フォグコンピューティングの市場規模：タイプ、用途、地域別、2024年～2031年

フォグコンピューティング市場の評価、2024年～2031年

フォグコンピューティング市場の高い成長率は、2023年の1億8,746万米ドルから2031年には29億4,735万米ドルに達すると予測され、2024年から2031年の予測期間中のCAGRは48.00%です。産業界全体でモノのインターネット（IoT）デバイスが採用され、データ生成源に近いエッジでの効率的なデータ処理の必要性が高まっています。

フォグコンピューティング市場の定義/概要

フォグコンピューティングは、クラウドコンピューティングの拡張であり、データ処理とストレージ機能を、データが生成されるネットワークのエッジに近づけるものです。クラウド・コンピューティング環境で集中管理されるのではなく、コンピューティング・リソースとアプリケーション・サービスをデバイスやノードに分散できるアーキテクチャです。フォグコンピューティングは、モノのインターネット（IoT）デバイスの増加とリアルタイムのデータ処理・分析の必要性によってもたらされる課題に対処するために設計されています。

フォグコンピューティングでは、すべてのデータを集中型のクラウドに送信するのではなく、データ生成源に近いネットワークのエッジでデータ処理と分析を行う。このアプローチにより、待ち時間が短縮され、レスポンスタイムが向上し、機密データをインターネット経由で送信する必要性が最小限に抑えられるため、セキュリティとプライバシーが強化されます。フォグコンピューティングは、リアルタイムのデータ処理と低遅延通信が重要な産業オートメーション、スマートシティ、ヘルスケア、自律走行車など、さまざまな領域で応用されています。コネクテッドデバイスの数が増え続ける中、エッジでの効率的なデータ管理、処理、分析を可能にし、クラウドコンピューティングの機能を補完・拡張する上で、フォグコンピューティングが果たす役割はますます重要になると予想されます。

モノのインターネット（IoT）デバイスの採用増加はフォグコンピューティング市場の成長を促進するか？

さまざまな産業でモノのインターネット（IoT）デバイスの採用が増加していることが、フォグコンピューティング市場の成長の大きな促進要因になると予想されます。IoTデバイスは、効率的な意思決定と自動化のためにリアルタイムで処理・分析する必要がある大量のデータを生成します。従来のクラウド・コンピューティング・アーキテクチャは、多くのIoTアプリケーションのリアルタイム処理と低遅延の要件を満たす上で課題に直面しています。フォグコンピューティングは、クラウド機能をネットワークのエッジまで拡張し、データ生成源に近い場所でデータ処理と分析を可能にすることで、こうした課題に対処します。IoTエコシステムにおいて、フォグコンピューティングは、レイテンシーの短縮、レスポンスタイムの改善、機密データをインターネット経由で送信する必要性を最小限に抑えることによるセキュリティとプライバシーの強化という重要な役割を果たします。エッジでデータを処理することで、フォグコンピューティングはリアルタイムの意思決定を可能にします。これは、即時対応が重要な産業オートメーション、スマートシティ、自律走行車などのアプリケーションに不可欠です。

さらに、IoTデバイスによって生成されるデータ量が増加しているため、エッジでの効率的なデータ管理と処理機能が必要とされています。フォグコンピューティングは、IoTデバイスから大量に流入するデータを処理できる分散型アーキテクチャを提供し、集中型クラウドリソースの負担を軽減し、システム全体のパフォーマンスを向上させます。製造、ヘルスケア、輸送、スマートホームなど、さまざまな分野でコネクテッドデバイスが急増し続ける中、フォグコンピューティングソリューションの需要は急増すると予想されます。フォグコンピューティングは、エッジでの効率的なデータ処理、分析、意思決定を可能にし、リアルタイムモニタリング、予知保全、インテリジェントオートメーションの新たな可能性を解き放ちます。

大量のデータを管理・処理する複雑さは、フォグコンピューティング市場が直面する重大な課題か？

大量のデータの管理と処理の複雑さは、確かにフォグコンピューティング市場が直面する重要な課題です。フォグコンピューティングアーキテクチャは、ネットワークのエッジに分散コンピューティングリソースを含み、様々なIoTデバイスやセンサーから大量のデータを生成します。主な課題の1つは、これらの大容量データの効率的な処理と保存です。一般的にリソースに制約のあるデバイスであるフォグノードは、集中型のクラウドインフラと比較して、ストレージや処理能力に限界があります。この制限により、利用可能なリソースの効率的な利用を保証するために、データの優先順位付け、圧縮、フィルタリングなどの効果的なデータ管理戦略が必要となります。さらに、IoTデバイスの異種性と生成されるデータの多様な性質は、データ統合と相互運用性の面で課題をもたらします。異なるデバイスは、様々なフォーマット、構造、プロトコルでデータを生成する可能性があり、フォグコンピューティング環境内で効果的に処理・分析するためには、これらのデータを標準化し、調和させる必要があります。

もう一つの課題は、エッジでのデータ処理と分析の複雑さです。フォグコンピューティングでは、収集されたデータから貴重な洞察をリアルタイムで抽出するための高度な分析機能が必要です。これには、機械学習や人工知能アルゴリズムをリソースに制約のあるフォグノード上に展開する必要があり、計算集約的で実装が難しい場合があります。分散フォグコンピューティング・アーキテクチャで大量のデータを扱う場合、セキュリティとプライバシーに関する懸念も生じる。データのプライバシーと完全性を維持しながら、複数のフォグノード間で安全なデータトランスミッション、ストレージ、処理を保証することは、強固なセキュリティメカニズムとプロトコルを必要とする重要な課題です。このような課題に対処するため、現在進行中の研究開発では、効率的なデータ管理技術の開発、リソースの割り当てと利用の最適化、相互運用性基準の改善、フォグコンピューティング環境におけるセキュリティとプライバシー対策の強化に重点的に取り組んでいます。さらに、エッジコンピューティングや5Gネットワークなどの新技術の統合により、エッジで大量のデータを管理・処理するためのインフラや機能の向上が期待されています。

目次

第1章 世界のフォグコンピューティング市場：イントロダクション

• 市場概要
• 調査範囲
• 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

• データマイニング
• バリデーション
• 一次資料
• データソース一覧

第4章 世界のフォグコンピューティング市場展望

• 概要
• 市場力学
  • 促進要因
  • 抑制要因
  • 機会
• ポーターのファイブフォースモデル
• バリューチェーン分析
• 規制の枠組み

第5章 フォグコンピューティングの世界市場：タイプ別

• 概要
• ハードウェア
• ソフトウェア

第6章 フォグコンピューティングの世界市場：用途別

• 概要
• ビルとホームオートメーション
• スマートエネルギー
• コネクテッドヘルス
• スマート製造
• コネクテッド・ビークル
• その他

第7章 世界のフォグコンピューティング市場：地域別

• 概要
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • その他欧州
• アジア太平洋
  • 中国
  • 日本
  • インド
  • その他アジア太平洋地域
• 世界のその他の地域
  • ラテンアメリカ
  • 中東・アフリカ

第8章 世界のフォグコンピューティング市場：競合情勢

• 概要
• 各社の市場シェア
• ベンダー情勢
• 主な発展戦略

第9章 企業プロファイル

• Cisco Systems Inc.
• Microsoft Corporation
• ARM Holdings PLC
• Schneider Electric Software LLC
• Toshiba Corporation
• Prismtech Corporation
• Dell Inc.
• Nebbiolo Technologies
• Intel Corporation
• GE Digital
• Fujitsu Ltd.

第10章 主な発展

• 製品の上市／開発
• 合併と買収
• 事業拡大
• パートナーシップと提携

第11章 付録

• 関連調査

Fog Computing Market Valuation - 2024-2031

The high growth rate of the Fog Computing Market, projected to reach USD 2947.35 Million by 2031 from USD 187.46 Million in 2023, with a CAGR of 48.00% during the forecast period of 2024 to 2031, is being driven by the increasing demand for real-time data processing and low latency in various applications. The adoption of Internet of Things (IoT) devices across industries is fueling the need for efficient data processing at the edge, closer to the source of data generation.

Fog Computing Market Definition/ Overview

Fog computing is an extension of cloud computing that brings data processing and storage capabilities closer to the edge of the network, where data is being generated. It is an architecture that enables computing resources and application services to be distributed across devices and nodes, rather than being centralized in a cloud computing environment. Fog computing is designed to address the challenges posed by the increasing number of Internet of Things (IoT) devices and the need for real-time data processing and analysis.

In fog computing, data processing and analysis are performed at the edge of the network, closer to the source of data generation, rather than transmitting all data to a centralized cloud. This approach reduces latency, improves response times, and enhances security and privacy by minimizing the need to transmit sensitive data over the internet. Fog computing finds applications in various domains, such as industrial automation, smart cities, healthcare, and autonomous vehicles, where real-time data processing and low-latency communication are crucial. As the number of connected devices continues to grow, fog computing is expected to play an increasingly important role in enabling efficient data management, processing, and analysis at the edge, complementing and extending the capabilities of cloud computing.

Will the Increasing Adoption of Internet of Things (IoT) Devices Drive the Growth of the Fog Computing Market?

The increasing adoption of Internet of Things (IoT) devices across various industries is expected to be a significant driver for the growth of the Fog Computing Market. IoT devices generate massive amounts of data that need to be processed and analyzed in real time for efficient decision-making and automation. Traditional cloud computing architectures face challenges in meeting the real-time processing and low latency requirements of many IoT applications. Fog computing addresses these challenges by extending cloud capabilities to the edge of the network, enabling data processing and analysis closer to the source of data generation. In the IoT ecosystem, fog computing plays a crucial role in reducing latency, improving response times, and enhancing security and privacy by minimizing the need to transmit sensitive data over the internet. By processing data at the edge, fog computing enables real-time decision-making, which is essential for applications such as industrial automation, smart cities, and autonomous vehicles, where immediate responses are critical.

Furthermore, the growing volume of data generated by IoT devices necessitates efficient data management and processing capabilities at the edge. Fog computing provides a decentralized architecture that can handle the massive influx of data from IoT devices, reducing the burden on centralized cloud resources and improving overall system performance. As the number of connected devices continues to proliferate across various sectors, including manufacturing, healthcare, transportation, and smart homes, the demand for fog computing solutions is expected to surge. Fog computing enables efficient data processing, analysis, and decision-making at the edge, unlocking new possibilities for real-time monitoring, predictive maintenance, and intelligent automation.

Is the Complexity of Managing and Processing Large Volumes of Data a Significant Challenge Faced by the Fog Computing Market?

The complexity of managing and processing large volumes of data is indeed a significant challenge faced by the Fog Computing Market. Fog computing architectures involve distributed computing resources at the edge of the network, generating massive amounts of data from various IoT devices and sensors. One of the primary challenges is the efficient handling and storage of these large data volumes. Fog nodes, which are typically resource-constrained devices, have limited storage and processing capabilities compared to centralized cloud infrastructures. This limitation necessitates effective data management strategies, such as data prioritization, compression, and filtering, to ensure efficient utilization of available resources. Furthermore, the heterogeneity of IoT devices and the diverse nature of data generated pose challenges in terms of data integration and interoperability. Different devices may generate data in various formats, structures, and protocols, which need to be standardized and harmonized for effective processing and analysis within the fog computing environment.

Another challenge is the complexity of data processing and analysis at the edge. Fog computing requires advanced analytics capabilities to extract valuable insights from the collected data in real time. This involves the deployment of machine learning and artificial intelligence algorithms on resource-constrained fog nodes, which can be computationally intensive and challenging to implement. Security and privacy concerns also arise when dealing with large volumes of data in a distributed fog computing architecture. Ensuring secure data transmission, storage, and processing across multiple fog nodes while maintaining data privacy and integrity is a critical challenge that requires robust security mechanisms and protocols. To address these challenges, ongoing research and development efforts are focused on developing efficient data management techniques, optimizing resource allocation and utilization, improving interoperability standards, and enhancing security and privacy measures in fog computing environments. Additionally, the integration of emerging technologies, such as edge computing and 5G networks, is expected to provide improved infrastructure and capabilities for managing and processing large volumes of data at the edge.

Category-Wise Acumens

How is the Hardware Segment Driving the Growth of the Fog Computing Market?

The hardware segment plays a pivotal role in the growth of the Fog Computing Market. Fog computing relies on a distributed architecture of hardware components, including fog nodes, gateways, and sensors, deployed at the edge of the network to enable data processing and analysis closer to the source of data generation. Fog nodes, which are essentially small-scale computing devices, form the backbone of fog computing infrastructure. These nodes are equipped with processors, memory, storage, and networking capabilities to perform data processing, analysis, and communication tasks. The hardware segment encompasses a wide range of fog nodes, from embedded systems and single-board computers to ruggedized industrial computers, depending on the application requirements.

The performance and capabilities of these fog nodes are critical in enabling real-time data processing and decision-making at the edge. As the demand for low-latency and high-performance fog computing solutions increases, the hardware segment is witnessing continuous advancements in terms of processing power, energy efficiency, and miniaturization. Moreover, the hardware segment also includes specialized components like sensors, actuators, and IoT gateways, which are essential for collecting and transmitting data from various sources to the fog nodes. These devices are designed to operate in harsh environments and cater to specific industry requirements, such as temperature and vibration resistance in industrial settings. The hardware segment's growth is driven by the increasing adoption of IoT devices across various sectors, including manufacturing, healthcare, smart cities, and transportation. As the number of connected devices continues to rise, the demand for robust and efficient fog computing hardware solutions is expected to surge.

Furthermore, the integration of emerging technologies, such as 5G networks and edge computing, is creating new opportunities for the hardware segment. These technologies enable seamless connectivity, low latency, and efficient data transmission, further driving the need for advanced fog computing hardware components.

How is the Healthcare Sector Leveraging Fog Computing Solutions?

The healthcare sector is increasingly leveraging fog computing solutions to enable efficient data processing, real-time analysis, and improved patient care. The adoption of fog computing in healthcare is driven by the need for low-latency data processing, enhanced security and privacy, and the ability to handle large volumes of data generated by various medical devices and sensors. In healthcare facilities, fog computing enables the collection and processing of data from various sources, such as patient monitoring devices, medical imaging equipment, and electronic health records (EHRs). By processing this data at the edge, near the source of data generation, fog computing solutions can provide real-time insights and enable timely decision-making for patient care and treatment. One of the key applications of fog computing in healthcare is remote patient monitoring. Fog nodes can collect and analyze data from wearable devices and home monitoring systems, enabling healthcare providers to continuously monitor patients' vital signs and health conditions. This real-time data analysis at the edge allows for early detection of potential issues and timely interventions, improving patient outcomes and reducing the need for hospital visits.

Moreover, fog computing plays a crucial role in telemedicine and telehealth services. By processing data locally, fog computing solutions can ensure low latency and high-quality video and audio streaming, enabling seamless remote consultations and virtual care delivery, particularly in rural or underserved areas. In medical imaging and diagnostics, fog computing can assist in the efficient processing and analysis of large medical image datasets, such as CT scans, MRI scans, and X-rays. By performing initial processing and analysis at the edge, fog computing can reduce the bandwidth requirements and latency associated with transmitting large image files to the cloud, enabling faster diagnosis and treatment. Furthermore, fog computing addresses the critical concern of data privacy and security in the healthcare sector. By processing sensitive patient data at the edge, fog computing minimizes the need to transmit data over the internet, reducing the risk of data breaches and ensuring compliance with data protection regulations.

Country/Region-wise Acumens

How has North America emerged as the dominant market for Fog Computing solutions?

North America, particularly the United States, has emerged as the dominant market for fog computing solutions. North America has been at the forefront of adopting emerging technologies, including cloud computing, the Internet of Things (IoT), and edge computing. This early adoption has paved the way for the rapid integration of fog computing solutions across various industries, enabling real-time data processing and decision-making at the edge. The region is home to many leading technology companies, such as Cisco Systems, Dell Technologies, Intel Corporation, and Microsoft, which are actively developing and promoting fog computing solutions. These companies have made significant investments in research and development, driving innovation and enhancing the capabilities of fog computing platforms. North America boasts a well-developed telecommunications infrastructure and widespread availability of high-speed internet connectivity, which is crucial for enabling seamless communication between fog nodes, devices, and the cloud. This robust infrastructure supports the efficient deployment and operation of fog computing solutions.

Several key industries in North America, such as manufacturing, healthcare, and smart cities, have recognized the benefits of fog computing and are rapidly adopting these solutions. The region's focus on industrial automation, remote patient monitoring, and smart city initiatives has driven the demand for low-latency data processing and real-time decision-making capabilities offered by fog computing. North America benefits from a skilled workforce with expertise in areas such as cloud computing, IoT, and data analytics. This availability of skilled professionals has facilitated the successful implementation and integration of fog computing solutions across various sectors. Furthermore, the region's strong emphasis on research and development, coupled with substantial investments in emerging technologies, has positioned North America as a leader in the fog computing market. As the demand for real-time data processing and analysis continues to grow, North America is expected to maintain its dominance in this market, driving further advancements and widespread adoption of fog computing solutions.

How is the Asia Pacific Region Emerging as a Rapidly Growing Market for Fog Computing Solutions?

The Asia Pacific region is witnessing rapid growth in the adoption of fog computing solutions. Countries like China, India, and Southeast Asian nations are undergoing massive industrialization and infrastructure development projects, including smart city initiatives and the establishment of industrial complexes. These developments are driving the demand for real-time data processing, low-latency communication, and efficient data management, which are key advantages offered by fog computing solutions. The Asia Pacific region is a global manufacturing hub, with countries like China, Japan, and South Korea leading the way. The manufacturing sector is rapidly adopting Industry 4.0 and industrial IoT (IIoT) technologies, which generate massive amounts of data from various sensors and devices. Fog computing enables efficient data processing and analysis at the edge, optimizing manufacturing processes and enabling real-time decision-making.

The region is witnessing a surge in healthcare initiatives, such as remote patient monitoring and telemedicine, as well as the development of smart cities. Fog computing plays a crucial role in these sectors by enabling low-latency data processing, seamless integration of IoT devices, and enhanced security and privacy for sensitive data. Privacy and data sovereignty concerns are prompting many countries in the Asia Pacific region to adopt data localization policies. Fog computing aligns with these policies by enabling data processing and storage at the edge, reducing the need to transmit sensitive data to centralized cloud platforms. The region is home to several leading technology companies, such as Huawei, Samsung, and Tencent, which are actively developing and promoting fog computing solutions. These companies are investing in research and development, driving innovation, and addressing the specific requirements of the Asia Pacific market.

Competitive Landscape

The fog computing market is characterized by the presence of several established players and innovative solution providers. These companies are continuously pushing the boundaries of fog computing technology through research and development efforts, strategic partnerships, and the introduction of advanced features and capabilities. The competitive landscape is marked by companies offering a diverse range of fog computing solutions and services, including computing platforms, edge computing hardware, software frameworks, analytics tools, and consulting services.

Some of the prominent players operating in the fog computing market include:

Cisco Systems, Inc.

Microsoft Corporation

ARM Holding Plc

Dell Inc.

Fujitsu

General Electric Company

Nebbiolo Technologies, Inc.

Schneider Electric

Toshiba Corporation

PrismTech Corporation

ADLINK Technology Inc.

Cradlepoint, Inc.

FogHorn Systems

Latest Developments

In Aug 2022, The Pentagon is seeking new agreements with technology businesses that can provide potentially game-changing edge and fog computing capabilities to support military activities. Platforms used by the Defense Department to support multi-domain operations rely on sensors that gather enormous amounts of information on machinery and its operating conditions.

In January 2023, Amazon Web Services (AWS) launched AWS IoT Greengrass 3.0, an update to its edge computing software for IoT devices. The new version includes enhanced security features and support for more programming languages, making it easier for developers to build and deploy fog computing applications.

In May 2022, Nokia and Microsoft partnered to develop 5G and edge computing solutions for enterprise customers. The partnership will leverage Nokia's 5G network infrastructure, Microsoft's Azure cloud platform, and Azure Edge Zones to provide low-latency and high-bandwidth computing capabilities.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL FOG COMPUTING MARKET

• 1.1 Overview of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL FOG COMPUTING MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis
• 4.5 Regulatory Framework

5 GLOBAL FOG COMPUTING MARKET, BY TYPE

• 5.1 Overview
• 5.2 Hardware
• 5.3 Software

6 GLOBAL FOG COMPUTING MARKET, BY APPLICATION

• 6.1 Overview
• 6.2 Building and Home Automation
• 6.3 Smart Energy
• 6.4 Connected Health
• 6.5 Smart Manufacturing
• 6.6 Connected Vehicles
• 6.7 Others

7 GLOBAL FOG COMPUTING MARKET, BY GEOGRAPHY

• 7.1 Overview
• 7.2 North America
  • 7.2.1 U.S.
  • 7.2.2 Canada
  • 7.2.3 Mexico
• 7.3 Europe
  • 7.3.1 Germany
  • 7.3.2 The U.K.
  • 7.3.3 France
  • 7.3.4 Rest of Europe
• 7.4 Asia Pacific
  • 7.4.1 China
  • 7.4.2 Japan
  • 7.4.3 India
  • 7.4.4 Rest of Asia Pacific
• 7.5 Rest of the World
  • 7.5.1 Latin America
  • 7.5.2 Middle East and Africa

8 GLOBAL FOG COMPUTING MARKET COMPETITIVE LANDSCAPE

• 8.1 Overview
• 8.2 Company Market Share
• 8.3 Vendor Landscape
• 8.4 Key Development Strategies

9 COMPANY PROFILES

• 9.1 Cisco Systems Inc.
  • 9.1.1 Overview
  • 9.1.2 Financial Performance
  • 9.1.3 Product Outlook
  • 9.1.4 Key Developments
• 9.2 Microsoft Corporation
  • 9.2.1 Overview
  • 9.2.2 Financial Performance
  • 9.2.3 Product Outlook
  • 9.2.4 Key Developments
• 9.3 ARM Holdings PLC
  • 9.3.1 Overview
  • 9.3.2 Financial Performance
  • 9.3.3 Product Outlook
  • 9.3.4 Key Developments
• 9.4 Schneider Electric Software LLC
  • 9.4.1 Overview
  • 9.4.2 Financial Performance
  • 9.4.3 Product Outlook
  • 9.4.4 Key Developments
• 9.5 Toshiba Corporation
  • 9.5.1 Overview
  • 9.5.2 Financial Performance
  • 9.5.3 Product Outlook
  • 9.5.4 Key Developments
• 9.6 Prismtech Corporation
  • 9.6.1 Overview
  • 9.6.2 Financial Performance
  • 9.6.3 Product Outlook
  • 9.6.4 Key Developments
• 9.7 Dell Inc.
  • 9.7.1 Overview
  • 9.7.2 Financial Performance
  • 9.7.3 Product Outlook
  • 9.7.4 Key Developments
• 9.8 Nebbiolo Technologies
  • 9.8.1 Overview
  • 9.8.2 Financial Performance
  • 9.8.3 Product Outlook
  • 9.8.4 Key Developments
• 9.9 Intel Corporation
  • 9.9.1 Overview
  • 9.9.2 Financial Performance
  • 9.9.3 Product Outlook
  • 9.9.4 Key Developments
• 9.10 GE Digital
  • 9.10.1 Overview
  • 9.10.2 Financial Performance
  • 9.10.3 Product Outlook
  • 9.10.4 Key Developments
• 9.11 Fujitsu Ltd.
  • 9.11.1 Overview
  • 9.11.2 Financial Performance
  • 9.11.3 Product Outlook
  • 9.11.4 Key Developments

10 KEY DEVELOPMENTS

• 10.1 Product Launches/Developments
• 10.2 Mergers and Acquisitions
• 10.3 Business Expansions
• 10.4 Partnerships and Collaborations

11 Appendix

• 11.1 Related Research