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A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks

Manish Bansal, Pramsu Shrivastava, J. Harshan

TL;DR

This work addresses the privacy-utility trade-off in spatial provenance for V2X multi-hop networks, where RSUs need some localization information without requiring exact vehicle GPS data. The authors propose a fragment-based spatial-provenance approach that encodes segment identities in Bloom filters, with RAKE compression to reduce payload, and they validate feasibility on a ZigBee (XBee) and LoRa testbed. Key contributions include a concrete prototype demonstrating low-to-moderate localization within constrained payloads, quantifying payload and latency impacts, and outlining how segmentation granularity governs privacy versus localization accuracy. The findings suggest the approach is viable for next-generation vehicular networks to provide real-time security and diagnostics while preserving vehicle privacy, albeit with a trade-off where higher localization precision demands larger payloads.

Abstract

In Vehicle-to-Everything (V2X) networks that involve multi-hop communication, the Road Side Units (RSUs) typically desire to gather the location information of the participating vehicles to provide security and network-diagnostics features. Although Global Positioning System (GPS) based localization is widely used by vehicles for navigation; they may not forward their exact GPS coordinates to the RSUs due to privacy issues. Therefore, to balance the high-localization requirements of RSU and the privacy of the vehicles, we demonstrate a new spatial-provenance framework wherein the vehicles agree to compromise their privacy to a certain extent and share a low-precision variant of its coordinates in agreement with the demands of the RSU. To study the deployment feasibility of the proposed framework in state-of-the-art wireless standards, we propose a testbed of ZigBee and LoRa devices and implement the underlying protocols on their stack using correlated Bloom filters and Rake compression algorithms. Our demonstrations reveal that low-to-moderate precision localization can be achieved in fewer packets, thus making an appealing case for next-generation vehicular networks to include our methods for providing real-time security and network-diagnostics features.

A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks

TL;DR

This work addresses the privacy-utility trade-off in spatial provenance for V2X multi-hop networks, where RSUs need some localization information without requiring exact vehicle GPS data. The authors propose a fragment-based spatial-provenance approach that encodes segment identities in Bloom filters, with RAKE compression to reduce payload, and they validate feasibility on a ZigBee (XBee) and LoRa testbed. Key contributions include a concrete prototype demonstrating low-to-moderate localization within constrained payloads, quantifying payload and latency impacts, and outlining how segmentation granularity governs privacy versus localization accuracy. The findings suggest the approach is viable for next-generation vehicular networks to provide real-time security and diagnostics while preserving vehicle privacy, albeit with a trade-off where higher localization precision demands larger payloads.

Abstract

In Vehicle-to-Everything (V2X) networks that involve multi-hop communication, the Road Side Units (RSUs) typically desire to gather the location information of the participating vehicles to provide security and network-diagnostics features. Although Global Positioning System (GPS) based localization is widely used by vehicles for navigation; they may not forward their exact GPS coordinates to the RSUs due to privacy issues. Therefore, to balance the high-localization requirements of RSU and the privacy of the vehicles, we demonstrate a new spatial-provenance framework wherein the vehicles agree to compromise their privacy to a certain extent and share a low-precision variant of its coordinates in agreement with the demands of the RSU. To study the deployment feasibility of the proposed framework in state-of-the-art wireless standards, we propose a testbed of ZigBee and LoRa devices and implement the underlying protocols on their stack using correlated Bloom filters and Rake compression algorithms. Our demonstrations reveal that low-to-moderate precision localization can be achieved in fewer packets, thus making an appealing case for next-generation vehicular networks to include our methods for providing real-time security and network-diagnostics features.
Paper Structure (8 sections, 4 figures, 1 table)

This paper contains 8 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Importance of Spatial-Provenance
  3. Privacy Issues with Spatial-Provenance
  4. Impact of this Testbed
  5. Testbed Setup for Spatial-provenance
  6. Hardware Setup
  7. Message Flow for Spatial-provenance
  8. Takeaways from our Testbed

Figures (4)

  • Figure 1: A depiction of area fragmentation wherein the coverage area of an RSU has been divided into 5 segments, and the vehicles are asked to reveal the identity of their segments instead of their exact location.
  • Figure 2: Depiction of general structure of a network packet, where H. implies Header. In our testbed, part of the payload will be used to convey the spatial-provenance information.
  • Figure 3: Testbed comprising XBee and LoRa devices to demonstrate a static vehicular network.
  • Figure 4: Flow of messages between RSU and nodes for conveying spatial-provenance in a two-hop network.