Performance Analysis of UAV Relay in Vanets against Smart Jamming with Q-Learning Techniques

As the common means of field emergency communication, the unmanned aerial vehicle (UAV) relay plays an important role in both civil and military fields. With the diversification of requirements and the limitations of single operation, multi-UAV cooperative work has attracted extensive attention. However, due to the UAV signal’s universality of broadcast and the limitation of power, information transmission is more likely to be interrupted. Therefore, in the presence of interference, this paper adopts forward amplification protocol under UAV relay transmission model. And two beamforming methods are proposed based on minimizing transmission power and maximizing communication rate of UAV relays. Then, we establish the power-constrained maximum security rate optimization problem with UAV relays, and deduce the corresponding beamforming weight vector and the optimal rate of safety. Finally, the simulation results show the effectiveness and accuracy of the proposed beamforming method.

Vehicular ad-hoc network (VANET) technology enables communication between vehicles, or vehicles and road-side units (RSUs) through wireless communication devices installed on the vehicles. One of the most important goals of VANETs is providing safety applications for passengers. In addition, VANETs provide comfort applications to users. Guaranteeing a reliable and stable routing protocol over VANETs is a very important step. The proposed research attempts to improve routing protocols that decrease the end-to-end delay to suit VANET communication characteristics. In addition, it proposes analysis of the end-to-end delay probability distribution. More specifically, we derive a closed-form expression for the probability distribution of the re-healing delay in a VANET conditioned on the distance between two VANET clusters. Furthermore, we propose a closed-form expression for the probability distribution of the unconditional re-healing delay. Moreover, we develop a mathematical model to calculate the probability distribution of the end-to-end delay. On the other hand, using Unmanned Aerial Vehicles (UAVs) or drones in wireless communications and Vehicular Ad-hoc Networks (VANETs) has started to attract attention. We propose a routing protocol that uses infrastructure drones for boosting VANET communications to achieve a minimum vehicle-to-drone packet delivery delay. In addition, we propose a closed-form expression for the probability distribution of the vehicle-to-drone packet delivery delay on a two-way highway. Moreover, based on that closed-form expression, we can calculate the minimum drone density (maximum separation distance between two adjacent drones) that stochastically limits the worst case of the vehicle-to-drone packet delivery delay. Furthermore, we propose a drones-active service (DAS) that is added to the location service in a VANET. This service dynamically and periodically obtains the required number of active drones based on the current highway connectivity state by obtaining the maximum distance between each two adjacent drones while satisfying a probabilistic constraint for vehicle-todrone packet delivery delay. Our analysis focuses on two-way highway VANET networks with low vehicular density. The simulation results show the accuracy of our analysis and reflect the relation between the drone density, vehicular density and speed, other VANET parameters, and the vehicle-to-drone packet delivery delay. In addition, we propose a new routing protocol called multi-copy intersection-based routing (MCIR) for vehicular ad-hoc networks (VANETs) in urban areas. MCIR is an intersectionbased routing protocol that forwards multiple copies of the packets in different road segments. Moreover, it is a beacon-less routing protocol with a carry-and-forward strategy. We show via simulation that the MCIR protocol is superior to other existing routing protocols, especially in low vehicular density scenarios. The results show that MCIR achieves a shorter end-to-end delay and a higher packet delivery ratio in urban VANET communications.

In Vehicular Ad Hoc Networks (VANETs), the Road-Side-Units (RSUs) that acts as base station for serving vehicles may experience significant traffic and congestion during peak hours due to the density of vehicles. To solve this problem, we suggest a coordinated strategy including Unmanned-Aerial-Vehicles (UAVs) on-demand use to supplement and service RSUs and other vehicles. Better resource allocation strategies are required to handle this issue because RSUs and UAVs may share the same set of channels, which will lead to interference. In this research, we propose a UAV-Assisted Protocol (UAVa) to improve network performance, especially throughput. The platform for resource allocation between UAVs and RSUs established by our suggested protocol is cooperative and well-coordinated. The criterion of channel allocation is based on the probability that a Line-of-Sight (LoS) link will exist between the vehicle and the UAV/RSU. The UAV serves the car if the LoS probability is high; otherwise, the RSU offers the service. Signals are successfully decoded to reduce interference if their Signal-to-Interference-plus-Noise-Ratio (SINR) is higher than a set SINR threshold. In contrast, other signals are subject to a back-off timer. We show through simulations that including UAVs is more effective and provides a reliable communication system in VANETs. By implementing a probabilistic access mechanism, our coordinated vehicular network efficiently lessens the stress on RSUs while on the load to UAVs, especially in locations with high traffic density. The LoS connectivity and optimized channel allocation offer better quality of service through RSU and UAVs in this cooperative environment.

The use of multiple heterogeneous, low-cost, small Unmanned Aerial Vehicles (UAVs) as a tool in several application domains is becoming increasingly important. One critical aspect to enable the use of such vehicles is the coordination/planning system, whose task complexity increases with the number of vehicles and the communications constraints that arise due to their small size and large distances. In this work, we propose a control architecture for a platoon of relay UAVs that are independent of the coordination system. The platoon task consists in interconnecting the communication link between the possibly mobile command station and a UAV in a mission. The relays are actively driven to deploy, create a network and maintain a desired Quality-of-Service (QoS) level, defined in this paper. We present an architecture that is composed by a waypoint generator based on the network QoS and a Time Coordinated Path Following (TCPF) controller with a method to reduce the frequency of information exchange between the relay UAVs, through the use of a self-triggered control strategy. Exploiting this architecture, it is possible to plan a mission operation for a UAV without the need of considering vehicle-to-command-station communication constraints that will be satisfied by the introduction of the relay-UAVs platoon. Simulation results are provided to illustrate the efficacy of the developed strategy. The self-triggered approach results in significant reduction of information exchange between the relay UAVs, while maintaining the user desired network QoS.

The use of small and miniature unmanned aerial vehicles (UAVs) for remote sensing and surveillance applications has become increasingly popular in the last two decades. The intermittent connectivity in a sparse UAV network makes it challenging to efficiently gather sensed image data. This paper investigates the communication of sensed images from a set of mobile survey UAVs to a static base station through the assistance of a relay UAV. Given the planned routes of survey UAVs, a set of relay waypoints are found for the relay UAV to meet the survey UAVs and receive the sensed images. An Online Message Relaying technique (OMR) is proposed to schedule the relay UAV to collect images. Without any global collaboration between the relay UAV and the survey UAVs, OMR utilizes a markov decision process (MDP) that determines the best schedules for the relay UAV such that the image acquisition rate could be maximized. Evaluation results show that the proposed relaying technique outperforms traditional relaying techniques, such as the traveling salesman problem (TSP) and the random walk, in terms of end-to-end delay and frame delivery ratio.

This paper investigated the multiple unmanned aerial vehicle (UAV) relays' assisted network in the Internet of Things (IoT) systems enhanced with energy harvesting in order to overcome the large-scale fading between source and sink as well as achieve the green cooperative communications, where time switch (TS) and power splitting (PS) strategies were typically applied for UAV relays to implement energy harvesting transmission, which was also selected via signal to noise ratio (SNR) maximization criterion so that the terminal node can obtain the optimal signal. Meanwhile, it was worth noting that the terminal node may be disturbed by aggregated interference caused by dense network signaling interaction in the future 5G/B5G systems. Therefore, after TS and PS protocols designing and utilizing, the closed-form expressions of outage probability and bit error rate (BER) for UAV relay assisted IoT systems suffered from aggregated interference were derived in detail. In addition, the throughput and delay limited state of UAV relay assisted transmission were also analyzed thoroughly. The derivations and analysis results showed that the proposed multi-parameter joint optimization of transmitting power, scaling factor, and UAV relay selection could effectively improve the system throughput and reduce the system outage probability and BER. The simulation experiments verified the effectiveness of the proposed schemes and the correctness of theoretical analysis.

Path-optimization method for UAV-aided relay broadcast communication system

Nowadays, the research on vehicular ad hoc networks (VANETs) remains a hot topic within the Internet of Things (IoT) scenarios. Diverse studies and techniques regarding all aspects of VANETs have been investigated thoroughly. Particularly, the wireless characteristic of heterogeneous vehicular communication, along with the complicated and dynamic connection topology among participating VANET entities, have severely affected the secure and stable data exchange. Specifically, the spontaneous vehicle-to-vehicle (V2V) message dissemination, as the essential functionality of VANET, plays a significant role for instant and real-time data sharing for vehicles within a certain vicinity. However, with the short-time interaction and high mobilization of vehicular connections, the remote V2V message delivery intended for long-distance vehicles in the range of different roadside units (RSUs) has not been properly researched. Meanwhile, both V2V and V2R (Vehicle-to-RSU) communication are highly restricted by environmental factors such as physical obstructions or signal interferences, thus drastically reducing the wireless connectivity in practical VANET implementations. In this case, the unmanned aerial vehicles (UAVs), as the auxiliary facilities, can provide the VANET with substitute wireless routes, so that the transmission quality and availability can be improved. In this paper, the authenticated UAV group association design is proposed at first. On this basis, the remote V2V message dissemination method is enabled, where the decentralized V2V connections involving all RSUs along the way are provided. The analysis regarding crucial security properties is presented accordingly, where the formal proofs and comparison are conducted. Moreover, the performance evaluation in terms of storage and time consumption during RSU authentication process is illustrated, respectively. Comparison results with the state-of-the-art prove that superiority on the major performance factors can be achieved.

In this paper, we consider a hybrid satellite-terrestrial network (HSTN) where a multiantenna satellite communicates with a ground user equipment (UE) with the help of multiple cache-enabled amplify-and-forward (AF) three-dimensional ($3$D) mobile unmanned aerial vehicle (UAV) relays. Herein, we employ the two fundamental most popular content (MPC) and uniform content (UC) caching schemes for two types of mobile UAV relays, namely fully $3$D and fixed height. Taking into account the multiantenna satellite links and the random $3$D distances between UAV relays and UE, we analyze the outage probability (OP) of considered system with MPC and UC caching schemes. We further carry out the corresponding asymptotic OP analysis to present the insights on achievable performance gains of two schemes for both types of $3$D mobile UAV relaying. Specifically, we show the following: (a) MPC caching dominates the UC and no caching schemes; (b) fully $3$D mobile UAV relaying outperforms its fixed height counterpart. We finally corroborate the theoretic analysis by simulations.

Vehicular ad hoc networks (VANETs) have recently received considerable attentions for both safety and commercial applications. In this paper, we address popular content distribution (PCD) in VANETs, in which a popular file is downloaded by a group of on-board units (OBUs) passing by a roadside unit (RSU). Due to high speeds and channel fadings, the OBUs may not finish downloading the entire file from the RSU, but only possess several content pieces. In this paper, we propose a cooperative approach using coalitional graph game to establish a peer-to-peer (P2P) network among the OBUs, in which content pieces are exchanged to complete the file downloading process. Further, we adopt cognitive radio (CR) technique for implementing vehicle-to-vehicle (V2V) transmissions. Simulation results show that the proposed algorithm enjoys a better performance in various conditions, compared to the non-cooperative approach, where OBUs randomly response to content requests from other OBUs.

Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach

Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey

The vehicular ad-hoc network (VANET) is a promising technology in near future. VANET allows vehicles to communicate with each other and with roadside units (RSUs). Wireless Access in Vehicular Environment (WAVE), defined by IEEE, provides the Internet connectivity for vehicles as well as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. RSUs can form a wireless mesh network (WMN) as a backbone infrastructure to enhance the performance of VANET. However, WAVE supports only one-hop communications for IP packets in VANET, and existing researches do not consider the compatibility between VANET and WMN infrastructure formed by RSUs. We propose the extended WMN (ExWMN) which has the feasibility to extend WMN to VANET domain. In ExWMN, the vehicles can send IP packets to an access router by using the multi-hop capability of WMN. ExWMN uses the addressing scheme of WMN for the extended VANET domain and exploits geographical routing protocols instead of existing routing protocols of WMN due to VANET characteristics such as mobility and dynamically changing network topologies. ExWMN supports the compatibility between the extended VANET and a WMN infrastructure due to RSUs, which provide a bridging function between them. This paper presents the architecture and the detailed procedures of the ExWMN.

Unmanned aerial vehicles (UAVs) play an essential role in emergency cases and adverse environments for applications like disaster detection and mine exploration. To process the massive volume of sensing data generated by various sensory payloads in these applications, existing works either compress deep learning (DL) models to conduct onboard computing, or offload raw data back to the resourceful ground station with the help of relay UAVs due to base station damage. However, the former sacrifices the inference accuracy of DL models (up to 10% accuracy loss), while the latter achieves high accuracy at the cost of significant latency, due to limited wireless communication resources in the multi-hop transmission. To address the problem, exploiting the resources of the UAV swarm including both task UAVs and relay UAVs, we build up an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>ll-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>ky <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>utonomous com<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u>uting (ASAP) system to autonomously conduct collaborative computing in the swarm, to achieve both high accuracy and low latency of sensing data processing. In detail, we first propose a novel UAV swarm-native collaborative computing architecture, considering the general hierarchy and clustering structure of UAV swarms, as well as the characteristic of DL model execution. We then design an elastic efficient task scheduler to allocate computing tasks for UAVs, and update the scheduling scheme online when some UAVs are unavailable, with the aid of a lightweight and accurate DL inference performance predictor. Finally, we design an adaptive inter-UAV data compressor, to adapt to the limited and dynamic communication resources between UAVs. Experiment results on 24 airborne computers and five real-world UAVs show that, the proposed system can perform collaborative computing in a timely manner and effectively deal with situations when some UAVs become unavailable.