SUSTAINABLE EDGE COMPUTING - ENABLING GREEN AND INTELLIGENT FUTURE TECHNOLOGIES
持続可能なエッジコンピューティング - グリーンでインテリジェントな未来技術を実現する (AI 翻訳)
Lija Mishra
🤖 gxceed AI 要約
日本語
本論文は、IoTとAIの普及に伴うデータセンターのエネルギー消費増加に対し、再生可能エネルギーとAI駆動のタスクスケジューリングを統合した持続可能なエッジコンピューティングフレームワークを提案。数値解析により、クラウド比60-99%のエネルギー削減を実証し、スマートシティやヘルスケア等の応用で有効性を示した。
English
This paper proposes a sustainable edge computing framework integrating renewable energy-powered nodes and AI-driven task scheduling to address the high energy consumption of cloud data centers. Mathematical models and numerical analysis show energy savings of 60-99% compared to cloud systems, validated in smart city, healthcare, agriculture, and industrial applications.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は2030年までのデータセンター消費電力を46%削減する目標を掲げており、本フレームワークは再生可能エネルギー活用とAI最適化により日本のGX政策に直結する。特にエネルギー高騰とカーボンニュートラル達成に向け、迅速な実用化が期待される。
In the global GX context
Globally, data centers account for ~1% of energy-related CO2 emissions. This framework offers a concrete pathway to decarbonize the ICT sector by shifting computation to edge nodes powered by renewables, aligning with TCFD and ISSB recommendations for reducing Scope 2 emissions.
👥 読者別の含意
🔬研究者:Provides mathematical models for energy-latency trade-offs in sustainable edge computing, useful for further optimization research.
🏢実務担当者:Offers a deployable framework to reduce data center energy costs and carbon footprint, applicable to industries with IoT workloads.
🏛政策担当者:Highlights the potential of edge computing as a policy lever for national energy efficiency and emission reduction targets.
📄 Abstract（原文）
The rapid proliferation of Internet of Things (IoT) devices, artificial intelligence (AI), and data intensive applications has significantly increased the energy demands of conventional cloud computing systems, resulting in higher carbon emissions and operational inefficiencies. Sustainable computing aims to minimize energy consumption and environmental impact; however, traditional cloud infrastructures rely heavily on energy-intensive data centres powered by non-renewable sources.To address these challenges, this paper proposes a sustainable edge computing framework that enables localized data processing, thereby reducing latency, bandwidth usage, and energy consumption. The framework integrates renewable energy-powered edge nodes and AI-driven task scheduling to optimize system performance and resource utilization. Mathematical models for energy consumption, latency, and transmission are developed and validated through numerical analysis, demonstrating energy savings of up to 60 - 99% compared to cloud-based systems. Secure data storage and retrieval mechanisms are also incorporated to ensure data reliability and privacyThe proposed approach is evaluated across real-world applications, including smart cities, healthcare,agriculture, and industrial systems.The results confirm that sustainable edge computing significantly enhances energy efficiency and reduces carbon emissions, making it a key enabler for environmentally responsible and intelligent future technologies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.21474/ijar01/23316first seen 2026-06-10 05:27:14
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