An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence
arXiv 2605.22824•b6369668be04fffcefdac764a32b74c3b1b759ab86be5f1f73ea0d3da105912f
CephDAOSHPCIoTJAX-checkpointing','Orbax'KPI extractionKV-cacheLLMObjectCacheRISC-VSophon-SG2044TinyMLchilling-effect-consensusconsensusdecentralised-systemsdouble-spend-preventioneclipse-attackedge-computingenergy-efficiencymulti-agent-workflowobject-storageprivacyproof-of-corruptionsmart-citiesverifiable-random-function
Paper metadata
- arXiv ID
- 2605.22824
- 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
- b6369668be04fffcefdac764a32b74c3b1b759ab86be5f1f73ea0d3da105912f
- Enrichment time
- 2026-05-25T08:52:26Z
- AI-assisted enrichment
- Yes
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