{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PCZWNZHJ7AMEBE5VNECYEMF2EH","short_pith_number":"pith:PCZWNZHJ","schema_version":"1.0","canonical_sha256":"78b366e4e9f8184093b569058230ba21f290bf3bb7fbffedac19192c377371a0","source":{"kind":"arxiv","id":"2403.19833","version":2},"attestation_state":"computed","paper":{"title":"ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Huacheng Zeng, Kunzhe Song, Qijun Wang, Shichen Zhang","submitted_at":"2024-03-28T21:04:11Z","abstract_excerpt":"Large language models (LLMs) have transformed the way we interact with cyber technologies. In this paper, we study the possibility of connecting LLM with wireless sensor networks (WSN). A successful design will not only extend LLM's knowledge landscape to the physical world but also revolutionize human interaction with WSN. To the end, we present ChatTracer, an LLM-powered real-time Bluetooth device tracking system. ChatTracer comprises three key components: an array of Bluetooth sniffing nodes, a database, and a fine-tuned LLM. ChatTracer was designed based on our experimental observation tha"},"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":"2403.19833","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-03-28T21:04:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aa5d3b3aa40b10c312714aa9ae5fb4d3a30b1fa0fbaa54bf7c62563e67d090c6","abstract_canon_sha256":"665f88922ad4bbf767e45cca0be5d89679434f2ed52555a0e3ef87d1b8cee79a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:44.265310Z","signature_b64":"QA1+ROBfx21rBccwvnGl7fZ2WiXi309CWcMGrubv9Cx2/+Cn8K1S0IZ+i/RZqOSSbnlW+3P6h+WTuq6yniJdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78b366e4e9f8184093b569058230ba21f290bf3bb7fbffedac19192c377371a0","last_reissued_at":"2026-07-05T08:41:44.264836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:44.264836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChatTracer: Large Language Model Powered Real-time Bluetooth Device Tracking System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Huacheng Zeng, Kunzhe Song, Qijun Wang, Shichen Zhang","submitted_at":"2024-03-28T21:04:11Z","abstract_excerpt":"Large language models (LLMs) have transformed the way we interact with cyber technologies. In this paper, we study the possibility of connecting LLM with wireless sensor networks (WSN). A successful design will not only extend LLM's knowledge landscape to the physical world but also revolutionize human interaction with WSN. To the end, we present ChatTracer, an LLM-powered real-time Bluetooth device tracking system. ChatTracer comprises three key components: an array of Bluetooth sniffing nodes, a database, and a fine-tuned LLM. ChatTracer was designed based on our experimental observation tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19833","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.19833/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2403.19833","created_at":"2026-07-05T08:41:44.264901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.19833v2","created_at":"2026-07-05T08:41:44.264901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19833","created_at":"2026-07-05T08:41:44.264901+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCZWNZHJ7AME","created_at":"2026-07-05T08:41:44.264901+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCZWNZHJ7AMEBE5V","created_at":"2026-07-05T08:41:44.264901+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCZWNZHJ","created_at":"2026-07-05T08:41:44.264901+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2401.05459","citing_title":"Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security","ref_index":166,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH","json":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH.json","graph_json":"https://pith.science/api/pith-number/PCZWNZHJ7AMEBE5VNECYEMF2EH/graph.json","events_json":"https://pith.science/api/pith-number/PCZWNZHJ7AMEBE5VNECYEMF2EH/events.json","paper":"https://pith.science/paper/PCZWNZHJ"},"agent_actions":{"view_html":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH","download_json":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH.json","view_paper":"https://pith.science/paper/PCZWNZHJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.19833&json=true","fetch_graph":"https://pith.science/api/pith-number/PCZWNZHJ7AMEBE5VNECYEMF2EH/graph.json","fetch_events":"https://pith.science/api/pith-number/PCZWNZHJ7AMEBE5VNECYEMF2EH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH/action/storage_attestation","attest_author":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH/action/author_attestation","sign_citation":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH/action/citation_signature","submit_replication":"https://pith.science/pith/PCZWNZHJ7AMEBE5VNECYEMF2EH/action/replication_record"}},"created_at":"2026-07-05T08:41:44.264901+00:00","updated_at":"2026-07-05T08:41:44.264901+00:00"}