LoRaWAN Gateway Placement for Network Planning Using Ray Tracing-based Channel Models
2026-04-01T07:23:56Z•e470124449195c1428732c420a5d57145d670ae4f036e79b3dca22910a850252
5G-A6G6GAgentGymBGP hijackGreenFLagI/Q exposureIPv6ISACLoRaLoRaWANO-RANROVRPKIRaspberry PiTORCHUAV trackingYOLOv3-tinyagentic network managementdAppsedge AIfederated learninggateway placementnetwork measurementpoint cloudsray tracing
What happened
This document aggregates multiple recent arXiv papers (networking, wireless, and edge AI) covering: sensitivity of LoRaWAN gateway placement to channel-model fidelity (ray-tracing vs stochastic); an edge multi-sensor parking barrier using Raspberry Pi+LoRa and pruned YOLOv3-tiny; programmable inference and ISAC via dApps in open RAN (real-time I/Q and telemetry exposure); BSense — UAV tracking from commercial 5G-A base-station point clouds with a multi-stage noise-filtering pipeline; TORCH — a large-scale measurement framework showing incomplete RPKI/ROV deployment in IPv6 (only ~27% ASes near
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- e470124449195c1428732c420a5d57145d670ae4f036e79b3dca22910a850252
- Enrichment time
- 2026-04-01T07:23:56Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.