NARRAS: Edge-Triggered Distributed Inference for CSI-Based Localization in Vehicular IoT Networks
arXiv:2606.11914v1 Announce Type: cross Abstract: CSI-based localization with spatially distributed antenna arrays exposes a basic resource trade-off. Each array can provide a rich view of the channel, but forwarding observations from all arrays to a fusion center is wasteful when only a few carry useful information, and the shared uplink supports only a limited number of simultaneous transmissions. We let each array decide locally whether its current observation is worth reporting, subject to a budget on the average number of active transmitters. We refer to this abstraction as Edge-Triggered
The proliferation of IoT devices and demand for efficient, real-time data processing at the edge is driving innovation in distributed inference. Advancements in AI and edge computing capabilities are making such systems feasible.
This research addresses a fundamental trade-off in distributed sensor networks, optimizing resource use and enabling more scalable and efficient localized AI applications. It paves the way for smarter, more autonomous vehicular networks and other IoT ecosystems.
Traditional centralized data fusion methods for localization in vehicular IoT networks will be challenged by a more decentralized, intelligent, edge-triggered approach. This could significantly reduce bandwidth strain and latency while improving accuracy.
- · Vehicular IoT Manufacturers
- · Edge AI Developers
- · Smart City Infrastructure
- · Telecommunications Providers
- · Legacy Centralized Data Processing Platforms
More efficient and reliable real-time localization and sensing in dense IoT environments becomes possible.
Enhanced capabilities for autonomous vehicles and drone swarms that rely on precise, distributed environmental awareness.
This could lead to a shift in network architecture design, prioritizing edge intelligence and localized decision-making over constant cloud communication.
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Read at arXiv cs.LG