GENDN: A Geospatially Enhanced NDN Framework for Location-Related Pub/Sub Services in NTN-Enabled IoT

Leveraging satellites and aerials vehicle, nonterrestrial network (NTN)-enabled IoT networks enhance coverage and reliability, enabling global data connections in remote and underserved regions. A key application within these networks is the location-related publish/subscribe service (LPSS), which is geospatial location sensitive, real time, and energy efficient, supporting disaster early warning and environmental monitoring. We demonstrate that, compared to IP technology, named data networking (NDN) is more suited to supporting LPSS. However, current NTN-enabled IoT networks lack mechanisms to utilize geospatial characteristics effectively. Additionally, interactions between IoT devices and aerial vehicles or satellites face challenges, such as low bandwidth, high latency, and intermittent connectivity, which hinder the efficiency of LPSS. We propose geospatially enhanced NDN (GENDN), an adapted NDN framework for supporting LPSS. GENDN incorporates Geohash encoding in content names, allowing flexible use of geospatial characteristics in data subscription. GENDN enhances request aggregation, enabling a single Interest packet (I-pkt) to subscribe to all data in adjacent areas without sequential matching and retrieval. Simulation experiments demonstrate that, compared to traditional NDN, GENDN: 1) effectively leverages geospatial data characteristics, increasing the hit rate of I-pkts in LPSS; 2) reduces the PIT size and network communication overhead, enhancing real-time performance and energy efficiency; and 3) shows potential for large-scale deployment in NTN-enabled IoT environments.