{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:JDNTICI2PTN6QXUDQTYM4Y35PC","short_pith_number":"pith:JDNTICI2","schema_version":"1.0","canonical_sha256":"48db34091a7cdbe85e8384f0ce637d788c3f35a8042bd8c3f49c8f5fc5116743","source":{"kind":"arxiv","id":"1510.05324","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.SY","authors_text":"H. V. Poor, R. C. de Lamare, S. Xu","submitted_at":"2015-10-18T22:52:25Z","abstract_excerpt":"This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search--based least--mean--squares(LMS)/recursive least squares(RLS) link selection algorithms and sparsity--inspired LMS/RLS link selection algorithms that can exploit the topology of networks with poor--quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady--state and tracking performance, and computational complexity. In comparison with exist"},"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":"1510.05324","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-10-18T22:52:25Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"d6133d4c40211b4cb113b52d3dd394ee52f5ef82123c398181313bdca62325d9","abstract_canon_sha256":"4c18eeb4a7f90cc36abc1813ab43e079d8402cf324af3a867e68da331836f745"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:29:52.545806Z","signature_b64":"M7qml82cOqT4XxRp17TATDk61d2ThhwWzfo8N2v5zTSKYWeuBknC4iCwcR7QgFG06ieRic/RPEMw0CHsTrBxBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48db34091a7cdbe85e8384f0ce637d788c3f35a8042bd8c3f49c8f5fc5116743","last_reissued_at":"2026-05-18T01:29:52.545249Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:29:52.545249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.SY","authors_text":"H. V. Poor, R. C. de Lamare, S. Xu","submitted_at":"2015-10-18T22:52:25Z","abstract_excerpt":"This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search--based least--mean--squares(LMS)/recursive least squares(RLS) link selection algorithms and sparsity--inspired LMS/RLS link selection algorithms that can exploit the topology of networks with poor--quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady--state and tracking performance, and computational complexity. In comparison with exist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.05324","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":"1510.05324","created_at":"2026-05-18T01:29:52.545332+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.05324v1","created_at":"2026-05-18T01:29:52.545332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.05324","created_at":"2026-05-18T01:29:52.545332+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDNTICI2PTN6","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDNTICI2PTN6QXUD","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDNTICI2","created_at":"2026-05-18T12:29:27.538025+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/JDNTICI2PTN6QXUDQTYM4Y35PC","json":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC.json","graph_json":"https://pith.science/api/pith-number/JDNTICI2PTN6QXUDQTYM4Y35PC/graph.json","events_json":"https://pith.science/api/pith-number/JDNTICI2PTN6QXUDQTYM4Y35PC/events.json","paper":"https://pith.science/paper/JDNTICI2"},"agent_actions":{"view_html":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC","download_json":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC.json","view_paper":"https://pith.science/paper/JDNTICI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.05324&json=true","fetch_graph":"https://pith.science/api/pith-number/JDNTICI2PTN6QXUDQTYM4Y35PC/graph.json","fetch_events":"https://pith.science/api/pith-number/JDNTICI2PTN6QXUDQTYM4Y35PC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC/action/storage_attestation","attest_author":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC/action/author_attestation","sign_citation":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC/action/citation_signature","submit_replication":"https://pith.science/pith/JDNTICI2PTN6QXUDQTYM4Y35PC/action/replication_record"}},"created_at":"2026-05-18T01:29:52.545332+00:00","updated_at":"2026-05-18T01:29:52.545332+00:00"}