SDN-based Federated Learning approach for Satellite-IoT Framework to Enhance Data Security and Privacy in Space Communication
SDN-based Federated Learning approach for Satellite-IoT Framework to Enhance Data Security and Privacy in Space Communication
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DOI：
10.1109/wisee49342.2022.9926943
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ryhan Uddin;Sathish A. P. Kumar
中科院分区：
文献类型：
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作者：
Ryhan Uddin;Sathish A. P. Kumar
物联网设备的激增和机器学习技术的集成为人工智能（AI）指导下的各个领域的自动化铺平了道路。它支持多种用例，从智能物联网设备之间基于大规模云边缘计算的强大通信，天气变化检测驻留在恶劣地形上的低功率遥感节点，人工智能辅助无人驾驶车辆完美地在交通中巡航，到精确而巧妙地执行复杂任务的工业机器人。随着太空殖民正在成为过去的神话，并冒险走向现实，这种基于人工智能的物联网无处不在也将成为那些自主基础设施将成为常态的太空殖民地的主要市场。这些物联网集成网络还将拥有广泛的覆盖范围，通过低轨卫星集成达到最远的视野。然而，这些现代技术的大规模部署在很大程度上取决于保护数据免受恶意入侵的事实。因此，在本文中，我们提出了一种方法来阻止可能影响卫星-物联网框架在空间通信方面的数据泄露。该框架基于软件定义的网络，该网络使用分布式系统的联邦学习技术，并在设备之间共享数据时采用尊重隐私，以确保物联网设备之间安全的关键数据传输。
The proliferation of IoT devices and integration of machine learning technologies paved the path towards automation in various sectors guided by Artificial intelligence (AI). It enables multitudes of use cases ranging from mass scale cloud-edge computing based robust communication between smart IoT devices, weather variation detecting low powered remote sensor nodes residing on a harsh terrain, AI-assisted driverless vehicles immaculately cruising through traffic to industrial robots performing sophisticated tasks with precision and finesse. As space colonization is a becoming a myth of the past and venturing towards reality, this AI-based IoT ubiquity will also be a major mart of those space colonies where autonomous infrastructures with be the norm. These IoT integrated networks will also boast a wide area of coverage reaching the furthest of the horizons with low orbit satellite integration. However, the mass deployment of these modern technologies is heavily contingent to the fact that data is safeguarded from malicious intrusions. Therefore, in this paper we have proposed an approach to thwart data breach that can plague satellite-IoT framework with respect to space communication. The framework is based on software defined networking that uses federated learning techniques for distributed systems and employs deferential privacy while sharing data among devices to ensure secured critical data transmission between IoT devices.