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Edge-IoT 2020: EAI International Conference on Intelligent Edge Processing in the IoT Era

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Edge-IoT
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投稿締切日:
2020-06-22
通知日:
2020-08-03
開催日:
2020-12-02
開催地:
Viana do Castelo, Portugal
開催回数:
1
閲覧: 11805   フォロー: 0   参加: 0

会伴インデックス (CP-I)

35.8 / 100
全 5,658 件中 第 5,586 位 · 上位 99%

ネットワーク・通信 分野 862 件中 第 852 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
19
コミュニティの注目度 (10%)
8
公開情報の充実度 (15%)
25

使用した入力: 確認できる開催回数：1 · 過去 24 か月にこのページを開いた研究者：2 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
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信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-10

論文募集

Edge-IoT 2020 (EAI International Conference on Intelligent Edge Processing in the IoT Era) is an academic conference held in Viana do Castelo, Portugal on 2020-12-02. The paper submission deadline is 2020-06-22. Acceptance notifications are sent on 2020-08-03.

Scope The 1st EAI International Conference on Intelligent Edge Processing for the IoT Era (Edge-IoT 2020) will be held on December 2-4, 2020 in Viana do Castelo, Portugal. Edge-IoT 2020 conference has been created as a flagship conference aiming at addressing the decentralization of contemporary processing paradigms, notably Edge processing, focusing on the increasing demand for intelligent processing at the edge of the network, which is paving the way to the Intelligent IoT Era. The Internet of Things (IoT) is taking an important role in the digital transformation process in several application domains blurring the boundaries between the physical and digital worlds. Traditionally, the data produced by these IoT devices is relayed back to a centralized computing system, also known as a cloud-centered computing paradigm. In this approach, data is centrally processed at the cloud and its results are sent back to the IoT devices, that run on the edge of the network. Since IoT edge devices are becoming more compact, energy-efficient, affordable and incorporate increasing computational power, Intelligent Edge Processing can be used for distributed processing that will take place near the physical location of the data source, i.e. at the network edge, enabling real-time processing with low-latency (allowing a fast and more effective response), and the reduction of the data dimension (integration and aggregation of several data sources collected at the edge), to generate dynamic and structured smart data streams. This approach contributes to the reduction of the computational load in the fog/cloud computing layers and therefore to the reduction of the bandwidth of the communication. Topics Relevant topics include, but are not limited to: Architectures, techniques, and applications of intelligent edge/fog processing for the IoT; Advanced machine learning and intelligent edge and fog processing for the IoT; Security and Privacy in Mobile Edge Computing Quality of Service, energy efficiency in Mobile Edge Computing; Security and privacy in edge and fog computing; AI-enabled edge and fog computing architectures, frameworks, and protocols for IoT; Advanced edge and fog signal processing for the IoT; Cognitive IoT and edge and fog intelligence; Energy-awareness and sustainability at the edge and fog: metrics and measurements; Energy and security-aware edge and fog computing solutions for the IoT; Development of intelligent edge and fog computing platforms; Intelligent and network-aware edge and fog applications; Edge solutions towards 5G integration; Smart applications of edge and fog computing: Smart Cities, Smart Buildings, etc; Novel applications, and case studies with edge and fog computing for IoT; IoT and Edge Oriented Software Development Processes and Quality Metrics
最終更新：Dou Sun（）

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CCF正式名称インパクトファクター出版社ISSN
CIEEE Internet of Things Journal8.9IEEE2327-4662
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CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

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