09Mastered edge computing
Sensor-powered devices provide secured connectivity for the world (privacy, ai everywhere offline, decentralization)
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. As hardware continues to become cheaper and more powerful, edge computing has the potential to drastically improve the capabilities of our electronic infrastructure and tools.
What will change
As hardware becomes more capable and software becomes faster (yet more lightweight), artificial intelligence will move from the cloud into localised microprocessors capable of handling sophisticated algorithms. This will lead to even smarter vehicles and products, improved data security and zero latency decision making.
Edge AI will give devices the capability to make sophisticated, independent decisions in real time. This will enable smarter energy usage at lower cost with less data transmission. Local AI-powered decision making will also minimise latency and security issues, building better customer experiences. Cloud inventory will store transmitted data in one easy-accessible location, giving birth to new services that take advantage of both technologies.
Opportunities worth taking
Helping modern, widely-scaled IoT services move away from pure cloud connectivity will unburden centralised services and databases that struggle when devices require good connectivity to run as a raw pipeline to the cloud. Edge computing can also make devices more secure, particularly when handling sensitive data such as biometrics.
This is where the open-minded, the visionaries, the rebels and disruptors design new digital customer experiences for the world to come. Are you one of us?
Manufacturing of goods
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.
Why is this relevant
Total market 2024
Edge computing $ 13,9 bn with 32% CAGR, consumer electronics $ 1,3 trillion.
Look at these 2024
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.
Top 18 unicorn sniffing smart money VCs, 1376 investments 2018-2020
(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%.
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.
Technologies that will enable the shift
Cheaper GPU and TPU hardware
Graphical processing units and tensor processing units, that enable systems to act using less power. These are becoming cheaper and getting smaller in size.
More powerful processors
Easy to use, productised versions of computing hardware such as embedded chips and cost effective small computers like raspberry pi.
AI/ML Artificial intelligence (AI)
Computer systems that accomplish goals in some task(s). It is not a single thing; it is a combination of technologies such as machine learning. Machine Learning (ML) - toolbox of algorithms and techniques that learn rules from data. Used to implement AI in narrow tasks. Not magic; learns from the data by minimising a cost function.
Components that are required to run an ML Algorithm locally on a device. In edge computing, 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.
UNCAD: Half of the world still remains offline
(2018: 51,2% of population reached online)
Emphasis on improving networking technologies for remote working
Despite Covid-19 outbreak, edge computing to drive next wave of innovation in Asia-Pacific, says GlobalData
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