Emerging Computing Technologies
Sensor-powered devices provide secured connectivity for the world, flexibiliy and scalability in the cloud and data driven new business value.

Sensor-powered devices provide secured connectivity for the world, flexibiliy and scalability in the cloud and data driven new business value.
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Edge computing is bringing AI and other algorithms literally to the ‘edge’ of the high bandwidth world. It enables service providers to connect with their customers in-real time regardless of connectivity and physical location while a number of different cloud services offer the “scale” across different regions and devices.
Technological progress in, AI/ML in Cloud, Quantum, and ubiquity of data can address the bottlenecks that historically limited innovation & discovery. A connected technical infrastructure will expand collective opportunities for society by enabling data exploitation from edge to cloud, experimentation & interorganizational collaboration at scale.
Local AI-powered decision making will also minimise latency and security issues, building better customer experiences. Taking the IoT sensors and edge devices into use has become an easy task with fleet management services available in the cloud. Cloud inventory will store the transmitted data in one easy-accessible location, giving birth to new data driven analytics and services. Even in the extreme cases where the classic or super computers are not enough, there is the growing availability of quantum computing resources in the cloud.
Increasing adoption of IOT sensors and edge devices led to ubiquity of data. Availability of the cloud services has enabled the scale. it's more important than ever for organizations to have strong data governance competences. Right data and cloud transformation strategy will be the key enabler for companies in creating data driven user experiences, efficiencies in supply chains and inventory management. Most important of all, it will help diversify revenues by moving from product to service business models.
Modern computer vision applications will revolutionise predictive maintenance and quality control of assembly lines and big machinery.
Edge computing can be utilised in stores and warehouses to monitor products on shelves or analyse shopping behaviour, for example with indoor location data. In addition, edge computing can be leveraged for biometric identification of customers or employees.
The number of sensors and data points in Industrial IoT systems can be huge. These systems still require real-time control, which means cloud - and even wired - latency can be too high. Locally performed edge computing will be faster and more inexpensive than using cloud infrastructure.
Modern vehicles use increasing amounts of software to anticipate both surroundings and user actions. In most cases, it is crucial that latency stays low, making cloud services redundant. Also, the amount of data gathered by smart vehicles can be vast, so locally-performed analysis will be beneficial.
Edge computing can reduce the amount of data sent and received from customer electronics to remote services. It means more secure and quick devices like mobile phones. New kinds of algorithms such as computer vision can also be taken into use on devices previously not powerful enough to handle computation. This may lead to smarter cameras and home appliances, as well as devices like home assistant robots.
Smart home accessories can be helpful for residents, but they can also introduce irritation if they require constant connection to the cloud. Edge computing means appliances can work quickly and reliably - even on poor networks.
Edge computing $ 13,9 bn with 32% CAGR, consumer electronics $ 1,3 trillion.
Biometrics systems & tech $ 51,1 bn with 15% CAGR, Smart homes $ 180 bn with 18% CAGR, consumer robotics $ 19,2 bn with 20% CAGR, smart agriculture $ 20,6 bn with 15% CAGR, electric vehicles $ 667bn and 19% CAGR.
Methodology: A high-level view gathered from various openly available data sources on market size & compound annual growth rates. To simplify comparison between analysts sources, we’ve removed differences in market details by calculating an estimated average for total market size 2024 and average CAGR for selected time period. This allows us to gain “correct enough estimates”. Unless linked directly to one source, each of our market estimate has included 2-8 sources e.g. WEF, CB Insights, Mordor Intelligence, Markets & Markets, Research & Markets, Grand View Research, PWC, Allied Market Research, McKinsey, Market Watch")
(1376 investments 2018-2020): edge computing 2%, Cloud 6%, security 8%, links to decentralised computing and might become an appealing pivot from blockchain if supported by use cases 3%.
Source: Crunchbase Pro, accessed: 03/20, research of company description terms between 01/2018-02/2020, investment types: early stage investments by top 18 unicorn sniffing smart money VCs, n=1376 investments
Edge computing is a lucrative approach to ensure security & privacy issues, as well as operating offline in areas like automotive, robotic, maritime, offshore, logistics, and healthcare. Occasional connection to cloud in architecture design allows effective scaling & running updates. To get the most out of edge computing, software development, machine learning, cloud architecture & artificial intelligence are the enablers to make your hardware succeed.
Creating an edge-based solution takes time. With several decision points on the way, the development process requires creating the analytics model, deploying it and also running the model at the edge. One should also look into the processing capacity.
Compared to cloud computing, which advantage is the centralized processing power able to perform complex AI-algorithms, the processing power of the edge devices might not be sufficient.
When more and more de-centralized touchpoints are connected to the service systems, it adds complexity for the product and service ecosystem and the maintenance cost of the complete system might go high.
A local AI platform to build beneficial and privacy preserving AI by Google Research
Enabling high performance compute solutions for computer vision applications at the embedded edge
Distributed Services Platform bringing the cloud and edge architecture together
Intelligent and connected hardware products and edge solutions.
Pioneering data company specialized in data engineering for business data.
Graphical processing units and tensor processing units, that enable systems to act using less power.
Easy to use, productised versions of computing hardware such as embedded chips and cost effective small computers like raspberry pi.
Computer systems able to perform tasks normally requiring human intelligence by using a combination of Machine Learning (ML) toolbox of algorithms and learning rules from data.
A microcontroller unit (a small chip) is embedded with the ability to perform AI/ML computing offline based on sensor inputs without needing a cloud connection.
Neural Network (Deep Learning) is one of the most popular Machine Learning tools. Particularly effective with very large datasets.
The 5th generation of wireless technology that operates on a broader set of frequencies than 4G. This enables faster speed and latency in data transfer, as well as uninterrupted connectivity
Blockchain technology is a permanent, public, transparent ledger system and can be used to ensure the authenticity of the digital assets and value ranging from data on sales, tracking digital use and payments to content creators, such as wireless users or musicians.
Although cloud computing is nothing new its role will be redefined with the define its the advanced storage architecture that links cloud with edge.
Quantum computers perform calculations based on the probability of an object's state before it is measured, instead of just 1s or 0s. This enables them to process exponentially more data compared to classical computers. Quantum computers especially fit for complext problems where even supercomputers fall short such, designing new materials for carbon capture.