{"id":"W4391585333","doi":"10.1109/ssd58187.2023.10411182","title":"Adaptive Computation Offloading for Heterogeneous IoT Applications: An Experimental Study","year":2023,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Cloud computing; Energy consumption; Internet of Things; Edge computing; Enhanced Data Rates for GSM Evolution; Process (computing); Distributed computing; Efficient energy use; Computation offloading; Computation; Wireless sensor network; Edge device; Embedded system; Real-time computing; Computer network; 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":[0.0006160983,0.0005698139,0.0005769809,0.0004449401,0.00050684,0.0004822932,0.0007360943,0.0004934914,0.001437258],"category_scores_gemma":[0.001404329,0.0001388926,0.0003031566,0.0005872779,0.0005975995,0.0007198145,0.0005139479,0.000555835,0.0001711456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003810975,"about_ca_system_score_gemma":0.0003132199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089868,"about_ca_topic_score_gemma":0.0007710775,"domain_scores_codex":[0.9994622,0.00008085465,0.00004175566,0.0001179126,0.000137758,0.0001596301],"domain_scores_gemma":[0.9985393,0.0006042579,0.0001470867,0.0003132856,0.000229131,0.0001668307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005218592,0.01147561,0.009686382,0.000631778,0.0001947697,0.000957312,0.0004406857,0.09254284,0.8071131,0.002260104,0.00187264,0.06760619],"study_design_scores_gemma":[0.0006430268,0.01361236,0.01974578,0.00003505142,0.0001524745,0.0006605933,0.0007865497,0.4846156,0.4754824,0.00133911,0.002846604,0.00008066106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993262,0.0001065135,0.005297085,0.00003699903,0.00004119789,0.00008514401,0.00005375878,0.0001348492,0.0009824283],"genre_scores_gemma":[0.9946502,0.00007823002,0.004619801,0.00002392954,0.00001114428,0.00005686483,0.00008333016,0.00002289543,0.0004536654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001437258,"threshold_uncertainty_score":0.004808068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07274975503378003,"score_gpt":0.3467763441527694,"score_spread":0.2740265891189894,"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."}}