Summary

このレポートはIoT産業におけるエッジプロセッシング市場を調査し、企業へのインタビュー内容を掲載し、競合状況の分析、地域別の分析を行っています。

レポート内容　※一部抜粋

地域別のベンチマーク産業予測

• モバイルエッジコンピューティングの年間総支出
• モバイルエッジコンピューティングのオペレーターの年間総支出
• モバイルエッジコンピューティングノードの総展開数
• フォグコンピューティングの年間総支出
• フォグコンピューティングを採用したビジネスの総数

Overview

Juniper Research’s Edge Processing in IoT research provides a comprehensive evaluation of the origin, challenges, advantages, key drivers and future outlook of the Edge processing market. It offers an in-depth assessment of the key stakeholders; including start-ups, IT leaders and advanced technological innovations, such as AI and machine learning algorithms, that are collectively shaping Edge processing.

This research provides crucial insights for industries, particularly manufacturing, transport, utilities and telecommunications, which face a critical, significant need to collate, process and analyse data in real-time at the source of data generation, to facilitate intelligent decision making.

This report has also been produced to help the aforementioned industries deepen their understanding of the different strengths, opportunities and weaknesses of Edge processing services and products presented by the following players:

• Bosch
• Amazon Web Services
• Telit
• AdLink
• WICASTR
• Nymea
• VMware
• Eurotech
• Rigado
• FogHorn
• SWIM AI
• Litmus Automation
• VMWare
• Interactor
• ClearBlade

This research explores which industries seek to gain significantly from utilising Edge processing, as well as analyses which key players and factors are driving adoption of this technology.

This research suite includes:

• Market Trends & Vendor Strategies (PDF)
• 5-year Market Sizing & Forecast Spreadsheet (Excel)

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Table of Contents

Table of Contents

1. Deploying Edge Processing in IoT

1.1 Introduction: What is Edge Computing? . 6
1.1.1 Edge Computing & IoT . 6
1.1.2 Early Introductions of Edge Computing . 7
Table 1.1: Key Cloud & Edge Computing Developments, 1998-2012 . 7
1.1.3 Edge Computing vs Fog Computing . 7
Figure 1.2: Fog Computing Architecture . 8
Figure 1.3: Different Layers of Computing in IoT Systems, 2019 . 8
1.1.4 Using AI & ML to Enhance the Processing Intelligence of
Edge Devices. 9
Table 1.4 Select Edge AI Operating Start-Ups, 2016-2018 . 9
1.1.5 Drivers & Challenges of Using Edge Processing . 10
i. Drivers . 10
Table 1.5: Advantages of Using Edge Processing . 10
ii. Challenges . 11
Table 1.6: Major Challenges in Deploying Edge Processing . 12

2. Future Outlook for Edge Processing

2.1 Future Outlook for Edge Processing . 14
2.1.1 The Rise of AI in Edge Processing. 14
2.1.2 AI Edge Processing Start-ups . 15
2.1.3 Edge Computing Start-ups & Technology Leaders
Collaborate . 16
2.1.4 5G’s Role in AI Edge Processing . 17
2.1.5 The Role of Mobile Operators in Edge Processing . 18
2.1.6 Technology Leaders Enhance their Edge Computing
Capabilities . 20
Table 2.1: Select Edge Computing Developments, 2017-2018 . 20

3. Edge Processing: Vendor Analysis & Competitor Landscape

3.1 Introduction . 23
Table 3.1: Edge Platform Processing Provider Capability Assessment Criteria . 24
3.1.1 Leaderboard Scoring Results . 25
Table 3.2: Juniper Research Leaderboard Scoring Matrix: Capability & Capacity
vs Product & Position . 25
Figure 3.3: Juniper Research Leaderboard: Edge Processing Vendors. 26
3.1.2 Vendor Groupings . 27
i. Established Leaders . 27
ii. Leading Challengers. 28
3.1.1 Limitations & Interpretation . 30
3.2 Vendor Profiles . 31
3.2.1 VMware . 31
i. Corporate . 31
ii. Geographic Spread . 31
iii. Key Clients & Strategic Partnerships . 31
iv. High Level View Offerings . 31
Table 3.5: VMware’s AirWatch & Vrealize Automation Offerings . 32
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 32
3.2.2 AWS. 33
i. Corporate . 33
ii. Geographic Spread . 33
iii. Key Clients & Partnerships . 33
iv. High Level View Offerings . 33
Table 3.6: Three Drivers Behind the Launch of AWS Greengrass. 34
Figure 3.7: AWS Greengrass Business Model . 34
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 35
3.2.3 Siemens . 36
i. Corporate . 36
ii. Geographical Spread . 36
iii. Key Clients & Partnerships . 36
iv. High Level View Offerings . 36
Table 3.8: Three Key Functions of Siemens’ IDEP Offering . 37
Figure 3.9: Siemens Industrial Edge Management Business Model . 37
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 37
3.2.4 Rigado . 38
i. Corporate . 38
ii. Geographic Spread . 38
iii. Key Clients & Partnerships . 38
Table 3.10: IoT partners of Rigado . 38
iv. High Level View Offerings . 39
Figure 3.11: Rigado’s Cascade Edge-as-a-Service Platform . 39
Figure 3.12: Rigado Cascade-500 IoT Gateway . 40
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 41
3.2.5 Adlink . 41
i. Corporate . 41
ii. Geographic Spread . 41
iii. Key Clients & Partnerships . 41
iv. High Level View Offerings . 42
Table 3.13: Aspects of Adlink End-to-End Edge IoT Solution . 42
Table 3.14: Adlink Major Acquisitions, 2008-2015 . 42
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 43
3.2.6 Eurotech . 43
i. Corporate . 43
ii. Geographical Spread. 43
iii. Key Clients & Partnerships . 43
iv. High Level View Offerings . 44
Figure 3.15: Eurotech Edge-to-Cloud IoT Solution Framework . 44
Table 3.16: Operating Principles of Eurotech’s Everyware Cloud IoT Solution Framework, . 44
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 44
3.2.7 WICASTR . 45
i. Corporate . 45
ii. Geographic Spread . 45
iii. Key Clients & Partnerships . 45
iv. High Level View Offerings . 45
Figure 3.17: Core Activities of WICASTR’s SMART Edge Platform . 45
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 46
3.2.8 FogHorn . 46
i. Corporate . 46
ii. Geographic Spread . 46
iii. Key Clients & Partnerships . 47
iv. High Level View Offerings . 47
Table 3.18: Key Features of FogHorn Lightning Platform . 47
Figure 3.19: FogHorn Platform Business Model . 47
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 48
3.2.9 ClearBlade . 48
i. Corporate . 48
ii. Geographic Spread . 48
iii. Key Clients & Partnerships . 48
Table 3.20: ClearBlade: Select Major Partnerships . 48
iv. High Level View Offerings . 49
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 49
3.2.10 Bosch . 49
i. Corporate . 49
ii. Geographic Spread . 50
iii. Key Clients & Partnerships . 50
iv. High Level View Offerings . 50
Figure 3.21: Overview of Bosch IoT Gateway . 50
Table 3.22: Select Sectors & Companies using Bosch IoT Gateway Software for Edge processing . 51
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 51
3.2.11 Litmus Automation . 52
i. Corporate . 52
ii. Geographical Spread . 52
iii. Key Clients & Partnerships . 52
Figure 3.23: Litmus Automation Partners, 2013-2019 . 52
iv. High Level View Offerings . 53
Table 3.24: Litmus Automation Edge IoT Products . 53
Figure 3.25: Litmus Automation’s LoopEdge & Loop Functions . 54
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 54
3.2.12 Nymea . 54
i. Corporate . 54
ii. Geographical Spread. 54
iii. Key Clients & Partnerships . 54
iv. High Level View Offerings . 55
Figure 3.26: Nymea IoT Operating System . 55
Table 3.27: Key Components of Nymea . 55
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 56
3.2.13 Interactor . 56
i. Corporate . 56
Table 3.28: Interactor Key Developments, 2014-2018 . 56
ii. Geographical Spread. 57
iii. Key Clients & Partnerships . 57
iv. High level View Offering . 57
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 57
3.2.14 Telit . 57
i. Corporate . 57
ii. Geographical Spread. 58
iii. Key Clients & Partnerships . 58
iv. High Level View Offerings . 58
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 58
3.2.15 Swim . 59
i. Corporate . 59
ii. Geographical Spread . 59
iii. Key Clients & Partnerships . 59
iv. High Level View Offerings . 59
Table 3.29: Features of Swim’s EDX Open Source Platform. 60
Figure 3.30: Swim AI Business Model for Building Scalable Streaming Applications . 60
v. Juniper Research’s View: Key Strengths & Strategic Opportunities . 60

4. Edge Processing: Market Forecast Summary

4.1 Introduction . 63
4.2 Methodology & Assumptions . 63
4.2.1 Base Methodology . 63
4.2.2 MEC . 63
4.2.3 Fog Computing . 64
Figure 4.1: Edge Processing Forecasting Methodology . 65
4.3 Total Market Summary . 66
4.3.1 Total Annual Spend on Mobile Edge Computing . 66
Figure & Table 4.2: Total Annual Mobile Edge Computing Spend ($m), Split by 8
Key Regions 2019-2024 . 66
4.3.2 Total Annual Operator MEC Spend . 67
Figure & Table 4.3: Total Annual Mobile Edge Computing Spend ($m), Split By 8
Key Regions, 2019-2024 . 67
4.3.3 Total number of MEC Nodes in Operation . 68
Figure & Table 4.4: Total Number of Deployed MEC Nodes (m), Split by 8 Key
Regions 2019-2024 . 68
4.3.4 Total Annual Spend on Fog Computing . 69
Figure & Table 4.5: Total Annual Spend on Fog Computing ($m), Split by 8 Key
Regions, 2019-2024 . 69
4.3.5 Total Number of Businesses that have Deployed Fog Computing . 70
Figure & Table 4.6: Total Number of Businesses Deploying Fog Computing (000),
Split by 8 Key Regions, 2019-2024 . 70