An AI-Driven Framework for Energy-Efficient Environmental Monitoring in Smart Cities Using Edge Intelligence
2026-05-25T08:52:26Z•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
What happened
This feed contains multiple arXiv preprints (May 25, 2026) across distributed systems, edge/IoT, ML systems, storage, consensus, and HPC. Highlights include: an AI-driven, TinyML-enabled, context-aware framework for energy-efficient environmental sensing (edge activation/utility functions); KPI2KVI, a deterministic multi-agent LLM workflow for extracting and computing Key Value Indicators from service descriptions; Intercloud, a decentralised economic network with a novel “chilling-effect” consensus, VRF-assigned Watcher swarms, and self-certifying Proofs of Corruption to prevent double-spends
Why it matters
A reviewed impact interpretation has not been published for this record.
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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