Cover Image
市場調查報告書

IoT (物聯網) 的邊緣分析

Edge Analytics in IoT

出版商 ABI Research 商品編碼 328235
出版日期 內容資訊 英文 17 Pages; 11 Tables; 4 Charts
商品交期: 最快1-2個工作天內
價格
如有價格方面的疑問請按下「詢問」鍵來信查詢
Back to Top
IoT (物聯網) 的邊緣分析 Edge Analytics in IoT
出版日期: 2015年04月13日 內容資訊: 英文 17 Pages; 11 Tables; 4 Charts
簡介

目前的IoT(物聯網)全球市場上的重要課題,是邊緣運算和雲端運算之間的平衡轉變。初期階段的IoT其先驅者M2M(機器間通訊),具有雲端平台作為應用之運行環境而扮演了很重要的角色這樣的特徵。IoT用智慧型系統大大依賴於雲端方面的技術大準,實際上內建各種裝置的設備本身則相對簡單。但現場(內部部署)的系統也已更新,邊緣級運算機能也比雲端級更急速進步。目前可說正在日漸從「連接裝置」轉換到「聟能裝置」。藉由活用邊緣運算,可微妙調整、選擇架構,引進IoT的各種企業已可利用新方法來改善物理型資產、加工流程。

本報告提供邊緣運算 (Edge Computing),尤其是邊緣分析 (Edge Analytics) 的概念與未來性市場趨勢分析,提供您IoT情報功能的3個階段,在其中邊緣分析在IoT資料分析上所達成的作用,邊緣分析的普及促進·阻礙因素,目前市場主要趨勢·趨勢,彙整IoT相關設備 (連網型·設備) 所產生·理解·轉送的資料數量預測,主要技術供應商的邊緣分析措施 (相關產品與服務·解決方案等) 等資訊。為您概述為以下內容。

第1章 邊緣分析的概要

  • 邊緣分析 (Edge Analytics) 為何?
  • IoT情報的3個水準
    • 端點智能
    • 網關智能
    • 雲端智能
  • 邊緣分析的促進·阻礙因素
    • 邊緣分析的推動因素
    • 雲端分析的推動因素
  • 近幾年趨勢與展望

第2章 IoT資料數量的市場預測

  • 分析方法
  • IoT資料的產生量
  • IoT資料的理解數量
  • IoT資料的傳輸數量

第3章 供應商環境

  • AGT International
  • Bit Stew Systems
  • Bright Wolf
  • Camgian Microsystems
  • Cisco
  • CyberLightning
  • Eurotech
  • Falkonry
  • Flowthings.io
  • Intel
  • Kepware Technologies
  • OSIsoft
  • Panduit
  • ParStream
  • PrismTech

圖表一覽

目錄
Product Code: AN-1914

In ABI Research's view, one of the most significant trends in the Internet of Things (or the connected world-the Internet of Everything-as a whole) is the shifting balance between edge computing and cloud computing. The early days of the IoT and its conceptual precursor, M2M, have been characterized by the critical role of cloud platforms as application enablers. Intelligent systems have largely relied on the cloud level for their intelligence, and the actual devices of which they consist have been relatively unsophisticated. This old premise is currently being shaken up, as the computing capabilities on the edge level advance faster than those of the cloud level. ABI Research refers to this trend as a paradigm shift-from the connected device paradigm to the intelligent device paradigm. This is, most of all, making the available architecture choices more nuanced and allowing organizations deploying the IoT to enhance their physical assets and processes in novel ways. Edge computing, also known as edge intelligence, is what is driving this shift.

This study explores edge computing specifically as an analytic proposition: as an approach to analyze data close to its source instead of sending it to a remote server for cloud-level analysis. The report builds on ABI Research's earlier work on IoT analytics, aiming to provide further insight and conceptual clarity on the role of the network edge in the IoT. Its first section summarizes the three levels of IoT intelligence, and provides commentary on the related drivers and inhibitors. Also, the most noteworthy market trends and observations are listed under this section. The second section, in the meantime, includes quantitative analyses of the "data universe" associated with the IoT: providing forecasts on the data volumes that are (a) generated, (b) captured, and (c) transmitted by connected devices. Finally, the report's third section serves as an overview of a number of technology vendors that are pioneering edge analytics with their products, services, and solutions.

Table of Contents

1. INTRODUCTION TO EDGE ANALYTICS

  • 1.1. What Is Edge Analytics?
  • 1.2. Three Levels of IoT Intelligence
    • 1.2.1. Endpoint Intelligence
    • 1.2.2. Gateway Intelligence
    • 1.2.3. Cloud Intelligence
  • 1.3. Drivers and Inhibitors for Edge Analytics
    • 1.3.1. Drivers for Edge Analytics
    • 1.3.2. Drivers for Cloud Analytics
  • 1.4. Recent Trends and Observations

2. MARKET FORECASTS FOR IOT DATA VOLUMES

  • 2.1. Methodology
  • 2.2. Volume of Generated IoT Data
  • 2.3. Volume of Captured IoT Data
  • 2.4. Volume of Transmitted IoT Data

3. VENDOR LANDSCAPE

  • 3.1. AGT International
  • 3.2. Bit Stew Systems
  • 3.3. Bright Wolf
  • 3.4. Camgian Microsystems
  • 3.5. Cisco
  • 3.6. CyberLightning
  • 3.7. Eurotech
  • 3.8. Falkonry
  • 3.9. Flowthings.io
  • 3.10. Intel
  • 3.11. Kepware Technologies
  • 3.12. OSIsoft
  • 3.13. Panduit
  • 3.14. ParStream
  • 3.15. PrismTech

Tables

  • 1. Size of IoT Data Universe by Phase of Data, World Market, Forecast: 2014 to 2020
  • 2. Volume of Generated IoT Data by Type of Data, World Market, Forecast: 2014 to 2020
  • 3. Volume of Generated IoT Data by Type of Data, World Market, Forecast: 2014 to 2020
  • 4. Volume of Captured Sensor and Machine Data by Application Segment, World Market, Forecast: 2014 to 2020
  • 5. IoT Backhaul Data Traffic by Application Segment: Sensor and Machine Data (Baseline Scenario), World Market, Forecast: 2014 to 2020
  • 6. IoT Backhaul Data Traffic by Application Segment: Sensor and Machine Data (Disruptive Scenario), World Market, Forecast: 2014 to 2020
  • 7. Volume of Backhaul Data Traffic from Sensors and Machines by Technology (Baseline Scenario), World Market, Forecast: 2014 to 2020
  • 8. Volume of Backhaul Data Traffic from Sensors and Machines by Technology (Disruptive Scenario), World Market, Forecast: 2014 to 2020
  • 9. Volume of IoT Backhaul Data Traffic from Video and Images by Scenario, World Market, Forecast: 2014 to 2020
  • 10. Volume of IoT Backhaul Data Traffic from Video and Images by Technology (Baseline Scenario), World Market, Forecast: 2014 to 2020
  • 11. Volume of IoT Backhaul Data Traffic from Video and Images by Technology (Disruptive Scenario), World Market, Forecast: 2014 to 2020

Charts

  • 1. Volume of Generated IoT Data by Type of Data, World Market, Forecast: 2014 to 2020
  • 2. Volume of Captured IoT Data by Data Type, World Market, Forecast: 2014 to 2020
  • 3. Volume of IoT Backhaul Data Traffic from Sensors and Machines by Scenario, World Market, Forecast: 2014 to 2010
  • 4. Volume of IoT Backhaul Data Traffic from Video and Images by Scenario, World Market, Forecast: 2014 to 2020
Back to Top