A Dual-Mode Medium Access Control Mechanism for UAV-Enabled Intelligent Transportation System

With the exponential growth in technologies for the vehicular Internet of things applications and high demands for autonomous road vehicles, future transportation systems are projected to be revolutionized on a global scale. This new landscape requires a stable, flexible, and business-friendly base of connectivity, networking, and computing technology, in which Unmanned Aerial Vehicles (UAVs) can play an important role. A UAV-enabled Intelligent Transportation System (ITS) can provide a cost-effective communication solution to improve the safety and efficiency of the transportation system, particularly if the data traffic is nonhomogeneous and nonstationary. Typically, wireless is the communication medium between vehicles and UAVs in an ITS setting, which is based on the IEEE802.11p MAC protocol adopted by car manufactures. However, the IEEE 802.11p MAC protocol is modified solely for omnidirectional antennas, which restricts network coverage, delay, and throughput. In comparison, the directional antenna has greater network coverage, spatial reuse, and bandwidth. In addition, a multiaccess edge computing (MEC) facility at the backhaul link will provide ultralow latency and high bandwidth services to meet the increasingly growing demand for latency-sensitive vehicle applications such as vehicular video data analytics, autonomous driving, and intelligent navigation. Therefore, this article aims to propose a novel dual-mode MAC protocol that can work in two antenna modes, i.e., directional and omnidirectional. For modeling and simulation purposes, we use the Optimized Network Engineering Tool (OPNET) and aim to seek an evaluation with respect to throughput, media access delay, and retransmission attempts. The results obtained demonstrate the effectiveness of the proposed scheme

RSSI-Controlled Long-Range Communication in Secured IoT-Enabled Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) has recently gained significant attention due to their efficient structures, cost-effectiveness, easy availability, and tendency to form an ad hoc wireless mobile network. IoT-enabled UAV is a new research domain that uses location tracking with the advancement of aerial technology. In this context, the importance of 3D aerial networks is attracting a lot of attention recently. It has various applications related to information processing, communication, and location-based services. Location identification of wireless nodes is a challenging job and of extreme importance. In this study, we introduced a novel technique for finding indoor and open-air three-dimensional (3D) areas of nodes by measuring the signal strength. The mathematical formulation is based on a path loss model and decision tree machine learning classifier. We constructed 2D and 3D models to gather more accurate information on the nodes. Simulation findings demonstrate that the proposed machine learning-based model excels in nodes location estimation, the actual and estimated distance of different nodes, and calculation of received signal strength in aerial ad hoc networks. In addition, the decision tree constructs an offline phase control in the flying vehicle’s location to enhance the time complexity along with experimental accuracy

Application of Differential Geometry to the Array Manifolds of Linear Arrays in Antenna Array Processing

This article deals with the application of differential geometry to the array manifolds of non-uniform linear antenna array (NULA) when estimating the direction of arrival (DOA) of multiple sources present in an environment using far field approximation. In order to resolve this issue, we utilized a doublet linear antenna array (DLA) comprising two individual NULAs, along with a proposed algorithm that chooses correct directions of the impinging sources with the help of the prior knowledge of the ambiguous directions calculated with the application of differential geometry to the manifold curves of each NULA. The algorithm checks the correlation of the estimated direction of arrival (DOAs) by both the individual NULA with its corresponding ambiguous set of directions and chooses the output of the NULA, which has a minimum correlation between their estimated DOAs and corresponding ambiguous DOAs. DLA is designed such that the intersection of all the ambiguous set of DOAs among the individual NULAs are null sets. DOA of sources, which imping signals from different directions on the DLA, are estimated using three direction finding (DF) techniques, such as, genetic algorithm (GA), pattern search (PS), and a hybrid technique that utilizes both GA and PS at the same time. As compared to the existing techniques of ambiguity resolution, the proposed algorithm improves the estimation accuracy. Simulation results for all the three DF techniques utilizing the DLA along with the proposed algorithm are presented using MATLAB. As compared to the genetic algorithm and pattern search, the intelligent hybrid technique, such that, GA–PS, had better estimation accuracy in choosing corrected DOAs, despite the fact that the impinging DOAs were from ambiguous directions