{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZPUBXLS32OMC7UY3JMUFKUKQOU","short_pith_number":"pith:ZPUBXLS3","schema_version":"1.0","canonical_sha256":"cbe81bae5bd3982fd31b4b28555150752d2d3cdba0de5a80718286b4f852c3d3","source":{"kind":"arxiv","id":"1805.08525","version":1},"attestation_state":"computed","paper":{"title":"Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.SI","authors_text":"Daqing Zhang, Feng Wang, Jiangtao Wang, Leye Wang, Yasha Wang, Zhaopeng Qiu","submitted_at":"2018-05-22T11:57:38Z","abstract_excerpt":"Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influenced propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and F"},"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":"1805.08525","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-22T11:57:38Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"655dfba17f2971ca6d7079487ab904d9b61703f1c161d3b9b7108064a6749772","abstract_canon_sha256":"e569bcc28bab7592aad54e855386602709d2c7c389dcf8cfc1295a85815ad7b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:26.169367Z","signature_b64":"6NbXkg5hLDRN6zIm6lUj58ujMAu5Md+9Le0zRBdPbIqshRnephiuq/Py5lEhphkuCiSc2uKQT2aMpg1CgkDpAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cbe81bae5bd3982fd31b4b28555150752d2d3cdba0de5a80718286b4f852c3d3","last_reissued_at":"2026-05-18T00:15:26.168650Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:26.168650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.SI","authors_text":"Daqing Zhang, Feng Wang, Jiangtao Wang, Leye Wang, Yasha Wang, Zhaopeng Qiu","submitted_at":"2018-05-22T11:57:38Z","abstract_excerpt":"Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influenced propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08525","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":"1805.08525","created_at":"2026-05-18T00:15:26.168761+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.08525v1","created_at":"2026-05-18T00:15:26.168761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08525","created_at":"2026-05-18T00:15:26.168761+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZPUBXLS32OMC","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZPUBXLS32OMC7UY3","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZPUBXLS3","created_at":"2026-05-18T12:33:07.085635+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/ZPUBXLS32OMC7UY3JMUFKUKQOU","json":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU.json","graph_json":"https://pith.science/api/pith-number/ZPUBXLS32OMC7UY3JMUFKUKQOU/graph.json","events_json":"https://pith.science/api/pith-number/ZPUBXLS32OMC7UY3JMUFKUKQOU/events.json","paper":"https://pith.science/paper/ZPUBXLS3"},"agent_actions":{"view_html":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU","download_json":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU.json","view_paper":"https://pith.science/paper/ZPUBXLS3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.08525&json=true","fetch_graph":"https://pith.science/api/pith-number/ZPUBXLS32OMC7UY3JMUFKUKQOU/graph.json","fetch_events":"https://pith.science/api/pith-number/ZPUBXLS32OMC7UY3JMUFKUKQOU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU/action/storage_attestation","attest_author":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU/action/author_attestation","sign_citation":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU/action/citation_signature","submit_replication":"https://pith.science/pith/ZPUBXLS32OMC7UY3JMUFKUKQOU/action/replication_record"}},"created_at":"2026-05-18T00:15:26.168761+00:00","updated_at":"2026-05-18T00:15:26.168761+00:00"}