A Novel Collaborative IoD-Assisted VANET Approach for Coverage Area Maximization

Abstract

Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular adhoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes.

3D Simulation Model for IoD-to-Vehicles Communication in IoD-assisted VANET

“…Results are compared with a 2D model where only the free space propagation model is considered. We also compare our results with those of IoDAV [33] to demonstrate the necessity of the proposed 3D propagation Considering the goal of the proposed approach to produce realistic results and improve VANET connectivity, mean coverage, and mean RSSI are the adopted performance metrics in the simulation. Without loss of generality, the coverage is normalized to unity.…”

“…Based on the RSSI value, the number of covered vehicles is calculated, where a vehicle is considered covered if its received signal is greater than the minimum powerlevel threshold. The optimization algorithm used in this study is thoroughly explained in our previous work [33], and also illustrated in Fig. 5.…”

“…In [33], a novel IoD collaborative communication approach was proposed to improve communication among VANETs. UAVs were optimally deployed to maximize the coverage area and improve the quality of the received signal.…”

“…This subsection describes how to optimize IoD deployment in 3D space as well as the employed optimization approach. In our previous study [33], the dynamic deployment of the IoD network is considered where IoD nodes dynamically change their positions based on the locations of ground vehicles. The aim is to maximize the coverage area and enhance the quality of the received signal on the receiver's end.…”

“…The IoD network is deployed to support the wireless connectivity of ground vehicles. The coverage is obtained as a percentage of connected vehicles to total vehicles on the ground and can be obtained using Vcon/Vt (proposed in [33]), where Vcon is the number of connected vehicles, and Vt is the total number of ground vehicles.…”