{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BKEGUWD4NKAK3KSUSJEFKU4565","short_pith_number":"pith:BKEGUWD4","schema_version":"1.0","canonical_sha256":"0a886a587c6a80adaa54924855539df746a30018566fbaa6997a31a70a9c7691","source":{"kind":"arxiv","id":"1811.11259","version":1},"attestation_state":"computed","paper":{"title":"Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DS","cs.SY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bharathan Balaji, Francesco Fraternali, Rajesh Gupta","submitted_at":"2018-11-27T21:05:43Z","abstract_excerpt":"With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose soluti"},"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":"1811.11259","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-27T21:05:43Z","cross_cats_sorted":["cs.AI","cs.DS","cs.SY","stat.ML"],"title_canon_sha256":"121a269fc8a08c621391df0b5ac6191ca46f69c2745db5ebb553058f7d8c2d33","abstract_canon_sha256":"768a53627e7734cf3d7ea82065b0076a23cbd8948aef7d59b036afa91dc697b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:44.342249Z","signature_b64":"W02ueusDbMRvyjaJ1ER03FaYDIGpRj+v5BtR3htxhH5ItyAb009NhvbvQ3M5veWAilDkstNN/7lwHe7ealsQBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a886a587c6a80adaa54924855539df746a30018566fbaa6997a31a70a9c7691","last_reissued_at":"2026-05-17T23:59:44.341557Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:44.341557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DS","cs.SY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bharathan Balaji, Francesco Fraternali, Rajesh Gupta","submitted_at":"2018-11-27T21:05:43Z","abstract_excerpt":"With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose soluti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11259","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":"1811.11259","created_at":"2026-05-17T23:59:44.341678+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.11259v1","created_at":"2026-05-17T23:59:44.341678+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11259","created_at":"2026-05-17T23:59:44.341678+00:00"},{"alias_kind":"pith_short_12","alias_value":"BKEGUWD4NKAK","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BKEGUWD4NKAK3KSU","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BKEGUWD4","created_at":"2026-05-18T12:32:16.446611+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/BKEGUWD4NKAK3KSUSJEFKU4565","json":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565.json","graph_json":"https://pith.science/api/pith-number/BKEGUWD4NKAK3KSUSJEFKU4565/graph.json","events_json":"https://pith.science/api/pith-number/BKEGUWD4NKAK3KSUSJEFKU4565/events.json","paper":"https://pith.science/paper/BKEGUWD4"},"agent_actions":{"view_html":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565","download_json":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565.json","view_paper":"https://pith.science/paper/BKEGUWD4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.11259&json=true","fetch_graph":"https://pith.science/api/pith-number/BKEGUWD4NKAK3KSUSJEFKU4565/graph.json","fetch_events":"https://pith.science/api/pith-number/BKEGUWD4NKAK3KSUSJEFKU4565/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565/action/storage_attestation","attest_author":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565/action/author_attestation","sign_citation":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565/action/citation_signature","submit_replication":"https://pith.science/pith/BKEGUWD4NKAK3KSUSJEFKU4565/action/replication_record"}},"created_at":"2026-05-17T23:59:44.341678+00:00","updated_at":"2026-05-17T23:59:44.341678+00:00"}