{"schema_version":"0.1","map_id":"paper-22-map","publication_id":22,"publication_anchor":"paper-22","slug":"paper-22","canonical_path":"/knowledge/papers/paper-22/","machine_path":"/knowledge/papers/paper-22.json","root_node_id":"paper-22","stage":"mapped_draft","contribution_type_vocabulary_version":"0.1","contribution_types":["algorithm"],"title":"Neighborhood Watch: On Network Coding Throughput and Key Sharing","year":2013,"status":"Published","venue":"IEEE Global Communications Conference (GLOBECOM)","topic":"algorithms-foundations","labels":["Theory","Applied"],"authors":["Martin Strohmeier","Ivan Martinovic","Utz Roedig","Karim Eldefrawy","Jens Schmitt"],"keywords":["network coding","key sharing","wireless networks"],"research_question":"How does limiting the size and availability of shared link-layer keys change the multicast throughput attainable by network coding, and how should key groups be assigned to optimize throughput under local, global, and quality-of-service constraints?","central_answer":"The paper formulates key-aware multicast as an integer linear program and evaluates exact optima on generated feed-forward networks and a MoteLab-derived sensor topology, showing that modest increases in key-group size can recover measurable coding gain without requiring one network-wide key.","curation":{"drafted_at":"2026-07-11","drafted_by":[{"actor_type":"ai","name":"OpenAI Codex","role":"full-text extraction, model reconstruction, evidence linking, and initial assessment"}],"method":"Source-grounded review of the complete six-page Oxford author copy, including the ILP, experimental setup, figures, and limitations; PDF pages 1 and 5 were rendered and visually inspected.","source_scope":"full_source_audit","approval":{"status":"pending","note":"AI-authored source-linked map awaiting author verification. Numerical results were transcribed from the paper and not independently rerun."}},"sources":[{"id":"source-paper-22-fulltext","type":"scholarly_article","title":"Neighborhood Watch: On Network Coding Throughput and Key Sharing (author copy)","url":"/pubs/2013/neighborhood-watch-network-coding-key-sharing.pdf","media_type":"application/pdf","sha256":"cc7cd150c46044a76903f9772727f771072a23ed102b6d8a2a42a07f551f75f6","page_count":6,"provenance_category":"author"},{"id":"source-paper-22-author-origin","type":"author_copy","title":"Oxford author copy","url":"https://www.cs.ox.ac.uk/files/7240/globecom_camera.pdf"},{"id":"source-paper-22-official","type":"publication_record","title":"IEEE GLOBECOM publication record","url":"https://doi.org/10.1109/GLOCOM.2013.6831179"},{"id":"source-paper-22-citations","type":"scholarly_index","title":"OpenAlex work record for paper #22","url":"https://openalex.org/W2074968496","accessed_at":"2026-07-11"}],"source_anchors":[{"id":"anchor-paper-22-problem","source_id":"source-paper-22-fulltext","label":"Security-throughput tradeoff and contributions","locator":"Abstract and Section I, PDF pages 1-2","url":"/pubs/2013/neighborhood-watch-network-coding-key-sharing.pdf#page=1"},{"id":"anchor-paper-22-scenarios","source_id":"source-paper-22-fulltext","label":"Network-coding scenarios and experimental topology generation","locator":"Section III, PDF pages 2-3","url":"/pubs/2013/neighborhood-watch-network-coding-key-sharing.pdf#page=2"},{"id":"anchor-paper-22-model","source_id":"source-paper-22-fulltext","label":"Directed-graph model, objective, and local/global key constraints","locator":"Section IV and Tables I-II, PDF pages 3-4","url":"/pubs/2013/neighborhood-watch-network-coding-key-sharing.pdf#page=3"},{"id":"anchor-paper-22-evaluation","source_id":"source-paper-22-fulltext","label":"Random-network and MoteLab results","locator":"Section V and Figures 3-5, PDF pages 4-6","url":"/pubs/2013/neighborhood-watch-network-coding-key-sharing.pdf#page=4"},{"id":"anchor-paper-22-conclusion","source_id":"source-paper-22-fulltext","label":"Interpretation, transfer boundaries, and conclusion","locator":"Section VI, PDF page 6","url":"/pubs/2013/neighborhood-watch-network-coding-key-sharing.pdf#page=6"},{"id":"anchor-paper-22-publication","source_id":"source-paper-22-official","label":"Official IEEE publication record","locator":"GLOBECOM 2013","url":"https://doi.org/10.1109/GLOCOM.2013.6831179"},{"id":"anchor-paper-22-citations","source_id":"source-paper-22-citations","label":"Dated OpenAlex citation snapshot","locator":"cited_by_count = 0, accessed 2026-07-11","url":"https://openalex.org/W2074968496"}],"nodes":[{"id":"paper-22","kind":"paper","parent_id":null,"order":1,"epistemic_status":"published","title":"Neighborhood Watch","summary":"An optimization-and-evaluation study of how link-key sharing constrains the throughput gains available from wireless network coding.","source_anchor_ids":["anchor-paper-22-problem"]},{"id":"paper-22-question","kind":"question","parent_id":"paper-22","order":1,"epistemic_status":"research_question","title":"Research question","summary":"What throughput is lost when confidentiality policy limits which neighbors can overhear coded packets, and which key assignment best preserves multicast capacity?","source_anchor_ids":["anchor-paper-22-problem"]},{"id":"paper-22-answer","kind":"contribution","parent_id":"paper-22","order":2,"epistemic_status":"source_asserted","title":"Central answer","summary":"Optimize flows and key membership jointly: the exact ILP quantifies the tradeoff, and the evaluated topologies show that coding gain can reappear well before all nodes share one key.","source_anchor_ids":["anchor-paper-22-model","anchor-paper-22-evaluation"]},{"id":"paper-22-scope","kind":"scope","parent_id":"paper-22","order":3,"epistemic_status":"explicitly_scoped","title":"Model and optimization scope","summary":"The study treats single-source multicast in directed multi-hop wireless graphs with unit-capacity links, multiple streams, relay and sink nodes, and linear network coding.","source_anchor_ids":["anchor-paper-22-model"]},{"id":"paper-22-scope-keys","kind":"definition","parent_id":"paper-22-scope","order":1,"epistemic_status":"defined","title":"Two key-sharing constraints","summary":"A local limit bounds how many adjacent receivers share a sender's key; a global limit bounds the number and membership of keys available across the network.","source_anchor_ids":["anchor-paper-22-problem","anchor-paper-22-model"]},{"id":"paper-22-scope-security","kind":"threat_model","parent_id":"paper-22-scope","order":2,"epistemic_status":"coarse_model","title":"Security proxy","summary":"Security is represented by compromise exposure of shared link keys: smaller groups isolate a leaked key but reduce overhearing. The paper does not model an active attacker or prove a cryptographic security property.","source_anchor_ids":["anchor-paper-22-problem"]},{"id":"paper-22-method","kind":"method","parent_id":"paper-22","order":4,"epistemic_status":"specified","title":"Integer linear program","summary":"Binary flow and key-membership variables maximize aggregate sink flow while enforcing flow conservation, coding capacity, key-sharing eligibility, and optional source/sink quality-of-service constraints.","source_anchor_ids":["anchor-paper-22-model"]},{"id":"paper-22-method-data","kind":"component","parent_id":"paper-22-method","order":1,"epistemic_status":"documented","title":"Evaluated topologies","summary":"The authors solve the model on 100 randomized four-layer feed-forward graphs and on a feed-forward topology derived from Harvard's MoteLab deployment with four sources, 18 relays, and eight sinks.","source_anchor_ids":["anchor-paper-22-scenarios","anchor-paper-22-evaluation"]},{"id":"paper-22-claims","kind":"claim_group","parent_id":"paper-22","order":5,"epistemic_status":"empirically_supported_within_model","title":"Principal findings","summary":"The reported gains are exact ILP optima for the modeled instances, not measurements from a deployed coded network.","source_anchor_ids":["anchor-paper-22-evaluation"]},{"id":"paper-22-claim-random","kind":"claim","parent_id":"paper-22-claims","order":1,"epistemic_status":"computational_experiment","title":"Random-network coding gain","summary":"Across 100 generated networks, unrestricted coding gain averages 7.1%; 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unit link capacities and feed-forward structure simplify deployment reality.","source_anchor_ids":["anchor-paper-22-model","anchor-paper-22-conclusion"]},{"id":"paper-22-boundary-evidence","kind":"limitation","parent_id":"paper-22-boundaries","order":2,"epistemic_status":"evaluation_limitation","title":"Optimization, not field measurement","summary":"The MoteLab case uses a derived connectivity map, and the paper reports solver outcomes rather than packet-level network-coding deployment measurements.","source_anchor_ids":["anchor-paper-22-scenarios","anchor-paper-22-evaluation"]},{"id":"paper-22-artifacts","kind":"artifact_group","parent_id":"paper-22","order":8,"epistemic_status":"full_text_available","title":"Artifacts and resources","summary":"The six-page author copy is mirrored locally with fixity metadata and linked to the Oxford origin and IEEE DOI; no code or dataset artifact was identified.","source_anchor_ids":["anchor-paper-22-problem","anchor-paper-22-publication"]},{"id":"paper-22-scrutiny","kind":"scrutiny","parent_id":"paper-22","order":9,"epistemic_status":"publication_recorded","title":"External scrutiny","summary":"The work was published at IEEE GLOBECOM; 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