{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3RHKPWU56ADN7DB6XSG5MP6GDP","short_pith_number":"pith:3RHKPWU5","schema_version":"1.0","canonical_sha256":"dc4ea7da9df006df8c3ebc8dd63fc61bf8991be4576aee32f15ddd42b203b470","source":{"kind":"arxiv","id":"1711.10610","version":1},"attestation_state":"computed","paper":{"title":"Optimal Dynamic Sensor Subset Selection for Tracking a Time-Varying Stochastic Process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","math.OC"],"primary_cat":"cs.SY","authors_text":"Arpan Chattopadhyay, Urbashi Mitra","submitted_at":"2017-11-28T23:19:10Z","abstract_excerpt":"Motivated by the Internet-of-things and sensor networks for cyberphysical systems, the problem of dynamic sensor activation for the tracking of a time-varying process is examined. The tradeoff is between energy efficiency, which decreases with the number of active sensors, and fidelity, which increases with the number of active sensors. The problem of minimizing the time-averaged mean-squared error over infinite horizon is examined under the constraint of the mean number of active sensors. The proposed methods artfully combine three key ingredients: Gibbs sampling, stochastic approximation for"},"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":"1711.10610","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-11-28T23:19:10Z","cross_cats_sorted":["cs.NI","math.OC"],"title_canon_sha256":"0ba435d2f21afb9ac4f397d3c06f8c2f41688e36140258a153aad3aeed80c7ef","abstract_canon_sha256":"ae3488c042c9682757419a2674ee7f9c5086f8028831117da8248055f0091d19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:17.572235Z","signature_b64":"cm4HQ8D5kC+Du2D7fZjW0WzRYaJO/coFIvbtZ2gYC3Ods1vE8e9LzsVOITbfWj22EfXwij1vOn46dvWBdtn2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc4ea7da9df006df8c3ebc8dd63fc61bf8991be4576aee32f15ddd42b203b470","last_reissued_at":"2026-05-18T00:29:17.571557Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:17.571557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Dynamic Sensor Subset Selection for Tracking a Time-Varying Stochastic Process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","math.OC"],"primary_cat":"cs.SY","authors_text":"Arpan Chattopadhyay, Urbashi Mitra","submitted_at":"2017-11-28T23:19:10Z","abstract_excerpt":"Motivated by the Internet-of-things and sensor networks for cyberphysical systems, the problem of dynamic sensor activation for the tracking of a time-varying process is examined. The tradeoff is between energy efficiency, which decreases with the number of active sensors, and fidelity, which increases with the number of active sensors. The problem of minimizing the time-averaged mean-squared error over infinite horizon is examined under the constraint of the mean number of active sensors. The proposed methods artfully combine three key ingredients: Gibbs sampling, stochastic approximation for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10610","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":"1711.10610","created_at":"2026-05-18T00:29:17.571648+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.10610v1","created_at":"2026-05-18T00:29:17.571648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10610","created_at":"2026-05-18T00:29:17.571648+00:00"},{"alias_kind":"pith_short_12","alias_value":"3RHKPWU56ADN","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3RHKPWU56ADN7DB6","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3RHKPWU5","created_at":"2026-05-18T12:30:58.224056+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/3RHKPWU56ADN7DB6XSG5MP6GDP","json":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP.json","graph_json":"https://pith.science/api/pith-number/3RHKPWU56ADN7DB6XSG5MP6GDP/graph.json","events_json":"https://pith.science/api/pith-number/3RHKPWU56ADN7DB6XSG5MP6GDP/events.json","paper":"https://pith.science/paper/3RHKPWU5"},"agent_actions":{"view_html":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP","download_json":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP.json","view_paper":"https://pith.science/paper/3RHKPWU5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.10610&json=true","fetch_graph":"https://pith.science/api/pith-number/3RHKPWU56ADN7DB6XSG5MP6GDP/graph.json","fetch_events":"https://pith.science/api/pith-number/3RHKPWU56ADN7DB6XSG5MP6GDP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP/action/storage_attestation","attest_author":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP/action/author_attestation","sign_citation":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP/action/citation_signature","submit_replication":"https://pith.science/pith/3RHKPWU56ADN7DB6XSG5MP6GDP/action/replication_record"}},"created_at":"2026-05-18T00:29:17.571648+00:00","updated_at":"2026-05-18T00:29:17.571648+00:00"}