In the past decades, the unmanned aerial systems have been utilized only for military operations. However, recently, the potential uses and applicability of unmanned aerial vehicles (commonly known as drones) in civilian application domains are becoming a fast-growing phenomenon. A flying ad hoc network is a wireless ad hoc network specifically designed for the communication of unmanned aerial vehicles. Multicast routing is one of the vital aspects in wireless ad hoc networks. Using multicast transmission approaches, flying ad hoc network applications may need to send the same message to a specific group of flying nodes. The multicast communication approaches can benefit flying ad hoc network applications in conserving the scarce resources of flying nodes. Research works have been proposed to tackle the challenges in multicast routing with multi-hop communication in ad hoc network environments. Nevertheless, the conventional multicast routing mechanisms incur excessive control message overhead when a large number of nodes experience frequent topological changes. A scalable geographic multicast routing mechanism, which specially require localized operation and reduced control packet overhead, is necessary. Multicast routing in flying ad hoc networks is extremely challenging because of the dynamic topology changes and network disconnection resulted from frequent mobility of nodes. In this article, we present and implement a scalable and predictive geographic multicast routing mechanism in flying ad hoc networks. In uniform and random deployment scenarios, the MATLAB-based evaluation result has revealed that when the communication range increases, the probability of finding one-hop predicted forwarders to reach multicast destinations also increases. The implementation of scalable and predictive geographic multicast routing mechanism in flying ad hoc network is done using Optimizing Network Engineering Tools Modeler 16.0. We have added the scalable and predictive geographic multicast routing mechanism in flying ad hoc network as a new routing scheme in the Mobile Ad hoc Network routing protocol groups of the Optimizing Network Engineering Tools Modeler. Then, the performance of scalable and predictive geographic multicast routing mechanism in flying ad hoc network is compared with two of the existing Mobile Ad hoc Network routing protocols (Geographic Routing Protocol and Dynamic Source Routing). Eventually, we present two instance scenarios regarding the integration of scalable and predictive geographic multicast routing mechanism in flying ad hoc network scheme in the Internet of Things platform.

Unmanned Aerial Vehicles (UAVs or commonly known as drones) have the potential benefits in improving communications in wireless network environment. Besides their utilization in military operations, the potential use and applicability of UAVs in civilian applications is becoming a fast growing phenomenon. Routing is one of the vital aspects while conducting wireless communication among UAVs. It is extremely challenging because of dynamic topology changes resulted from frequent mobility of UAVs. In this paper, we analyse the performance of different Mobile Ad-hoc Network (MANET) routing protocols for the communication of UAVs. Using Riverbed (OPNET) Modeler, we evaluate the performance of four MANET routing protocols (AODV, DSR, GRP and OLSR) for UAV communications based on scenarios of various data rates supported by IEEE 802.11p (WAVE) standard.

As personal mobility devices (PMDs) have been widely adopted, the accident rate also increased. For safe driving, the speed of the PMDs should be controlled by recognizing the condition of the road surface. In recent advances in artificial intelligence, various studies have been conducted to recognize the type of material based on radar sensors and machine learning. However, there are very limited studies addressing radar-based material recognition in noisy radar signal environments like PMDs. In this paper, we proposed a road surface classification scheme based on the 60 GHz pulsed radar, which has low cost and low power usage to be applied to various PMDs. We also developed a lightweight deep neural network model to recognize the type of road surface from the radar data after removing unnecessary features using principal component analysis (PCA). The proposed mechanism is robust against ambient noise and can recognize surface types on roads of variable shapes with high accuracy and low computational complexity. The results of experiments showed that the proposed lightweight surface classification system improved accuracy with only about 40% of the parameters used in the previous study.

The integration of Internet of Things (IoT), wireless communications and Artificial Intelligence (AI) has become essential for enhancing IoT service efficiency. Autonomous Flying Base Stations (AFBSs)—such as unmanned aerial vehicles (UAVs) equipped with wireless base stations—have emerged as next-generation technologies that provide dynamic, on-demand coverage to ground users. However, determining optimal AFBS placement remains challenging, as its position directly impacts both energy consumption and the number of connected devices. To address this, providing reliable connectivity while balancing coverage and power efficiency is critical. In this paper we integrate the IoT, wireless communication and AI approaches and propose a deep reinforcement learning (DRL)-based approach to optimize AFBS positioning for ground IoT networks. We formulate mathematical air-to-ground channel models to inform location optimization and evaluate our proposed scheme across three distinct ground IoT distribution scenarios—Gaussian, random and grid. Experimental results demonstrate that the proposed scheme effectively guides the AFBS to stable convergence and best-found 3D coordinates across the three spatial deployment topologies, while also highlighting the significant impact of IoT device distribution on optimal placement decisions.