Sungsoo Ahn

The rapid adoption of small drones for applications such as surveillance, disaster response, and infrastructure inspection has increased demand for coordinated multi-drone operations, in which information exchange is essential. However, effective collaboration across different drones remains challenging due to differences in information exchange and communication protocols. This paper proposes a Networked Drone Information Exchange Model (NDIEM), an XML-based model that enables interoperability of the structural information. NDIEM defines five essential element categories: Identification, Telemetry, Command and Control, Sensor, and Mission. NDIEM can be used with protocol-specific adapters for exchanging information in multi-drone operations. The proposed model has been implemented and evaluated on three different drones (an ArduPilot-based Hexacopter, a Crazyflie 2.1, and a Tello EDU) using real sensor telemetry captured under controlled conditions, in which each platform’s onboard sensors were manipulated to generate representative telemetry variation. Experimental results demonstrate information interoperability with 0.013–0.019 ms processing overhead, 0.049–0.073 ms transformation latency per message, and XSD schema validation compliance, achieving 69.7% transformation completeness for telemetry data. These findings show that NDIEM can provide a practical and scalable foundation for software development for drone collaboration and interoperability.

Educational drones have become increasingly important in education and research due to their affordability, user-friendly design and control, and potential use as tools in STEM (Science, Technology, Engineering, and Math) learning. For example, CoDrone EDUs are used to teach basic programming principles and drone control to high school or university students. As drones in real-world applications often collaborate to solve problems, controlling multiple educational drones in a team is crucial and beneficial for enhancing students’ problem-solving and design skills. However, these educational drones primarily rely on one-to-one control via a radio-frequency remote controller, and programming libraries for coordinating multi-drone missions are limited, posing challenges for students or developers in controlling them effectively. To address the lack of control in missions with multiple educational drones, we present a drone control station (DCS), featuring a centralized architecture that connects and controls various drones. We first develop scenarios and use cases that utilize multiple drones, specifying the system requirements. We then design conceptual models and architectures for the DCS. Next, we implement the DCS and evaluate whether it achieves the team missions. Experimental results show that the DCS with the centralized architecture is suitable for team missions with multiple educational drones. We expect the approach in our work to serve as a method for controlling multi-drone missions in an educational environment.

Educational drones have become important in research due to their affordability, user-friendly design and control, and potential for beginner learning. However, most rely on one-to-one control through radio-frequency remote controller, which creates challenges in coordinating multiple units since inter-device communication is limited. To address this issue, this work uses two architectural approaches with object-oriented methods for CoDrone EDU models and explores a Drone Control Station system to achieve a mission using onboard capabilities such as the color sensor. The first approach is a centralized architecture, connecting multiple drones to a single station via a USB hub. The second approach is a client-server architecture, enabling coordination among many station instances over TCP/IP. Applying object-oriented principles allows the control of multi-drone missions to be organized into well-defined components. With these principles, it is possible to enhance scalability, enabling flexible integration of behaviors and mission requirements. Through flight tests based on the mission of yellow card detection, the control architectures are being tested and compared, focusing on mission time and inspection coverage. Results show that while centralized control is simple and suitable for basic applications, it lacks scalability. In contrast, client-server control improves scalability, though it requires higher implementation effort and a stable network. The flight tests demonstrated approximately 77% reduction in mission time when employing multiple drones compared to using a single drone. We expect our work to serve as one approach for controlling multiple drones with limited capabilities and encourage the use of educational drones in other domains.

Research datasets—capturing natural, societal, or artificial phenomena—are critical in generating new scientific insights, validating research models, and supporting data‐intensive discovery. Data papers that describe and contextualise these datasets aim to ensure their findability, accessibility, interoperability, and reusability (FAIR) while providing academic credit to data creators. However, the peer review of data papers and associated datasets presents considerable challenges, requiring reviewers to assess both the syntactic and semantic integrity of the data, metadata quality, and domain‐specific scientific relevance. Furthermore, the coordination between journal editors, reviewers, and curators demands substantial effort, often leading to publication delays in the conventional review and then publishing framework. This study proposes a novel Publish‐Review‐Curate (PRC) model tailored to the synchronised publication and review of data papers and their underlying datasets. Building on preprint and open science practices, the model defines a collaborative, multi‐stakeholder workflow involving authors, peer reviewers, data experts, and journal editors. The PRC model integrates open feedback, transparent peer review, and structured curation to improve research data's quality, discoverability, and impact. By articulating conceptual and operational workflows, this study contributes a practical framework for modernising data publishing infrastructures and supporting the co‐evaluation of narrative and data artefacts.