilotne Letelice – Pregled Literature]	Promet - Traffic and Transportation, 34, 6 (2022)
47100	0.866	Sohail M.; Latif Z.; Javed S.; Biswas S.; Ajmal S.; Iqbal U.; Raza M.; Khan A.U.	Routing Protocols In Vehicular Adhoc Networks (Vanets): A Comprehensive Survey	Internet of Things (Netherlands), 23 (2023)
44548	0.865	Bangui H.; Buhnova B.	Recent Advances In Machine-Learning Driven Intrusion Detection In Transportation: Survey	Procedia Computer Science, 184 (2021)
1940	0.864	Mehdi B.; Moussaoui S.; Mohamed G.	A Geographic Routing Based On Local Traffic Density And Multi-Hop Intersections In Vanets For Intelligent Traffic System In Smart Cities (Grbltd-Mi)	Wireless Networks, 30, 6 (2024)
12032	0.863	Aljehane N.O.; Mansour R.F.	Big Data Analytics With Oppositional Moth Flame Optimization Based Vehicular Routing Protocol For Future Smart Cities	Expert Systems, 39, 5 (2022)