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市場調查報告書

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1622917

2024 年至 2031 年霧計算市場規模（按類型、應用和地區劃分）

Fog Computing Market Size By Type (Hardware, Software), Application (Building And Home Automation, Smart Energy), And Region for 2024-2031

出版日期: 2024年07月27日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

霧計算市場評估，2024-2031

霧運算市場成長率高，預計將從 2023 年的 1.8746 億美元成長到 2031 年的 29.4735 億美元，在 2024 年至 2031 年的預測期內，複合年增長率為 48.00%。各行各業對物聯網 (IoT) 設備的採用推動了對邊緣高效資料處理的需求，更接近資料產生的來源。

霧計算市場定義/概述

霧運算是雲端運算的擴展，它將資料處理和儲存能力移到更接近生成資料的網路邊緣。它是一種允許運算資源和應用服務分佈在各個設備或節點上，而不是在雲端運算環境中集中管理的架構。霧運算旨在解決物聯網 (IoT) 設備激增以及即時數據處理和分析需求所帶來的課題。

在霧運算中，資料處理和分析發生在網路邊緣，更靠近資料產生來源，而不是將所有資料傳送到集中式雲端。這種方法最大限度地減少了透過網路發送敏感資料的需要，從而減少了延遲，縮短了回應時間，並增強了安全性和隱私性。霧運算正在應用於工業自動化、智慧城市、醫療保健和自動駕駛汽車等多個領域，其中即時數據處理和低延遲通訊是關鍵。隨著連接設備數量的不斷增長，霧運算將在實現邊緣高效的資料管理、處理和分析方面發揮越來越重要的作用，補充和擴展雲端運算的功能。

物聯網（IoT）設備的日益普及是否會推動霧運算市場的成長？

各行各業擴大採用物聯網 (IoT) 設備，預計將成為霧運算市場成長的主要驅動力。物聯網設備產生大量數據，需要即時處理和分析，以實現高效的決策和自動化。傳統雲端運算架構在滿足許多物聯網應用的即時處理和低延遲要求方面面臨課題。霧運算透過將雲端功能擴展到網路邊緣來解決這些課題，使資料處理和分析更接近資料生成的來源。霧運算在物聯網生態系統中發揮關鍵作用，它能減少延遲、縮短回應時間，並透過最大限度地減少透過網路傳輸敏感資料的需要來增強安全性和隱私性。透過在邊緣處理數據，霧運算可以實現即時決策。這對於工業自動化、智慧城市和自動駕駛汽車等至關重要的即時反應應用至關重要。

此外，物聯網設備產生的資料量不斷增加，需要邊緣高效的資料管理和處理能力。霧運算提供了一種分散式架構，可以處理來自物聯網設備的大量資料湧入，從而減少集中式雲端資源的壓力並提高整體系統效能。隨著連網設備在製造業、醫療保健、交通運輸和智慧家居等領域的不斷普及，對霧運算解決方案的需求預計將飆升。霧運算實現了邊緣高效的資料處理、分析和決策，為即時監控、預測性維護和智慧自動化開闢了新的可能性。

管理和處理大量資料的複雜性是否是霧運算市場面臨的重大課題？

管理和處理大量資料的複雜性無疑是霧運算市場面臨的關鍵課題。霧運算架構涉及網路邊緣的分散式運算資源，以處理各種物聯網設備和感測器產生的大量資料。主要課題之一是有效地處理和儲存這些大量數據。霧節點通常是資源受限的設備，與集中式雲端基礎設施相比，其儲存和處理能力有限。這種限制需要有效的資料管理策略，例如資料優先權、壓縮和過濾，以確保有效利用可用資源。此外，物聯網設備的異質性及其產生的數據的多樣性對數據整合和互通性帶來了課題。不同的設備可能會產生各種格式、結構和協議的數據，這些數據需要標準化和協調，以便在霧計算環境中有效地處理和分析。

另一個課題是邊緣資料處理和分析的複雜性。霧運算需要先進的分析能力，以便從即時收集的數據中提取有價值的見解。這需要在資源受限的霧節點上部署機器學習和人工智慧演算法，這可能需要大量計算且難以實現。在分散式霧運算架構中處理大量資料也會引發安全性和隱私問題。確保多個霧節點之間的安全資料傳輸、儲存和處理，同時維護資料隱私和完整性是一項重大課題，需要強大的安全機制和協定。為了應對這些課題，我們正在進行研究和開發工作，包括開發高效的資料管理技術、最佳化資源分配和利用、改進互通性標準以及確保霧運算環境中的安全性和隱私性。我們的措施。此外，邊緣運算和5G網路等新技術的整合有望改善邊緣管理和處理大量資料的基礎設施和能力。

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.

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