Predictive RTO for CoAP using Lightweight Support Vector Regression in Internet of Things
arXiv 2607.18273•1e4648ae06b3011962c5837d41ec2178e358c40b3738b1857187e8a8bc13e413
ACEAPEXCoAPDoSGPUH100IoTLLM cold start','vLLM','InstantInfer'LZ77Mixture-of-ExpertsMoENoCRTOadversarial MLavailabilitycache coherencecoherence-aware mappingcompressiondeployment realismmodel poisoningon-device MLoptimizerrandom forestside-channelsupport vector regressiontask mapping
Paper metadata
- arXiv ID
- 2607.18273
- Version
- Not specified by this published record
- Category
- Computer Science — Distributed, Parallel, and Cluster Computing (cs.DC)
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Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- 1e4648ae06b3011962c5837d41ec2178e358c40b3738b1857187e8a8bc13e413
- Enrichment time
- 2026-07-22T08:52:31Z
- AI-assisted enrichment
- Yes
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