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

• URL: http://arxiv.org/abs/2401.06638v1
• Date: Fri, 12 Jan 2024 15:36:28 GMT
• Title: A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks
• Authors: Manish Bansal, Pramsu Shrivastava, J. Harshan,
• Abstract summary: In Vehicle-to-Everything (V2X) networks, Road Side Units (RSUs) typically desire to gather the location information of the participating vehicles to provide security and network-diagnostics features. We propose 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. 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-
• Score: 0.732582506267845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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