Chemical agents are one of the major threats to soldiers in modern warfare, so it is so important to detect chemical agents rapidly and accurately on battlefields. Raman spectroscopy-based detectors are widely used but have many limitations. The Raman spectrum changes unpredictably due to various environmental factors, and it is hard for detectors to make appropriate judgments about new chemical substances without prior information. Thus, the existing detectors with inflexible techniques based on determined rules cannot deal with such problems flexibly and reactively. Artificial intelligence (AI)-based detection techniques can be good alternatives to the existing techniques for chemical agent detection. To build AI-based detection systems, sufficient amounts of data for training are required, but it is not easy to produce and handle fatal chemical agents, which causes difficulty in securing data in advance. To overcome the limitations, in this paper, we propose the distributed Raman spectrum data augmentation system that leverages federated learning (FL) with deep generative models, such as generative adversarial network (GAN) and autoencoder. Furthermore, the proposed system utilizes various additional techniques in combination to generate a large number of Raman spectrum data with reality along with diversity. We implemented the proposed system and conducted diverse experiments to evaluate the system. The evaluation results validated that the proposed system can train the models more quickly through cooperation among decentralized troops without exchanging raw data and generate realistic Raman spectrum data well. Moreover, we confirmed that the classification model on the proposed system performed learning much faster and outperformed the existing systems.

In recent years, due to the development of technologies for unmanned aerial vehicles (UAVs), also known as drones, UAVs have developed rapidly. Because of UAVs’ high mobility and computational capability, UAVs have a wide range of applications in Industrial Internet of Things (IIoT), such as infrastructure inspection, rescue, exploration, and surveillance. To accomplish such missions, it is more proper and efficient to utilize multiple UAVs in a swarm, rather than a single UAV. However, it is difficult for an operator to understand and control numerous UAVs in different situations, so UAVs require the significant level of autonomy. Artificial intelligence (AI) has become the most promising combination with UAVs to ensure the high autonomy of UAVs by establishing swarm intelligence (SI). However, existing learning methods for building SI require continuous information sharing among UAVs, which incurs repeated data exchanges. Thus, such techniques are not suitable for constructing SI in the UAV swarm, in which communication resources are not readily available on unstable UAV networks. To overcome this limitation, in this paper, we propose the federated reinforcement learning‐ (FRL‐) based UAV swarm system for aerial remote sensing. The proposed system applies reinforcement learning (RL) to UAV clusters to establish the SI in the UAV system. Furthermore, by combining federated learning (FL) with RL, the proposed system constructs the more reliable and robust SI for UAV systems. We conducted diverse evaluations, and the results show that the proposed system outperforms the existing centralized RL‐based system and is more suited for UAV swarms from a variety of perspectives.

Various unmanned aerial vehicles (UAVs), also called drones, have developed based on advances in hardware and software technologies. Thus, service providers in diverse areas have tried to utilize drones to create more effective solutions. In many cases, employing multiple drones is more effective to perform the given mission than using a single drone. To utilize multiple drones, the drones should be strongly connected, but it is not trivial to construct reliable and efficient networks for drones due to their high mobility. Therefore, we propose a ground control system (GCS) routing protocol (GCS-routing) to overcome this limitation and provide reliable and efficient multi-drone control system, where GCS-routing maximizes GCS utilization. GCS is the essential component of flying ad-hoc network (FANET) and can obtain information about drones. Using this information, GCS-routing can provide more effective routing, predict any topology changes, and react immediately. GCS-routing does not issue any periodic HELLO message for neighbor discovery or link cost estimation, which significantly enhances network performance. We implemented GCS-routing on real drones, and applied GCS-routing to actual drone fleets, as well as simulations to evaluate GCS-routing performance. The results clearly identify the advantages of the proposed routing protocol for drone networks compared with current routing protocols.

Recently, unmanned aerial vehicles (UAVs) have been applied to various applications. In order to perform repetitive and accurate tasks with a UAV, it is more efficient for the operator to perform the tasks through an integrated management program rather than controlling the UAVs one by one through a controller. In this environment, control packets must be reliably delivered to the UAV to perform missions stably. However, wireless communication is at risk of packet loss or packet delay. Typical network communications can respond to situations in which packets are lost by retransmitting lost packets. However, in the case of UAV control, delay due to retransmission is fatal, so control packet loss and delay should not occur. As UAVs move quickly, there is a high risk of accidents if control packets are lost or delayed. In order to stably control a UAV by transmitting control messages, we propose a control packet transmission scheme, ConClone. ConClone replicates control packets and then transmits them over multiple network connections to increase the probability of successful control packet transmission. We implemented ConClone using real equipment, and we verified its performance through experiments and theoretical analysis.

Supported by the advances in rocket technology, companies like SpaceX and Amazon competitively have entered the satellite Internet business. These companies said that they could provide Internet service sufficiently to users using their communication resources. However, the Internet service might not be provided in densely populated areas, as the satellites coverage is broad but its resource capacity is limited. To offload the traffic of the densely populated area, we present an adaptable aerial access network (AAN), composed of low-Earth orbit (LEO) satellites and federated reinforcement learning (FRL)-enabled unmanned aerial vehicles (UAVs). Using the proposed system, UAVs could operate with relatively low computation resources than centralized coverage management systems. Furthermore, by utilizing FRL, the system could continuously learn from various environments and perform better with the longer operation times. Based on our proposed design, we implemented FRL, constructed the UAV-aided AAN simulator, and evaluated the proposed system. Base on the evaluation result, we validated that the FRL enabled UAV-aided AAN could operate efficiently in densely populated areas where the satellites cannot provide sufficient Internet services, which improves network performances. In the evaluations, our proposed AAN system provided about 3.25 times more communication resources and had 5.1% lower latency than the satellite-only AAN.

Practical evaluation of the Unmanned Aerial Vehicle (UAV) network requires a lot of money to build experiment environments, which includes UAVs, network devices, flight controllers, and so on. To investigate the time-sensitivity of the multi-UAV network, the influence of the UAVs’ mobility should be precisely evaluated in the long term. Although there are some simulators for UAVs’ physical flight, there is no explicit scheme for simulating both the network environment and the flight environments simultaneously. In this paper, we propose a novel co-simulation scheme for the multiple UAVs network, which performs the flight simulation and the network simulation simultaneously. By considering the dependency between the flight status and networking situations of UAV, our work focuses on the consistency of simulation state through synchronization among simulation components. Furthermore, we extend our simulator to perform multiple scenarios by exploiting distributed manner. We verify our system with respect to the robustness of time management and propose some use cases which can be solely simulated by this.