The comparative analysis of velocity and density in VANET using prediction-based intelligent routing algorithms

UAV assisted communication for ground users using machine learning and optimization

“…According to article [24] , the spatio-temporal trajectories are utilized to estimate the vehicle density, and the predicted data is then used to determine the optimal path for transmitting data packets to the preferred destination. As per article [25] , the density prediction is also utilized in vehicular Ad-hoc networks (VANETs) to create an intelligent routing system for sending and receiving packets and assessing the performance of communication methods. Several optimization methods are implemented to increase energy efficiency,for instance, in paper [26] and [27] the author used optimization techniques and an iterative process to solve an energy efficiency maximization problem.…”

“…Reference number Scenario Main method [16] Space Air Ground Integrated Networks (SAGINs) Artificial Intelligence (AI) [17] Low altitude platforms (LAPs) Hybrid satellite-aerial terrestrial (HSAT) networks [19] Unmanned aerial vehicle (UAV) networks Swarm optimization [20] Unmanned aerial vehicle (UAV) networks Monotonic optimization [21] Multicarrier (MC) systems Suboptimal algorithm [22] Intelligent Transportation System (ITS) Topological Graph Convolutional Network (ToGCN) [23] Radio access network (RAN) Proactive drone cell deployment framework [24] Internet of Things (IoTs) Collaborative communication scheme [25] Vehicular ad hoc networks (VANETs) Intelligent prediction based routing algorithms [26] unmanned aerial vehicle (UAV) networks Energy efficiency (EE) maximization scheme [27] UAV enabled wireless communications Efficient iterative algorithm [28] Unmanned aerial vehicle (UAV) networks Block coordinate descent method per [10] , and [11] . Each location data point that falls within the chosen area 𝑅 has the vehicle ID 𝑉 𝑖𝑑 labeled on it.…”