Remotely-Controlled Aerial Vehicles (RCAV), popularly known as drones, have gained wide popularity in several applications from military to civilian due to the usage of sensors, actuators, and ...processors with wireless connectivity for data collection and processing. However, data security and authentication are still challenging as sensitive data is collected and shared in RCAV using an open channel, i.e., the Internet. The existing security of Internet-of-Drones (IoD) communication mainly depends on the hardness of discrete logarithms and factorization problems. However, due to Shor's algorithm, both authorized and secure transmissions are challenging in the presence of highly scalable quantum computers. To mitigate the aforementioned issues and challenges, in this article, we propose an authorized and secure communication scheme for IoD based on Ring Learning With Error(RLWE) problem on lattices, which have the potential to sustain low computation and quantum attacks. The proposed scheme supports mutual authentication and has an efficient session establishment. The evaluation results show that the proposed scheme has superior performance in comparison to the existing state-of-the-art solutions on benchmark data sets using various evaluation metrics.
Internet of Drones (IoD) is the interconnection of unmanned aerial vehicles or drones deployed for collecting sensitive data to be used in critical applications. The drones transmit the collected ...data to the control room (CR) for analysis, while CR sends control commands to the drone to monitor their operations. This exchange of information between the drones and CR takes place through a wireless communication channel, which is susceptible to various security risks. Therefore, it is vital to ensure the confidentiality and integrity of such information in the IoD environment. To this end, authenticated key management (AKM) protocols can be leveraged to provide reliable and secure communication. However, due to the peculiarities associated with IoD environments, it is challenging to devise a robust and resource-efficient AKM protocol. To tackle this challenge, in this article, we propose a robust AKM protocol for IoD (RAMP-IoD). RAMP-IoD uses lightweight cryptography-based authenticated encryption primitive and elliptic-curve cryptography along with a hash function to perform the AKM process. Moreover, RAMP-IoD verifies the user's authenticity and then sets up a session key (SK) between the user and a specific drone for indecipherable communications. We verify the security of SK using the random oracle model. Scyther-based validation demonstrates that RAMP-IoD is protected against replay and man-in-the-middle attacks. Moreover, the informal analysis illustrates that RAMP-IoD is secure against various covert security attacks. Through a comparative study, we also demonstrate that RAMP-IoD provides enhanced security with low storage, communication, and computational overheads as compared to related AKM protocols.
•Designing one-of-a-kind experiment to monitor urban congestion with a swarm of drones.•Creating the most complete urban multimodal dataset, nicknamed pNEUMA, to study congestion.•Investigating ...traffic phenomena at different scales of modeling.•Developing an open science initiative with almost half a million trajectories for transportation-oriented research.
The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.
Nano-drones of the size of an insect can be used to perform stealthy surveillance or to gather intelligence crucial to attack roles at a relatively short range and within enclosed spaces and ...buildings. Conventional radar systems have been optimised to detect and classify bigger targets and are not specifically designed to detect nano-targets of less than 5 cm in size. Hence, this project aims to develop a radar system to detect and classify an insect-like size drone that corresponds to a low RCS. This will exhibit challenges due to the nature of the weak echoed signal that will be masked by an uninterested target with a stronger echoed signal. To tackle this sort of problem, micro Doppler extraction is applied for better target detection. This type of target that consists of a bladed propeller will give rise to a significant micro-Doppler signature that will contribute to the discernment of the interested target. An ad-hoc S-band FMCW radar prototype using off-the-shelf components An ad-hoc S-band FMCW radar prototype using off-the-shelf components has been successfully delivered. This prototype act as a groundwork for the next research phase of design and development for a higher frequency. Then, with the strong foundation of the S-band demonstrator, a flexible K-band FMCW radar prototype has successfully delivered aiming to meet the research purpose. The radar prototype offers a wide range of flexibility for the user to select the radar parameters (like operating frequency, ramp duration, bandwidth and integration time) and configure its performance. It will collect the signatures of real targets (nano-drone model) so that their performance can be assessed on experimental data. The results demonstrated that a nano-drone, a small size of less than 5 cm can be detected with the radar prototype developed.
Timing synchronization has a vital role in swarm drones' network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews ...which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones' network, master drones' (MD) neighbor drone's timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.
Developments in the war of the future have brought to the fore unmanned aircraft or drones used in national and/or multinational military actions, as particularly important means to achieve ...operational success. The parameters related to this modern, flexible and particularly maneuverable combat system allow the conjugation of capabilities necessary to fulfill the missions received by the operational structures during preparation and conduct of operations.
The present study first provides a general approach to drone use in military actions, taking into account aspects from previous armed conflicts, as well as from the ongoing war in Ukraine. The objective of the study points to a series of important drone purchases that will be made by our country, to increase the combat potential of the Romanian joint and tactical forces, which will act on national territory and within the North Atlantic Alliance, against any aggressive forces of an unfriendly state.
With the rapid increase of vehicles in recent years, traffic surveillance becomes a crucial issue of traffic management. Since the traditional static sensor-based surveillance system can only ...passively monitor traffic, this paper considers the usage of unmanned aerial vehicles (UAVs), which can proactively conduct traffic surveillance thanks to the excellent mobility of UAVs. Specifically, we consider the navigation problem of a network of UAVs to effectively monitor a group of ground targets which move along a curvy road. A surveillance optimization problem is stated, and a distributed navigation algorithm for the UAV network is developed. It is proved that the proposed algorithm is locally optimal. Simulations confirm the effectiveness of the proposed navigation algorithm.
Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, ...some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people's privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. However, a counter-drone system supported by an artificial intelligence (AI) method can be an efficient way to fight against drones instead of human intervention. In this paper, a deep reinforcement learning (DRL) method has been proposed to counter a drone in a 3D space by using another drone. In a 2D space it is already shown that the deep reinforcement learning method is an effective way to counter a drone. However, countering a drone in a 3D space with another drone is a very challenging task considering the time required to train and avoid obstacles at the same time. A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay is presented to catch another drone in the environment provided by an Airsim simulator. The models have been trained and tested with different scenarios to analyze the learning progress of the drone. Experiences from previous training are also transferred before starting a new training by pre-processing the previous experiences and eliminating those considered as bad experiences. The results show that the best models are obtained with transfer learning and the drone learning progress has been increased dramatically. Additionally, an algorithm which combines imitation learning and reinforcement learning is implemented to catch the target drone. In this algorithm, called deep q-learning from demonstrations (DQfD), expert demonstrations data and self-generated data by the agent are sampled and the agent continues learning without overwriting the demonstration data. The main advantage of this algorithm is to accelerate the learning process even if there is a small amount of demonstration data.
This article describes and investigates the planning aspect of military actions aimed at destroying enemy targets with the help of an attack drone swarm. This study attempts to solve the task of ...operational-tactical planning of a massive attack on enemy targets with the help of combat drones, which have different combat potential characteristics. It analyzes the problems of unmanned aerial vehicles (UAVs) swarms’ usage, which ensures maximum efficiency during combat operations. The article shows that in order to plan effective military operations, it is necessary to form the following logistical sequence: identification of relevant targets set, formation of drones into a swarm to attack targets, distribution of drones by targets, and planning flight routes of a drone swarm in conditions of military threats. It concludes that for the effective use of a combat drone swarm, it is necessary to plan logistical actions in advance to inflict maximum damage on the enemy and successfully fulfill the operational and tactical goals of the military leadership. The purpose of this study is to create information technology models that will allow planning logistical military actions for the effective use of combat drone swarms to defeat enemy targets. This article describes a systematic representation of logistical military operations for combat drone swarms. It also analyzes enemy targets, which are represented in the form of a priority list with the characteristics of relevance, the necessary combat potential to hit the targets, the risks of approaching the targets, and the flight time of the drones. From the list of targets, a sublist is formed, considering the combat potential of the drone swarm and the necessary potential to defeat the selected enemy targets. The optimization model helps to distribute the swarm of drones into groups to achieve the enemy targets and destroy them. The movement of drones is planned considering flight zones, possible anti-drone actions of the enemy, and the risks of military threats. Any Logic agent simulation platform can be used to create a simulated flight model of a drone swarm to selected enemy targets. Modeling makes it possible to form rational flight routes of a drone swarm under conditions of military threats from the enemy. An example is given to illustrate the formation of logistical actions for planning a massive attack on enemy targets with the help of a drone swarm. The scientific novelty of this study is related to the solution of the urgent problem of planning logistical military operations for the effective use of a combat drone swarm to destroy enemy targets. The results of this study should be used for the operational and tactical planning of logistical military operations to defeat enemy targets with the help of a combat drone swarm.
Optimization of routing problems using drones (unmanned aerial vehicles or UAVs) has become an important area of academic research. The purpose of this article is to look to the future and help ...stimulate drone routing research in directions we hope will prove interesting and fruitful. We discuss opportunities for better modeling of (1) drone capabilities for both existing drones and those likely to be used in the future, (2) constraints on drone performance and operations, (3) different objectives for various drone services, and (4) alternative delivery modes, as well as some areas for methodological advances and some possible new applications. While much of the research to date has leveraged existing TSP (traveling salesman problem), VRP (vehicle routing problem), and arc routing models, we look forward to new contributions from drone research that use better models of more realistic drone types and new drone applications.
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