表紙:モバイルエッジコンピューティングの世界市場-2023年~2030年
市場調査レポート
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1372605

モバイルエッジコンピューティングの世界市場-2023年~2030年

Global Mobile Edge Computing Market - 2023-2030

出版日: | 発行: DataM Intelligence | ページ情報: 英文 199 Pages | 納期: 約2営業日

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モバイルエッジコンピューティングの世界市場-2023年~2030年
出版日: 2023年10月18日
発行: DataM Intelligence
ページ情報: 英文 199 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
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  • 概要
  • 目次
概要

概要

世界のモバイルエッジコンピューティング市場は、2022年に6億米ドルに達し、2023~2030年の予測期間中にCAGR 26.3%で成長し、2030年には31億米ドルに達すると予測されています。

拡大現実(AR)、仮想現実(VR)、自律走行車、IoTデバイスなどのアプリケーションは、極めて低いレイテンシーを必要とします。モバイルエッジコンピューティングは、データをソースに近いところで処理することで遅延を減らし、ユーザー体験を向上させる。5Gネットワークの展開は、モバイルエッジコンピューティングが効果的に機能するために必要な広帯域幅と低遅延を提供します。モバイルエッジコンピューティングは、データの局所的な処理を可能にすることで5Gを補完し、集中型クラウドサーバーへのデータ送信の必要性を低減します。

例えば、2023年9月26日、東南アジア最大の通信プロバイダーであるTelkomselは、デジタル・トランスフォーメーションへの取り組みにおいて、アマゾン・ウェブ・サービスを優先クラウド・プロバイダーとして選択しました。Telkomselは、顧客チャネル、ゲーム・プラットフォーム、ミドルウェア、機械学習など、さまざまなITアプリケーションをAWSに移行します。インドネシアで1億5,300万人以上の加入者を抱えるTelkomselは、AWSを利用してユーザー体験を向上させ、新しいサービスをより迅速に展開することを目指しています。

アジア太平洋は、5G技術展開の最前線にあります。5Gネットワークの展開は、モバイルエッジコンピューティングに必要な広帯域幅と超低遅延を提供します。モバイルエッジコンピューティングは、コンピューティング・リソースをネットワーク・エッジに近づけることで5Gを補完し、リアルタイムかつ低遅延のアプリケーションを可能にします。IoT機器によってエッジで生成される膨大な量のデータを処理するには、モバイルエッジコンピューティングが必要です。モバイルエッジコンピューティングは、IoTアプリケーションを可能にするため、産業、農業、スマートシティなどの分野で利用されています。

力学

5Gアプリケーションの増加

5Gは、旧世代と比較して大幅に高い帯域幅を提供します。モバイルエッジコンピューティングは、この帯域幅を活用して、4Kビデオストリーミング、クラウドゲーム、大規模なIoT展開など、データ量の多いアプリケーションを処理・配信します。モバイルエッジコンピューティングは、エッジコンピューティングリソースを各ネットワークスライス固有の要件に合わせて調整することでこれを補完し、最適なパフォーマンスを確保します。モバイルエッジコンピューティングは、機密情報をローカルで処理することでセキュリティとデータ・プライバシーを強化し、集中型データセンターへの転送中のデータの露出を最小限に抑えます。

例えば、シンガポールのシングテルは2021年2月2日、企業向けに5Gエッジコンピュート・インフラストラクチャの提供を開始し、オプションの1つとしてマイクロソフト・アジュール・スタックを提供しました。これにより企業は、自律走行ガイド付き車両、ドローン、ロボット、複合現実などのアプリケーションをエンドユーザーに近いところで処理できるようになります。シングテルの5Gネットワークでは、これらのアプリケーションを10ミリ秒未満の低遅延で配信することができます。

高度なネットワークソリューションの採用

モバイルエッジコンピューティングは、集中型データセンターからエッジサーバーに処理タスクをオフロードし、コアネットワークへの広帯域幅接続の必要性を減らすことで、帯域幅の使用を最適化し、ネットワークの混雑を緩和します。モバイルエッジコンピューティング・アーキテクチャは拡大性が高く、ワークロードの増大やユーザーの需要に対応するためにエッジサーバーを効率的に追加することができます。

例えば、2023年2月21日、T-モバイルとアマゾン・ウェブ・サービス(AWS)は、T-モバイルの5GネットワークソリューションとAWSのクラウドベースのサービスを組み合わせることで提携しました。この提携は、5Gエッジコンピュート機能へのアクセスと展開をよりシームレスにし、導入を加速してコストを削減する方法を企業に提供することを目的としています。統合プライベートワイヤレス(Integrated Private Wireless on AWS)として知られるこの統合サービスにより、企業は、産業用キャンパスの遠隔監視や製造業における予知保全など、特定の使用事例向けにソリューションをカスタマイズできるようになります。

技術の進歩と革新

エッジにおける人工知能(AI)と機械学習(ML)の統合は、モバイルエッジコンピューティングの重要な推進力です。エッジAIは、様々な産業において、ローカルな意思決定、予知保全、インテリジェントな自動化を可能にします。モバイルエッジコンピューティングは、機密データを集中型データセンターに送信する代わりにローカルで処理することでセキュリティを強化することができ、このアプローチにより、転送中にデータが潜在的脅威にさらされることを減らすことができます。

例えば、2023年9月14日、ベンガルールを拠点とする新興企業KaleidEO Space Systems社は、宇宙空間でエッジコンピューティングを実証した初のインド企業となり、重要なマイルストーンを達成しました。同社はディープラーニング・アルゴリズムを使って、衛星コンステレーションプロバイダーのSatellogic社が撮影した高解像度の衛星画像をリアルタイムで解析しました。この成果は、KaleidEO社がエッジコンピューティング機能を搭載した衛星を開発し、単独で画像を撮影・解析できるようにする道を開くものです。

限られたデータセンターと複雑なサーバー

エッジサーバーの処理能力は、集中型データセンターと比較すると限界があります。複雑な計算やリソースを大量に必要とするアプリケーションでは、クラウドやデータセンターのリソースが必要な場合があり、そのようなタスクでは待ち時間が発生します。エッジサーバーのCPU、メモリ、ストレージのリソースは限られているため、エッジで実行できるアプリケーションの種類やサイズが制限されます。

増大するワークロードとユーザー需要に対応するためにエッジインフラを拡大することは、複雑でコストがかかります。エッジサーバーを追加導入し、既存のネットワークとシームレスに統合する必要があります。分散エッジ環境の管理は、集中型データセンターの管理よりも複雑になる可能性があります。エッジサーバーの効率的なオーケストレーション、モニタリング、メンテナンスが必要になります。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 5Gアプリケーションの増加
      • 先進ネットワークソリューションの採用
      • 技術の進歩と革新
    • 抑制要因
      • 限られたデータセンターと複雑なサーバー
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • ロシア・ウクライナ戦争の影響分析
  • DMI意見

第6章 COVID-19分析

第7章 コンポーネント別

  • ソフトウェア
  • ハードウェア
  • サービス別

第8章 組織規模別

  • 大企業
  • 中小企業

第9章 用途別

  • スマートシティ
  • IoT
  • コンテンツ配信
  • 拡大現実
  • その他

第10章 エンドユーザー別

  • 製造業
  • エネルギー・公益事業
  • 小売・消費財
  • メディア・娯楽
  • 運輸・物流
  • その他

第11章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他の欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他の南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他のアジア太平洋
  • 中東・アフリカ

第12章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第13章 企業プロファイル

  • Advantech Co., Ltd.
  • Johnson Controls International plc
  • Hewlett Packard Enterprise Development LP
  • Huawei Technologies Co., Ltd.
  • Juniper Networks, Inc.
  • SAGUNA Network LTD
  • SMART Global Holdings, Inc.
  • Vapor IO, Inc.
  • Nokia Corporation
  • Skyvera

第14章 付録

目次
Product Code: ICT7333

Overview

Global Mobile Edge Computing Market reached US$ 0.6 billion in 2022 and is expected to reach US$ 3.1 billion by 2030, growing with a CAGR of 26.3% during the forecast period 2023-2030.

Applications such as augmented reality, virtual reality, autonomous vehicles and IoT devices require extremely low latency. Mobile edge computing reduces latency by processing data closer to the source, improving the user experience. The rollout of 5G networks provides the high bandwidth and low latency necessary for mobile edge computing to function effectively. Mobile edge computing complements 5G by enabling localized processing of data, reducing the need to transmit data to centralized cloud servers.

For instance, on 26 September 2023, Telkomsel, Southeast Asia's largest telecommunications provider, chose Amazon Web Services as its preferred cloud provider for its digital transformation efforts. Telkomsel will migrate various IT applications to AWS, including customer channels, gaming platforms, middleware and machine learning. With over 153 million subscribers in Indonesia, Telkomsel aims to enhance the user experience and deploy new services more quickly using AWS.

Asia-Pacific has been at the forefront of the deployment of 5G technology. The rollout of 5G networks provides the necessary high bandwidth and ultra-low latency required for mobile edge computing. Mobile edge computing complements 5G by bringing computing resources closer to the network edge, enabling real-time and low-latency applications. Processing the vast quantities of data produced at the edge by IoT devices requires mobile edge computing. Mobile edge computing is being used by sectors like industry, agriculture and smart cities to allow IoT applications.

Dynamics

Rising Application of 5G

5G offers significantly higher bandwidth compared to previous generations. Mobile edge computing leverages this bandwidth to process and deliver data-intensive applications, such as 4K video streaming, cloud gaming and large-scale IoT deployments. Mobile edge computing complements this by tailoring edge computing resources to the specific requirements of each network slice, ensuring optimal performance. Mobile edge computing enhances security and data privacy by processing sensitive information locally and this minimizes the exposure of data during transit to centralized data centers.

For instance, on 2 February 2021, Singapore's Singtel launched 5G edge compute infrastructure for enterprises, offering Microsoft Azure Stack as one of the options and this allows enterprises to process applications such as autonomous guided vehicles, drones, robots and mixed reality closer to their end-users. With Singtel's 5G network, these applications can be delivered with low latency of less than 10 milliseconds.

Adoption of Advanced Network Solutions

Mobile edge computing offloads processing tasks from centralized data centers to edge servers, reducing the need for high-bandwidth connections to the core network and this optimizes bandwidth usage and alleviates network congestion. Mobile edge computing architecture is highly scalable, allowing for the efficient addition of edge servers to accommodate growing workloads and user demands as this scalability is crucial for handling the increasing volume of IoT devices and applications.

For instance, on 21 February 2023, T-Mobile and Amazon Web Services (AWS) partnered to combine T-Mobile's 5G network solutions with AWS cloud-based services and this collaboration aims to provide businesses with a more seamless way to access and deploy 5G edge compute capabilities, accelerating adoption and reducing costs. The integrated offering, known as Integrated Private Wireless on AWS, will allow organizations to customize solutions for specific use cases, such as remote industrial campus monitoring, predictive maintenance in manufacturing and more.

Technology Advancement and Innovations

The integration of artificial intelligence (AI) and machine learning (ML) at the edge is a significant driver of mobile edge computing. Edge AI enables local decision-making, predictive maintenance and intelligent automation in various industries. Mobile edge computing can enhance security by processing sensitive data locally instead of transmitting it to centralized data centers and this approach reduces the exposure of data to potential threats during transit.

For instance, on 14 September 2023, KaleidEO Space Systems, a Bengaluru-based startup, achieved a significant milestone by becoming the first Indian company to demonstrate edge computing in space. The company used deep learning algorithms to analyze high-resolution satellite imagery in real-time, captured by Satellogic, a satellite constellation provider and this achievement paves the way for KaleidEO to develop satellites with onboard edge computing capabilities, allowing them to capture and analyze images independently.

Limited Data Centers and Complex Servers

Edge servers have limited processing capabilities compared to centralized data centers. Complex computations and resource-intensive applications may still require cloud or data center resources, leading to latency for such tasks. dge servers have limited resources in terms of CPU, memory and storage and this restricts the types and sizes of applications that can run at the edge.

Scaling edge infrastructure to accommodate growing workloads and user demands can be complex and costly. It requires deploying additional edge servers and ensuring seamless integration with the existing network. Managing a distributed edge environment can be more complex than managing centralized data centers. It requires efficient orchestration, monitoring and maintenance of edge servers.

Segment Analysis

The global mobile edge computing market is segmented based on component, organization size, application, end-user and region.

Cloud-Native Technologies and Edge Network Deployment Boosts the Market

Mobile edge computing software leverages cloud-native technologies such as containerization and microservices which allows for scalable and flexible deployment of edge applications, making it easier for developers to create and manage mobile edge computing services. Intelligent decision-making in real-time has been rendered feasible by mobile edge computing software, which is essential for applications like autonomous vehicles, smart cities and predictive maintenance.

For instance, on 28 February 2023, 5G Networks and Intel announced a partnership to collaborate on edge network deployments in Australia. The companies plan to leverage Intel's technology, including Intel Xeon Scalable processors and FlexRAN software reference architecture, to enhance 5G Networks' edge computing capabilities and this partnership aims to provide businesses with low-latency, high-performance edge computing solutions for various applications, including IoT, artificial intelligence and more.

Geographical Penetration

Region Actively Deploys 5G Networks

North America has been actively rolling out 5G networks. Mobile edge computing leverages 5G to bring computing resources closer to the network edge, enabling real-time and low-latency services. Many cities in the region are implementing smart city projects, including traffic management, public safety and environmental monitoring whereas mobile edge computing plays a crucial role in enabling these initiatives by processing data at the edge in real-time.

For instance, on 30 December 2022, SK Telecom successfully transmitted terrestrial broadcasting in Washington D.C. using mobile edge computing and virtualization technologies in collaboration with Sinclair Broadcast Group, North America's largest terrestrial broadcast conglomerate. Mobile edge computing technology reduces latency by placing a small data center near a base station, minimizing data transmission distance. The platform enables efficient management of broadcast services for numerous regional stations across North America without requiring specialized equipment.

Competitive Landscape

The major global players in the market include: Advantech Co., Ltd., Johnson Controls International plc, Hewlett Packard Enterprise Development LP, Huawei Technologies Co., Ltd., Juniper Networks, Inc., SAGUNA Network LTD, SMART Global Holdings, Inc., Vapor IO, Inc., Nokia Corporation and Skyvera.

COVID-19 Impact Analysis

The pandemic forced many businesses to accelerate their digital transformation efforts to adapt to remote work and changing customer behavior. Mobile edge computing played a crucial role in enabling low-latency applications and services, such as video conferencing, telemedicine and e-commerce, to meet the increased demand. Mobile edge computing supported the growth of remote work and collaboration tools by reducing latency in video conferencing and virtual collaboration platforms.

Mobile edge computing facilitated the adoption of telemedicine and remote healthcare solutions, enabling real-time monitoring of patients and remote consultations with healthcare professionals and this was critical in managing healthcare services during lockdowns and minimizing the risk of virus transmission. Mobile edge computing combined with edge AI enabled the development of contactless solutions, including touchless payments, temperature screening and social distancing monitoring, to enhance safety in public spaces and businesses.

The pandemic disrupted global supply chains, impacting the availability of hardware components needed for mobile edge computing infrastructure deployment. Delayed equipment deliveries and shortages affected deployment timelines. Economic uncertainties caused budget constraints for some organizations, affecting their ability to invest in mobile edge computing infrastructure and services.

AI Impact

AI algorithms deployed at the edge can process and analyze data in real-time and this enables mobile edge computing to make intelligent decisions locally, reducing the need to transmit data to centralized cloud servers. For example, AI-powered edge devices can detect anomalies, recognize patterns and respond to events without relying on remote data centers. AI inference tasks, such as image recognition, natural language processing and predictive analytics can be performed at the edge.

AI-driven personalization and content recommendations can be delivered at the edge, enhancing user experiences in areas like content streaming, gaming and retail. AI algorithms analyze user behavior and preferences locally, enabling real-time adjustments and content delivery. AI-powered edge devices can identify and respond to security threats in real time. For example, AI algorithms can detect unusual network patterns, intrusions or malware at the edge, preventing potential security breaches before they reach the core network.

For instance, on 13 February 2023, AICRAFT, an Australian artificial intelligence (AI) company, has achieved a milestone by launching its edge computing module named Pulsar into space. The module, deployed as part of the JANUS-1 satellite, is designed to perform ultra-fast processing of space data using AI while consuming minimal power. During ground tests, it demonstrated the ability to classify 1,250 images of Earth Observation data in about 10 seconds.

Russia- Ukraine War Impact

In the global technology supply chain, Ukraine is a major player, particularly in the software development and IT outsourcing sectors. The battle could affect the availability of qualified software engineers and IT specialists, which could have an impact on the creation and upkeep of mobile edge computing systems. Geopolitical tensions and conflicts can lead to uncertainty in international business relationships.

In regions affected by conflict, the stability of critical infrastructure, including data centers and communication networks, may be at risk. Mobile edge computing relies on robust and secure infrastructure, so disruptions in conflict zones could impact mobile edge computing deployments. Geopolitical conflicts can raise concerns about data privacy and security, especially when data is processed at the edge. Organizations may become more cautious about where and how their data is processed, potentially affecting mobile edge computing adoption.

By Component

  • Software
  • Hardware
  • Services

By Organization Size

  • Large Enterprises
  • SMEs

By Application

  • Smart Cities
  • IoT
  • Content Delivery
  • Augmented Reality
  • Others

By End-User

  • Manufacturing
  • Energy and Utilities
  • Retail and Consumer Goods
  • Media and Entertainment
  • Transportation and Logistics
  • Others

By Region

  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Russia
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

Key Developments

  • In September 2021, AT&T, the Official 5G Innovation Partner of the WNBA, collaborated with the league to introduce a new fan experience called "AT&T 5G Game View" and this feature, available within the WNBA app, offers fans a more immersive way to engage with WNBA games.
  • In September 2022, Nokia is collaborating with Flex Brazil to deploy 5G Standalone (SA) private wireless networks in Flex's manufacturing facilities in Brazil. The partnership aims to explore 5G technology's potential in manufacturing, including reliable connectivity, data transfer and layout flexibility on the shop floor. Nokia's Digital Automation Cloud (Nokia DAC) will provide private wireless on-demand services, along with MX Industrial Edge computing and digital-enabling applications.
  • In October 2022, Verizon partnered with Reset Digital to pioneer a neuroprogrammatic approach to audience engagement. Reset Digital's Neuroprogrammatic TM advertising platform is a programmatic solution that enables brands to engage omnichannel audiences based on their motivations, allowing for broader and deeper audience reach across all communities, including underrepresented ones.

Why Purchase the Report?

  • To visualize the global mobile edge computing market segmentation based on component, organization size, application, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of mobile edge computing market-level with all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global mobile edge computing market report would provide approximately 69 tables, 71 figures and 199 Pages.

Target Audience 2023

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Component
  • 3.2. Snippet by Organization Size
  • 3.3. Snippet by Application
  • 3.4. Snippet by End-User
  • 3.5. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Rising Application of 5G
      • 4.1.1.2. Adoption of Advanced Network Solutions
      • 4.1.1.3. Technology Advancement and Innovations
    • 4.1.2. Restraints
      • 4.1.2.1. Limited Data Centers and Complex Servers
    • 4.1.3. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. Russia-Ukraine War Impact Analysis
  • 5.6. DMI Opinion

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID
    • 6.1.2. Scenario During COVID
    • 6.1.3. Scenario Post COVID
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Component

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 7.1.2. Market Attractiveness Index, By Component
  • 7.2. Software*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Hardware
  • 7.4. Services

8. By Organization Size

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 8.1.2. Market Attractiveness Index, By Organization Size
  • 8.2. Large Enterprises*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. SMEs

9. By Application

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.1.2. Market Attractiveness Index, By Application
  • 9.2. Smart Cities*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. IoT
  • 9.4. Content Delivery
  • 9.5. Augmented Reality
  • 9.6. Others

10. By End-User

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.1.2. Market Attractiveness Index, By End-User
  • 10.2. Manufacturing*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Energy and Utilities
  • 10.4. Retail and Consumer Goods
  • 10.5. Media and Entertainment
  • 10.6. Transportation and Logistics
  • 10.7. Others

11. By Region

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 11.1.2. Market Attractiveness Index, By Region
  • 11.2. North America
    • 11.2.1. Introduction
    • 11.2.2. Key Region-Specific Dynamics
    • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.2.7.1. U.S.
      • 11.2.7.2. Canada
      • 11.2.7.3. Mexico
  • 11.3. Europe
    • 11.3.1. Introduction
    • 11.3.2. Key Region-Specific Dynamics
    • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.3.7.1. Germany
      • 11.3.7.2. UK
      • 11.3.7.3. France
      • 11.3.7.4. Italy
      • 11.3.7.5. Russia
      • 11.3.7.6. Rest of Europe
  • 11.4. South America
    • 11.4.1. Introduction
    • 11.4.2. Key Region-Specific Dynamics
    • 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.4.7.1. Brazil
      • 11.4.7.2. Argentina
      • 11.4.7.3. Rest of South America
  • 11.5. Asia-Pacific
    • 11.5.1. Introduction
    • 11.5.2. Key Region-Specific Dynamics
    • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.5.7.1. China
      • 11.5.7.2. India
      • 11.5.7.3. Japan
      • 11.5.7.4. Australia
      • 11.5.7.5. Rest of Asia-Pacific
  • 11.6. Middle East and Africa
    • 11.6.1. Introduction
    • 11.6.2. Key Region-Specific Dynamics
    • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

  • 12.1. Competitive Scenario
  • 12.2. Market Positioning/Share Analysis
  • 12.3. Mergers and Acquisitions Analysis

13. Company Profiles

  • 13.1. Advantech Co., Ltd.*
    • 13.1.1. Company Overview
    • 13.1.2. Product Portfolio and Description
    • 13.1.3. Financial Overview
    • 13.1.4. Key Developments
  • 13.2. Johnson Controls International plc
  • 13.3. Hewlett Packard Enterprise Development LP
  • 13.4. Huawei Technologies Co., Ltd.
  • 13.5. Juniper Networks, Inc.
  • 13.6. SAGUNA Network LTD
  • 13.7. SMART Global Holdings, Inc.
  • 13.8. Vapor IO, Inc.
  • 13.9. Nokia Corporation
  • 13.10. Skyvera

LIST NOT EXHAUSTIVE

14. Appendix

  • 14.1. About Us and Services
  • 14.2. Contact Us