A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914639712878592 |
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| author | Bansal, Manish Shrivastava, Pramsu Harshan, J. |
| author_facet | Bansal, Manish Shrivastava, Pramsu Harshan, J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks Bansal, Manish Shrivastava, Pramsu Harshan, J. Networking and Internet Architecture Cryptography and Security 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. |
| title | A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks |
| topic | Networking and Internet Architecture Cryptography and Security |
| url | https://arxiv.org/abs/2401.06638 |