Spatial and Temporal Analysis of Direct Communications From Static Devices to Mobile Vehicles

Abstract

This paper proposes a framework to analyze an emerging wireless architecture where vehicles collect data from devices. Using stochastic geometry, the devices are modeled by a planar Poisson point process. Independently, roads and vehicles are modeled by a Poisson line process and a Cox point process, respectively. For any given time, a vehicle is assumed to communicate with a roadside device in a disk of radius ν centered at the vehicle, which is referred to as the coverage disk. We study the proposed network by analyzing its short-term and long-term behaviors based on its space and time performance metrics, respectively. As short-term analysis, we explicitly derive the signal-to-interference ratio distribution of the typical vehicle and the area spectral efficiency of the proposed network. As longterm analysis, we derive the area fraction of the coverage disks and then compute the latency of the network by deriving the distribution of the minimum waiting time of a typical device to be covered by a disk. Leveraging these properties, we analyze various trade-off relationships and optimize the network utility. We further investigate these trade-offs using comparison with existing cellular networks.

Q-LEARNING BASED OBSTACLE AVOIDANCE DATA HARVESTING MODEL USING UAV and UGV

“…On the other hand, 2D path planning needs less effort with the probabilistic roadmaps (PRM) algorithms [5,6] from 3D path planning [7]. In some research, after the swarm UAVs collect the data, the data is immediately loaded into the cloud [8,9,10]. These swarm data harvesters use the global system for mobile communications (GSM) to load the data to the cloud.…”