A recent study published in Engineering has introduced a novel framework for assessing and optimizing the spatiotemporal resilience of unmanned systems of systems (USS) enabled by the Internet of Things (IoT). The research, conducted by a team from Zhengzhou University, the University of Kent, and City University of Hong Kong, aims to address the critical gap in understanding how spatial and temporal dynamics impact the resilience of USS during mission execution.
The study begins by highlighting the increasing reliance on unmanned aerial vehicles (UAVs) and unmanned vehicles (UVs) in high-risk environments, where their ability to withstand disruptions and recover functionality is crucial. Traditional resilience assessments have predominantly focused on temporal variations, neglecting the spatial dimension that significantly influences data transmission stability. To bridge this gap, the researchers propose a spatiotemporal resilience model that integrates both temporal and spatial dimensions, providing a more comprehensive evaluation of USS performance.
The proposed model leverages the IoT framework to construct a multi-layered USS architecture that deeply integrates physical and data aspects. This architecture includes four hierarchical layers: physical, perceptual, communication, and application. The physical layer comprises unmanned assets such as UAVs and UVs, while the data layer facilitates inter-unit data exchange and cross-layer connectivity. The researchers argue that this integrated approach is essential for accurately mapping the real state of USS in complex mission environments.
A key contribution of the study is the introduction of the concept of spatiotemporal performance, which characterizes the state of the USS across both temporal and spatial dimensions. The researchers derive mathematical formulations to quantify spatiotemporal performance, considering factors such as path loss, signal frequency, and UAV-UV distances. They also propose a method for optimizing spatiotemporal resilience through preventive maintenance and strategic deployment of UVs based on spatiotemporal resilience loss metrics.
In the prevention phase, the study suggests an optimal path algorithm for UVs to minimize path loss and enhance data layer performance. During the recovery phase, a repair and reconfiguration strategy is proposed, prioritizing UAVs with higher recovery importance to accelerate resilience restoration. The researchers validate their concepts through a case study involving a hexagonal deployment of six UAVs and one UV, demonstrating that the proposed optimization strategy improves prevention spatiotemporal resilience by 0.22%, recovery spatiotemporal resilience by 8.39%, and overall spatiotemporal resilience by 11.29%.
The findings of this study have significant implications for the development and deployment of future unmanned systems. By integrating spatial and temporal dimensions into resilience assessments, the proposed framework offers a more holistic approach to enhancing the robustness and adaptability of USS. This research not only advances the theoretical understanding of spatiotemporal resilience but also provides practical guidelines for optimizing the performance of unmanned systems in dynamic and complex environments.
As the field of unmanned systems continues to evolve, the integration of IoT technologies and the consideration of spatiotemporal dynamics will be crucial for ensuring the reliability and effectiveness of these systems in various applications, from surveillance and reconnaissance to disaster response and environmental monitoring.
The paper "Spatiotemporal Resilience of IoT-enabled Unmanned System of Systems," is authored by Hongyan Dui, Huanqi Zhang, Shaomin Wu, Min Xie. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.06.024
