{"id":"W2154601749","doi":"10.1109/percomw.2005.8","title":"A Lightweight Service Discovery Mechanism for Mobile Ad Hoc Pervasive Environment Using Cross-Layer Design","year":2005,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Service discovery; Computer science; Computer network; Wireless ad hoc network; Mobile ad hoc network; Vehicular ad hoc network; Scalability; Overhead (engineering); Distributed computing; Service layer; Ad hoc wireless distribution service; Service (business); Optimized Link State Routing Protocol; Adaptive quality of service multi-hop routing; Mechanism (biology); Layer (electronics); Network layer; Routing (electronic design automation); Routing protocol; Quality of service; Web service; Telecommunications; Wireless; Database; World Wide Web; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005054916,0.001025206,0.001056939,0.001530239,0.001132541,0.002682673,0.003638014,0.001865243,0.001550975],"category_scores_gemma":[0.005140469,0.0009905952,0.001153765,0.0007907049,0.0009838111,0.00404454,0.003025157,0.003031006,0.001048861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000751446,"about_ca_system_score_gemma":0.001193726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008221216,"about_ca_topic_score_gemma":0.0006513834,"domain_scores_codex":[0.9972754,0.0005567847,0.0004208487,0.0003574695,0.001135242,0.0002543107],"domain_scores_gemma":[0.9969227,0.0007516548,0.0004177883,0.0009255763,0.0007171526,0.0002652438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007558457,0.0006565261,0.002467441,0.001441155,0.0005280469,0.001366128,0.001363046,0.03078992,0.2071158,0.221103,0.008751818,0.5236612],"study_design_scores_gemma":[0.0003297483,0.00127192,0.001276957,0.0002019272,0.0006559059,0.003369391,0.0001671236,0.6942627,0.1422754,0.04830999,0.1074656,0.0004133535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004193855,0.000263005,0.9921262,0.0001146666,0.00009411283,0.0001201807,0.00001367949,0.002445847,0.0006282706],"genre_scores_gemma":[0.1985842,0.000641426,0.7950505,0.0004533771,0.0002285998,0.0004985486,0.0002171733,0.0003562784,0.003969898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005054916,"threshold_uncertainty_score":0.02673328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03236000064353097,"score_gpt":0.2651497703232261,"score_spread":0.2327897696796951,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}