{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DJ6XDGOSNKEQJAIKENMDPLKFET","short_pith_number":"pith:DJ6XDGOS","schema_version":"1.0","canonical_sha256":"1a7d7199d26a8904810a235837ad4524e31a13355e254bb6655ac65480669641","source":{"kind":"arxiv","id":"1904.07623","version":1},"attestation_state":"computed","paper":{"title":"DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Amani Al-Shawabka, Francesco Restuccia, Kaushik Chowdhury, Luca Angioloni, Mauro Belgiovine, Salvatore D'Oro, Stratis Ioannidis, Tommaso Melodia","submitted_at":"2019-04-16T12:33:56Z","abstract_excerpt":"Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy. By mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard. One of the most crucial challenges in radio fingerprinting is to counteract the action of the wireless channel, which decreases fingerprinting accuracy significantly by disrupting hardware impairments. On the other hand, due to their sheer size, deep learning algorithms are hardly re-trainable in real-time. Another aspect that is yet"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.07623","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2019-04-16T12:33:56Z","cross_cats_sorted":[],"title_canon_sha256":"6850d4aaec0af9d05c1e50db02e6b62300c98b0433496ee600ee5b7ee2f64d54","abstract_canon_sha256":"020499293e427fdbca385456dff359203eea2985485c1246776e2aabb913e7d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:24.674341Z","signature_b64":"wt1f54/VAsWkITzUCvMwTdQ+n4bgtFZJGID+EroLlErkbfwVg3+ZrMH1eZNglJQjpwQwpue84dHjdX1rkeltBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a7d7199d26a8904810a235837ad4524e31a13355e254bb6655ac65480669641","last_reissued_at":"2026-05-17T23:48:24.673780Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:24.673780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Amani Al-Shawabka, Francesco Restuccia, Kaushik Chowdhury, Luca Angioloni, Mauro Belgiovine, Salvatore D'Oro, Stratis Ioannidis, Tommaso Melodia","submitted_at":"2019-04-16T12:33:56Z","abstract_excerpt":"Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy. By mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard. One of the most crucial challenges in radio fingerprinting is to counteract the action of the wireless channel, which decreases fingerprinting accuracy significantly by disrupting hardware impairments. On the other hand, due to their sheer size, deep learning algorithms are hardly re-trainable in real-time. Another aspect that is yet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.07623","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.07623","created_at":"2026-05-17T23:48:24.673874+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.07623v1","created_at":"2026-05-17T23:48:24.673874+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.07623","created_at":"2026-05-17T23:48:24.673874+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJ6XDGOSNKEQ","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJ6XDGOSNKEQJAIK","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJ6XDGOS","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET","json":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET.json","graph_json":"https://pith.science/api/pith-number/DJ6XDGOSNKEQJAIKENMDPLKFET/graph.json","events_json":"https://pith.science/api/pith-number/DJ6XDGOSNKEQJAIKENMDPLKFET/events.json","paper":"https://pith.science/paper/DJ6XDGOS"},"agent_actions":{"view_html":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET","download_json":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET.json","view_paper":"https://pith.science/paper/DJ6XDGOS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.07623&json=true","fetch_graph":"https://pith.science/api/pith-number/DJ6XDGOSNKEQJAIKENMDPLKFET/graph.json","fetch_events":"https://pith.science/api/pith-number/DJ6XDGOSNKEQJAIKENMDPLKFET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET/action/storage_attestation","attest_author":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET/action/author_attestation","sign_citation":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET/action/citation_signature","submit_replication":"https://pith.science/pith/DJ6XDGOSNKEQJAIKENMDPLKFET/action/replication_record"}},"created_at":"2026-05-17T23:48:24.673874+00:00","updated_at":"2026-05-17T23:48:24.673874+00:00"}