Airborne Networks Research Articles

ATMChain: Blockchain-Based Security Framework for Cyber-Physics System in Air Traffic Management

The air traffic management (ATM) system is an intelligent system that integrates the ground computer network, the airborne network, and the space satellite (communication and navigation) network. It has remarkable characteristics of cyber-physical system (CPS). The development of ATM system is inseparable from the application of new technologies. This paper proposes a security framework based on blockchain for CPS in the ATM system. Through the research on the characteristics of blockchain and CPS, this paper analyzes the necessity of integrating them into the research of the ATM system, demonstrates the feasibility of combining them, and then constructs the ATMChain framework mechanism to realize the in-depth integration of blockchain and CPS in ATM. On this basis, this paper gives the overall design architecture and implementation steps of the scheme. In addition, this paper also makes a series of analysis and demonstration from the perspective of scheme security. The research scheme will help to improve the security, reliability, and scalability of ATM services and provide a new reference for establishing a more security and efficient ATM system.

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

In this paper, a novel framework is proposed to perform data-driven air-to-ground channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network. First, an effective channel estimation approach is developed to collect mmWave channel information, allowing each UAV to train a stand-alone channel model via a conditional generative adversarial network (CGAN) along each beamforming direction. Next, in order to expand the application scenarios of the trained channel model into a broader spatial-temporal domain, a cooperative framework, based on a distributed CGAN architecture, is developed, allowing each UAV to collaboratively learn the mmWave channel distribution in a fully-distributed manner. To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure. Simulation results show that the proposed distributed CGAN approach is robust to the local training error at each UAV. Meanwhile, a larger airborne network size requires more communication resources per UAV to guarantee an efficient learning rate. The results also show that, compared with a stand-alone CGAN without information sharing and two other distributed schemes, namely: A multi-discriminator CGAN and a federated-learning CGAN method, the proposed distributed CGAN approach yields a higher modeling accuracy while learning the environment, and it achieves a larger average data rate in the online performance of UAV downlink mmWave communications.

Formal Guarantees of Timely Progress for Distributed Knowledge Propagation

Autonomous air traffic management (ATM) operations for urban air mobility (UAM) will necessitate the use of distributed protocols for decentralized coordination between aircraft. As UAM operations are time-critical, it will be imperative to have formal guarantees of progress for the distributed protocols used in ATM. Under asynchronous settings, message transmission and processing delays are unbounded, making it impossible to provide deterministic bounds on the time required to make progress. We present an approach for formally guaranteeing timely progress in a Two-Phase Acknowledge distributed knowledge propagation protocol by probabilistically modeling the delays using theories of the Multicopy Two-Hop Relay protocol and the M/M/1 queue system. The guarantee states a probabilistic upper bound to the time for progress as a function of the probabilities of the total transmission and processing delays being less than two given values. We also showcase the development of a library of formal theories, that is tailored towards reasoning about timely progress in distributed protocols deployed in airborne networks, in the Athena proof assistant.

A secure blockchain-oriented data delivery and collection scheme for 5G-enabled IoD environment

−There are innumerable ways the Internet of Drones (IoD) technology can impact our society. With the deployment of an airborne network, the IoD can support real-time low-cost delivery of services ranging from military surveillance to a myriad of civilian applications. Nevertheless, the drones employ insecure wireless communication channels to communicate with other entities in the system, inhibiting its induction in sensitive installations if insecure or inefficient Authenticated Key Agreement (AKA) schemes are employed. The blockchain, an open distributed ledger-based technology, is increasingly being adopted to address the security concern as discussed. Recently, Bera et al. presented an efficient blockchain-enabled AKA scheme for data management among various entities in IoD network. However, their scheme does not support anonymity and untraceability for the drones; also, it does not provide resistance to Ground station server impersonation attack, while the protocol has a few redundancies. Later, we proposed an enhanced blockchain-enabled AKA scheme BOD5-IOD to authenticate drones in the system. The BOD5-IOD, other than supporting a robust access control mechanism between drones and GSS, also ensures safe transactions among all entities in the IoD environment. The formal analysis and performance evaluation endorse that our scheme supports security requirements with computational and communication efficiency of 34.4% and 23.3%, respectively.

Synchronous playback technology of airborne network video based on RTP

In the flight test, the airborne video data is an important part of the flight test data. In order to solve the problem of synchronous playback of multiple channels of airborne network video data, a synchronous playback scheme of multiple channels of airborne network video data based on the RTP/RTCP protocol is proposed. The network video data streaming and preprocessing mechanism are used in this paper. Firstly, by identifying the data packets, the network video data packets are extracted from the data recorded on the airborne. Then, after unpacking and time calculation of the RTP-encapsulated H.264 video data, it uses FFmpeg’s good decoding capabilities to decode a frame of image, and uses SDL’s excellent video performance to display YUV data. The synchronization mechanism based on timestamp is adopted to realize the fast synchronized playback of multiple airborne network videos. The software adopts modular and multi-threaded design ideas to improve processing efficiency and ensure functional scalability. At present, the system has been successfully applied to the simultaneous playback of multi-channel airborne network video. The actual application effect shows that the system works stably and can effectively improve the efficiency of model flight tests.

Learning-Backoff based Wireless Channel Access for Tactical Airborne Networks

Learning-Backoff based Wireless Channel Access for Tactical Airborne Networks

Airborne Network Traffic Identification Method under Small Training Samples

Due to the high cost and difficulty of traffic data set acquisition and the high time sensitivity of traffic distribution, the machine learning-based traffic identification method is difficult to be applied in airborne network environment. Aiming at this problem, a method for airborne network traffic identification based on the convolutional neural network under small traffic samples is proposed. Firstly, the pre-training of the initial model for the convolutional neural network is implemented based on the complete data set in source domain, and then the retraining of the convolutional neural network is realized through the layer frozen based fine-tuning learning algorithm of convolutional neural network on the incomplete dataset in target domain, and the convolutional neural network model based feature representing transferring(FRT-CNN) is constructed to realize online traffic identification. The experiment results on the actual airborne network traffic dataset show that the proposed method can guarantee the accuracy of traffic identification under limited traffic samples, and the classification performance is significantly improved comparing with the existing small-sample learning methods.

Enhancing Extensive and Remote LoRa Deployments through MEC-Powered Drone Gateways.

The distribution of Internet of Things (IoT) devices in remote areas and the need for network resilience in such deployments is increasingly important in smart spaces covering scenarios, such as agriculture, forest, coast preservation, and connectivity survival against disasters. Although Low-Power Wide Area Network (LPWAN) technologies, like LoRa, support high connectivity ranges, communication paths can suffer from obstruction due to orography or buildings, and large areas are still difficult to cover with wired gateways, due to the lack of network or power infrastructure. The proposal presented herein proposes to mount LPWAN gateways in drones in order to generate airborne network segments providing enhanced connectivity to sensor nodes wherever needed. Our LoRa-drone gateways can be used either to collect data and then report them to the back-office directly, or store-carry-and-forward data until a proper communication link with the infrastructure network is available. The proposed architecture relies on Multi-Access Edge Computing (MEC) capabilities to host a virtualization platform on-board the drone, aiming at providing an intermediate processing layer that runs Virtualized Networking Functions (VNF). This way, both preprocessing or intelligent analytics can be locally performed, saving communications and memory resources. The contribution includes a system architecture that has been successfully validated through experimentation with a real test-bed and comprehensively evaluated through computer simulation. The results show significant communication improvements employing LoRa-drone gateways when compared to traditional fixed LoRa deployments in terms of link availability and covered areas, especially in vast monitored extensions, or at points with difficult access, such as rugged zones.

A robustness-enhanced traffic classification method in airborne network

A robustness-enhanced traffic classification method in airborne network

A Deep Learning-Based Blockchain Mechanism for Secure Internet of Drones Environment

Drones are equipped with high-vision cameras, advanced sensors, and GPS receivers to deliver diverse services from high altitude thereby creating an airborne network. In this environment, physical things (drones, sensors, etc.,) are controlled using computational algorithms to form a cyber-physical system for the Internet of drones. Although the drones provide manifold benefits still there are many issues (security, privacy, and data integrity) which must be resolved before the usage of drones in smart cyber-physical systems. So, in this paper, a blockchain-based security mechanism for cyber-physical systems is proposed to ensure secure transfer of information among drones. In this mechanism, the miner node is selected using a deep learning-based approach, i.e., a deep Boltzmann machine, using features like computational resources, the available battery power, and flight time of the drone. The proposed mechanism is evaluated based on different performance metrics and the results obtained show the potential benefits of the proposed scheme.

Mobility Modeling and Stochastic Property Analysis of Airborne Network

In literature, a few attempts have been made to model the mobility of the airborne networks. However, existing models are mostly application specific and oversimplify the system dynamics. This paper proposes a comprehensive mobility model for the airborne networks where the mobility of the airborne node is presented as a Gauss–Markov process comprising of three key parameters—the speed, angular velocity, and pitch angle. The consideration of the Gauss–Markov process and angular velocity enables the model to address the mobility more realistically and, thus, makes it capable of generating apparently all possible types of trajectories. Additionally, this model designs more realistic boundary effects to be practical. This paper also presents the stochastic analysis of the proposed model. Finally, by analyzing the extensive simulation results, it has been affirmed that the proposed mobility model can be used as a fundamental template to model and analyze a wide range of practical airborne networks regardless of the application including both homogeneous and heterogeneous networks.

Real-time performance evaluation technology of airborne network for avionics system

Real-time performance evaluation technology of airborne network for avionics system

A Timeliness-Enhanced Traffic Identification Method in Airborne Network

High dynamic topology and limited bandwidth of the airborne network make it difficult to provide reliable information interaction services for diverse combat mission of aviation swarm operations. Therefore, it is necessary to identify the elephant flows in the network in real time to optimize the process of traffic control and improve the performance of airborne network. Aiming at this problem, a timeliness-enhanced traffic identification method based on machine learning Bayesian network model is proposed. Firstly, the data flow training subset is obtained by preprocessing the original traffic dataset, and the sub-classifier is constructed based on Bayesian network model. Then, the multi-window dynamic Bayesian network classifier model is designed to enable the early identification of elephant flow. The simulation results show that compared with the existing elephant flow identification method, the proposed method can effectively improve the timeliness of identification under the condition of ensuring the accuracy of identification.

A Distributed Mode Reassignment Scheme for Mobile Multihop Wireless Network of FDD Nodes

The use of frequency division duplex (FDD) communication in a multihop wireless network partitions the nodes in two operating modes. Since the FDD nodes of the same mode cannot communicate with each other, it can limit the node connectivity and also lead to network partitioning. In this article, a novel, distributed, low-complexity mode reassignment (MR) scheme is designed for maintaining connectivity among the mobile FDD nodes in a decentralized, multihop wireless (airborne) network. Since it is not practical to know the complete topology of such a network in real time, the proposed scheme is fully distributed, relies only on the local node information, and applied only on those nodes whose connectivity drops below a threshold. Besides maintaining a desired value of connectivity degree for each node while it is moving around, the proposed MR scheme also minimizes the impact of mode change on the existing links and ongoing data transmissions. A Kalman filter based node mobility prediction and link failure prediction scheme is designed to select the best candidate nodes for mode change. The optimality and error control bounds of the proposed MR scheme show that it consistently achieves optimal connectivity for most nodes, while requiring a low number of nodes undergoing the mode change during each MR round.

Enhanced OLSR routing for airborne networks with multi-beam directional antennas

This research targets the robust routing protocols in airborne networks (ANs). Particularly, we target the AN which is equipped with the latest antenna technology, called Multi-Beam Directional Antenna (MBDA) [1]. MBDA allows the simultaneously packet delivery in multiple directions without RF interference between directions/beams. We have found that MBDAs can actually bring new opportunities to enhance a popularly used AN routing scheme, i.e., optimized link state routing protocol (OLSR). In particular, MBDAs enable OLSR to better achieve low probability of detection (LPD), a critical requirement in many military applications with adversary nodes nearby which try to eavesdrop the signals. Therefore, we propose a multi-path enhanced OLSR, which is able to exploit the advantages of multi-beam transmissions and select the proper multi-point relays (MPRs) to easily reach all nodes by using only a small number of message broadcasts. An innovative method based on social network concept is used for the selection of MPRs in multi-beam OLSR. Finally, we have conducted the simulations and showed that the enhanced OLSR is suitable for mobile and dense airborne networks with LPD requirements.

Dynamic resource adaptation method for airborne network based on multi-objective optimisation

Aiming at the problem that the existing methods have low resource utilisation, low task submission success rate, high overhead and high consumption in the process of airborne network resource adaptation, a dynamic resource allocation method based on multi-objective optimisation for airborne network is proposed. Taking the RAGE delay of the airborne network system model as the objective function, the problem is transformed into the graph dyeing problem. By constructing the dynamic resource migration model on the airborne network server and the application-aware dynamic resource migration model, the average delay in the dynamic resource allocation process of the airborne network can be achieved. The speech completed the key technical analysis of the dynamic resource adaptation of the airborne network. The experimental results show that the method overcomes the shortcomings of the existing methods, improves resource utilisation, task submission success rate and deadline satisfaction rate, reduces system overhead and improves user experience.

Dynamic resource adaptation method for airborne network based on multi-objective optimisation

Aiming at the problem that the existing methods have low resource utilisation, low task submission success rate, high overhead and high consumption in the process of airborne network resource adaptation, a dynamic resource allocation method based on multi-objective optimisation for airborne network is proposed. Taking the RAGE delay of the airborne network system model as the objective function, the problem is transformed into the graph dyeing problem. By constructing the dynamic resource migration model on the airborne network server and the application-aware dynamic resource migration model, the average delay in the dynamic resource allocation process of the airborne network can be achieved. The speech completed the key technical analysis of the dynamic resource adaptation of the airborne network. The experimental results show that the method overcomes the shortcomings of the existing methods, improves resource utilisation, task submission success rate and deadline satisfaction rate, reduces system overhead and improves user experience.

An improved FAHP-cloud-based security risk assessment model for airborne networks

In connection with problems such as difficulty in the objective and accurate calculation of the index weight in the security risk assessment of an airborne network, as well as a unitary assessment result and poor descriptiveness, a risk assessment method for airborne network security based on an improved fuzzy analytic hierarchy process (FAHP)-cloud model has been proposed. First, the particularity of the airborne network is taken into account to establish an index system for the risk assessment of airborne network security. the traditional FAHP, information entropy is introduced to calculate the comprehensive weight, which is subsequently combined with the cloud model theory to construct an actual integrated cloud model. By calculating the degree of similarity between the actual integrated cloud model and the standard cloud model, the risk levels of airborne network security are determined. The simulation experiments show that this method can reflect the objectivity of the index weight, make the assessment results more visual and reliable, and fully reflect the fuzziness and randomness in the assessment.

Regression Based Dynamic Elephant Flow Detection in Airborne Network

As an important part of the spatial information network, airborne network (AN), which connects air platforms with upper satellites and ground devices, has been increasingly important right now. Due to the heavy-tailed distribution of network traffic, elephant flow detection is usually used to catch and control the key part of network traffic with low costs, which is a practical strategy to strengthen network management and improve network performance. In this paper, we consider the problem of dynamic threshold elephant flow detection in AN, and an intelligent method based on regression with pre-classification is proposed to adapt to the limited and dynamically changing bandwidth. The filtering mechanism with waiting-window is used firstly to filter out parts of small flows to decrease the detection cost. Then, the pre-classification is used to divide the range to be predicted and the flow size regression can be carried out in a compressed range, which makes the results more accurate. Finally, the predicted size is compared with the specific detection threshold related to the specific moment, and the elephant flow is identified. Numerical experiments demonstrate that the proposed method has a better adaptability to dynamic threshold and the performance is much better.

Modification of feige-fiat-shamir based on public key cryptography

In networks and entity groupings that have sensitive resources, user identification is a crucial requirement for secure access, communication and transactions involving those resources. However, there are networks and entity groupings that require entity authentication while preserving the privacy of the entity being authenticated. There are several zero-knowledge protocols (ZKP) including the Fiege-Fiat-Shamir that authenticate an entity anonymously. We present a revised Feige-Fiat-Shamir ZKP scheme for the Airborne Networks (ANs) that reduces the ping-pong effect in the scheme and speeds up the growth of the Verifier trust of the Prover, thus making the authentication process faster and more efficient.