While the Internet of Things (IoT), coupled with integrated terrestrial-aerial-space networks, has revolutionized the domain of ubiquitous remote sensing for natural resource management, several research challenges emerge due to the explosion of the collected IoT data. For instance, the edge nodes in these hybrid, next-generation networks are anticipated to carry out edge computing on the collected data to provide localized computing to enable early warning systems, including forest fire occurrence and spread, earth- quake wave detection, tsunami forecasting, and so forth. While edge computing can significantly reduce the high communication time required in the traditional cloud-based remote sensing analytics, it is important to develop a lightweight training framework to obtain smart remote sensing analytics at the edge devices in the integrated network while preserving the privacy of the collected data. In this article, we propose an asynchronously updating federated learning model for the edge nodes to build local artificial intelligence models for smart remote sensing with a forest fire detection use case without the need for explicit data exchange with the cloud. This jointly preserves data privacy and also alleviates the network overhead. Extensive experimental results demonstrate the viability of our proposal in terms of significantly high remote sensing accuracy, low convergence time, and low bandwidth overhead compared to existing methods.

中文翻译：

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地面-空中-空间综合网络上的智能物联网遥感：一种异步联合学习方法

虽然物联网 (IoT) 与集成的地-空-空间网络相结合，彻底改变了用于自然资源管理的无处不在的遥感领域，但由于收集到的物联网数据的爆炸式增长，一些研究挑战也出现了。例如，预计这些混合下一代网络中的边缘节点将对收集的数据进行边缘计算，以提供本地化计算，以实现预警系统，包括森林火灾的发生和蔓延、地震波检测、海啸预报。 ，等等。虽然边缘计算可以显着减少传统基于云的遥感分析所需的高通信时间，重要的是开发一个轻量级的训练框架，以便在集成网络的边缘设备上获得智能遥感分析，同时保护所收集数据的隐私。在本文中，我们为边缘节点提出了一种异步更新的联合学习模型，以使用森林火灾检测用例为智能遥感构建本地人工智能模型，而无需与云进行显式数据交换。这共同保护了数据隐私并减轻了网络开销。与现有方法相比，大量的实验结果证明了我们的提议在遥感精度高、收敛时间短和带宽开销低方面的可行性。我们为边缘节点提出了一种异步更新的联合学习模型，以使用森林火灾检测用例为智能遥感构建本地人工智能模型，而无需与云进行显式数据交换。这共同保护了数据隐私并减轻了网络开销。与现有方法相比，大量的实验结果证明了我们的提议在遥感精度高、收敛时间短和带宽开销低方面的可行性。我们为边缘节点提出了一种异步更新的联合学习模型，以构建用于具有森林火灾检测用例的智能遥感的本地人工智能模型，而无需与云进行显式数据交换。这共同保护了数据隐私并减轻了网络开销。与现有方法相比，大量的实验结果证明了我们的提议在遥感精度高、收敛时间短和带宽开销低方面的可行性。