Drone Communication Research Articles

Secured Drone Communication Based on Esalsa20 Algorithm

The Unmanned Aerial Vehicle (UAV) (sometimes known as a ""drone"") is used in a variety of fields. Unfortunately, as they become more popular and in demand, they become more vulnerable to a variety of security threats. To combat such attacks and security threats, a proper design of a robust security and authentication system based on and stream cipher lightweight salsa20 algorithm with chaotic maps is required. By using a proposed key generation method which is based on a 1d Logistic chaotic map to produce a flight session key for a drone with a flight plan, and then records the flight session key and the drone’s flight plan in a central database that can be accessed. Finally, while the drone is flying, a GCS checks authentication of the current flight session based on the on flight session key and its flight plan as the message authentication code key to authenticate the drone by any flight session, and the drone after which uses salsa20 lightweight to cipher payload data to improve security Network Transfer of RTCM Messages over Internet Protocol Protocol (NTRIP) communication protocol and send it to GCS, and at last, a GCS verifies authentication of the current flight session based on the on flight session key and its flight plan as the message authentication code key to authenticate the drone. The proposed system is superior to other similar systems in terms of security and performance, according to the review.

Enhancing Perceptual Experience of Video Quality in Drone Communications by Using VPN Bonding

Recently, drones are increasingly being used as innovative IoT sensors. The application contexts of UAV systems where there is often a need to transmit time-critical data between the drone and the Internet are increasingly numerous and varied. Keeping this in mind and assuming that most professional UAVs are equipped with multiinterface routers and use virtual private networks (VPNs) to ensure reliable, robust, and secure transmission to the operation room, this letter presents a technique to enhance real-time and time-critical communications such as real-time video streaming. An embedded system, Raspberry-based gateway prototype, was used to create a testbed using a professional drone that captured and transmitted real-time video during a 30-min flight over a rural area characterized by poor mobile radio coverage. A perceptual video quality assessment tool, video multimethod assessment fusion (VMAF), has been employed to evaluate the improvement in terms of video quality perceived by the operator.

A Simple Analytical Model for THz Band Path Loss

In this letter, we devise a frequency, distance, and altitude-dependent, analytical model for THz band path loss. This model is obtained from cascaded regression of transmittance data from Line-by-Line Radiative Transfer Model (LBLRTM) propagation analysis tool, considering altitudes from the sea-level to 1 km, distances from 1 m to 100 m and frequencies within 0.79-0.91 THz and 0.93-0.94 THz. The proposed approach yields high accuracy with respect to propagation data while providing a simple, closed-form expression to help the design of Terahertz (THz) band drone communications.

Secured drone communication based on Esalsa20 algorithm and 1d logistic map

<span lang="EN-US">Over recent years, drones are increasingly being used not only for military tasks only, but also for civilian tasks too, such as environment and traffic monitoring, delivery services, and aerial surveys. Unfortunately, as they become more popular and in demand, they become more vulnerable to a variety of security threats. To combat such attacks and security threats, a proper design of a robust security and authentication system based on and stream cipher lightweight salsa20 algorithm with chaotic maps to cipher payload data to improve security network transfer of radio technical commission for maritime services (RTCM) messages over (NTRIP) communication protocol. By using a proposed key generation method which is based on a 1d logistic map to produce a flight session key for a drone with a flight plan, and then records the flight session key and the drone's flight plan in a central database that can be accessed. The proposed system is superior to other similar systems in terms of security and performance, according to the review.</span>

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

Joint Spectrum and Computing Resource Allocation in Fog-Assisted Drone Communications for Ambiental Services

Joint Spectrum and Computing Resource Allocation in Fog-Assisted Drone Communications for Ambiental Services

3D printed stacked antenna for WLAN drone communication

3D printed stacked antenna for WLAN drone communication

Reliability estimation for drone communications by using an MLP-based model

Unmanned aerial vehicles (UAVs) or drones have been widely employed in both military and civilian tasks due to their reliability and low cost. UAVs ad hoc networks also acknowledged as flying ad-hoc networks (FANETs), are multi-UAV systems arranged in an ad hoc manner. In order to maintain consistent and effective communication, reliability is a prime concern in FANETs. This paper presents an analytical framework to estimate the reliability of drones’ communication in FANETs. The proposed system takes into account the reliability of communications in FANETs, including channel fading. The suggested analytical investigation is used to generate a dataset, then an artificial neural network (ANN) based multi-layer perceptron (MLP) model is used to estimate the reliability of drones’ communication. Moreover, to define the best MLP model with hidden layers, the correlation coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are obtained. Moreover, numerical results are presented which verify analytical studies.

Joint Power Control and Phase Shift Design for Future PD-NOMA IRS-Assisted Drone Communications under Imperfect SIC Decoding.

Intelligent reflecting surfaces (IRS) and power-domain non-orthogonal multiple access (PD-NOMA) have recently gained significant attention for enhancing the performance of next-generation wireless communications networks. More specifically, IRS can smartly reconfigure the incident signal of the source towards the destination node, extending the wireless coverage and improving the channel capacity without consuming additional energy. On the other side, PD-NOMA can enhance the number of devices in the network without using extra spectrum resources. This paper proposes a new optimization framework for IRS-enhanced NOMA communications where multiple drones transmit data to the ground Internet of Things (IoT) devices under successive interference cancellation errors. In particular, the power budget of each drone, PD-NOMA power allocation of IoT devices, and the phase shift matrix of IRS are simultaneously optimized to enhance the total spectral efficiency of the system. Given the system model and optimization setup, the formulated problem is coupled with three variables, making it very complex and non-convex. Thus, this work first transforms and decouples the problem into subproblems and then obtains the efficient solution in two steps. In the first step, the closed-form solutions for the power budget and PD-NOMA power allocation subproblem at each drone are obtained through Karush-Kuhn-Tucker (KKT) conditions. In the second step, the subproblem of efficient phase shift design for each IRS is solved using successive convex approximation and DC programming. Numerical results demonstrate the performance of the proposed optimization scheme in comparison to the benchmark schemes.

An Intrusion Detection Model for Drone Communication Network in SDN Environment

Drone communication is currently a hot topic of research, and the use of drones can easily set up communication networks in areas with complex terrain or areas subject to disasters and has broad application prospects. One of the many challenges currently facing drone communication is the communication security issue. Drone communication networks generally use software defined network (SDN) architectures, and SDN controllers can provide reliable data forwarding control for drone communication networks, but they are also highly susceptible to attacks and pose serious security threats to drone networks. In order to solve the security problem, this paper proposes an intrusion detection model that can reach the convergence state quickly. The model consists of a deep auto-encoder (DAE), a convolutional neural network (CNN), and an attention mechanism. DAE is used to reduce the original data dimensionality and improve the training efficiency, CNN is used to extract the data features, the attention mechanism is used to enhance the important features of the data, and finally the traffic is detected and classified. We conduct tests using the InSDN dataset, which is collected from an SDN environment and is able to verify the effectiveness of the model on SDN traffic. The experiments utilize the Tensorflow framework to build a deep learning model structure, which is run on the Jupyter Notebook platform in the Anaconda environment. Compared with the CNN model, the LSTM model, and the CNN+LSTM hybrid model, the accuracy of this model in binary classification experiments is 99.7%, which is about 0.6% higher than other comparison models. The accuracy of the model in the multiclassification experiment is 95.5%, which is about 3% higher than other comparison models. Additionally, it only needs 20 to 30 iterations to converge, which is only one-third of other models. The experiment proves that the model has fast convergence speed and high precision and is an effective detection method.

Oppositional poor and rich optimization with deep learning enabled secure internet of drone communication system

As a result of technological advancements and the need for continuous reduction in manufacturing costs, the concept of Internet of Things (IoT), consisting of Unmanned Aerial Vehicles (UAVs), has entered the industrial production units. IoT devices have not only penetrated the day-to-day activities of human beings, but also in defence. In recent years, there is a widespread application of Internet of Drones (IoD) in areas such as television and film shooting, meteorological monitoring, forest fire detection, agricultural monitoring, emergency rescue, etc. In this background, Intrusion Detection System (IDS) plays an important role to effectually secure the IoD network. The current research work develops an Opposition Poor and Rich Optimization-based Feature Selection with Optimal Deep Feed-forward Neural Network (OPRFS-ODFNN) model for intrusion detection in IoD communication system. The aim of the presented OPRFS-ODFNN technique is to accomplish enhanced security in IoD communication system. In order to achieve the objective, OPRFS-ODFNN model initially executes feature scaling as a pre-processing step. Then, OPRFS technique is applied for effective selection of the features. Moreover, Improved Mayfly Optimization (IMFO) is applied with ODFNN model for intrusion detection and classification processes. In order to validate the enhanced performance of the proposed OPRFS-ODFNN method, extensive simulations were conducted.

CSECMAS: An Efficient and Secure Certificate Signing Based Elliptic Curve Multiple Authentication Scheme for Drone Communication Networks

With the rapid development of communication technology, unmanned aerial vehicles (UAVs) have been widely used in the military, rescue, agricultural and other fields. The security and privacy issues are one of the biggest challenges for UAV communications. In this paper, we propose a certificate signing based on an Elliptic Curve multiple authentication scheme (CSECMAS). The scheme employs multiple authentication tools to ensure the legitimacy of the identity and the integrity of the information. We treat multiple UAVs as a cluster, and after establishing a secure session between the ground control station (GCS) and the UAV, we use the fitness function to select one node from them as the key node. Mutual authentication between the UAVs through the key node establishes secure conversations while increasing the security and resistance to destruction of the entire communication network. The proposed protocol is formally analyzed and verified by using the model-checking tool SPIN. The informal analysis shows that the protocol can resist various attacks, such as man-in-the-middle attacks, replay attacks, and DoS attacks. The performance analysis shows that the proposed protocol is superior compared to other related protocols.

Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint

We propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets. It consists of performing traffic analysis of communication link between the drone and its controller. Conversely to classic drone traffic analysis working at data link layer (or at upper layers), it performs traffic analysis directly from the corresponding I/Q signal, i.e., at the physical layer. The approach shows interesting properties such as scale invariance, frequency invariance, and noise robustness. Furthermore, the classification method allows us to distinguish WiFi drones from other WiFi devices due to underlying requirement of drone communications such as good reactivity in control. Finally, we propose different experiments to highlight theses properties and performances. The physical-layer protocol statistical fingerprint exploiting communication specificities could also be used in addition of RF fingerprinting method to perform authentication of devices at the physical-layer.

Multifunctional Drone-Based Antenna for Satellite Communication

This communication presents a drone-to-satellite beam-forming antenna with multifunctional purposes. A glass material with a gradient coating configuration is exploited to realize a transparent structure. Beam tilting is obtained by controlling the phase distribution of an antenna by means of an inexpensive and passive surface. Simultaneously, solar cells are implemented on the proposed structure for solar energy harvesting using the transparency feature of the surface. The sputtering physical vapor deposition process is used to layer a thin coat of indium tin oxide (ITO) onto the glass. A conceptual beam tilting with a multipurpose design is introduced for drone2sat communication and energy harvest. The proposed communication structure works at a frequency of 3 GHz and covers a 60° spatial beamwidth. The directivity and propagation characteristics are preserved by this configuration. To validate the design performance, a prototype has been fabricated and a satisfactory agreement between the numerical results and experimental ones is achieved. The proposed structure can enhance the multifunctional designs, which could offer advantages for real-world space drone communications.

Devising a traffic control method for unmanned aerial vehicles with the use of gNB-IOT in 5G

UAVs or drones as an alternative solution to providing high-quality Internet service in difficult terrain are environmentally friendly and do not consume electricity during the day as is the case with communication towers. But the developers of the network face difficulties in the drone communication system associated with the need to take into consideration unpredictable weather conditions and terrain, as well as the short life of the drone's batteries. Therefore, the object of this study is the process of managing UAV traffic through the use of gNB-IoT in 5G. The possibility of using a mobile UAV repeater during traffic management using radio resources (RR), radio access network (RAN), the infrastructure with broadcasting tools and dynamic connection using MU-MIMO modulation is shown. The use of these tools makes it possible to connect the drone to the wired base network from the provider and then restore the radio frequency signal and broadcast to another coverage area where this subscriber does not have network coverage, use the channel quality indicator (CQI) representation as a QoE function. Undoubtedly, traffic management is the process of obtaining information about traffic control from one endpoint to another, which confirms the reliability and management of data transmission. Meanwhile, drone traffic management can be used to reduce time delays and remove network interference by relying on Internet of Things programs that use NB-5G technology. The UAV's traffic management improvement process uses a proposed algorithm to generate dynamic flow data management to enhance traffic processing of flow control in the IoT

Receiver diversity with selection combining for drone communication around buildings at frequency 10 GHz

The communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method.

CovaDel: a blockchain-enabled secure and QoS-aware drone delivery framework for COVID-like pandemics

With increase in the use of contactless deliveries during the times such as COVID-19 pandemic, the emphasis was to minimize the human presence to reduce the spread of virus. In this regard, drones are one promising alternative to be used as delivery agents. However, security and Quality of Service (QoS) are major concerns while making use of drones for deliveries. In order to secure the drone communication system, we propose, CovaDel: a blockchain-based scheme to secure data transactions for the drone delivery use case that works in a phased manner. The proposed scheme make use of decoupled blockchain architecture to overcome the limited resources capabilities of drones to perform blockchain-based computations. Further, to ensure the QoS adherence, we also propose a QoS-aware communication approach that handles collisions and congestion on the basis of firefly algorithm’s attractiveness parameter (light intensity) received by the drones. Results obtained using a simulated environment verify the efficacy of the proposed scheme on the basis of gas consumed, transaction time, average network throughput, and delay.

Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor.

EEOMA: End-to-end oriented management architecture for 6G-enabled drone communications

EEOMA: End-to-end oriented management architecture for 6G-enabled drone communications

A Proxy Signature-Based Swarm Drone Authentication With Leader Selection in 5G Networks

Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.