10 research outputs found

    Secrecy performance analysis on spatial modeling of wireless communications with unmanned aerial vehicle and ground devices

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    In this paper, the secrecy performance of the spatial modeling for ground devices with randomly placed eavesdroppers when an unmanned aerial vehicle (UAV) acted as two hops decode and forward (DF) was investigated. We characterize the secrecy outage probability (SOP) and intercept probability (IP) expressions. Our capacity performance analysis is based on the Rayleigh fading distributions. After analytical results by Monte Carlo simulation, and the Gauss-Chebyshev parameter was selected to yield a close approximation, the results demonstrate the SOP with the average signal-to-noise ratio (SNR) between UAV and ground users among the eavesdroppers and the IP relationship with the ability to intercept the information of the ground users successfully

    Routing schemes in FANETs: a survey

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    Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues

    An intelligent cluster-based routing scheme in 5G flying ad hoc networks

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    Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%

    Secure UAV-to-UAV Systems With Spatially Random UAVs

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