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PUBLISHER: DataM Intelligence | PRODUCT CODE: 1372605

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PUBLISHER: DataM Intelligence | PRODUCT CODE: 1372605

Global Mobile Edge Computing Market - 2023-2030

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PAGES: 199 Pages
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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
Product Code: ICT7333

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
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