18 CLOUD COMPUTING
18 CLOUD COMPUTING
more quickly and analysed in greater detail so enterprises should embrace ‘ event thinking ’ and Lambda Architectures as part of a digital landscape .
IoT on the Edge IoT has contributed to a serious rise in the types and amount of datasets generated and the proliferation of things connected to the Internet has meant that Edge Computing is hugely important for all industries . Research firm IHS predicts that IoT will grow to reach a staggering 75 billion devices by 2025 .
Right now , there needs to be a way to aggregate , analyse and distribute that data from the ‘ things ’ and send it back to the ‘ things ,’ quicker . Currently , Edge computing technology such as AWS Greengrass collects data and processes it from nearby items , sending it back to the cloud where analytics and machine learning ( ML ) can take place in order to make sense of the data , before sending the data back to the edge and subsequently the things – making them more intelligent .
The next wave will be for the compute to move from the cloud towards the edge giving the objects the ability to make intelligent real-time decisions i . e . a car needing to make a split-second decision on whether it should apply the brakes to avoid an accident . Edge computing holds data analysed from the cloud that is immediately passed to the object for instantaneous updates and responses , and
Cloud to the Edge data distribution is essential to improve customer experience in industries like manufacturing , health and retail .
Industrial Internet of Things ( IIoT ) Today , there are roughly 6.4 billion data-communicating objects in the world and this number is forecast to triple by 2020 ( Accenture ). The majority of these objects will be ‘ things ’ - whether cars , white goods or industrial assets : aka smart machines . These smart machines - featuring a multitude of sensors , automation proficiency and machineto-machine communication capability constitute the IIoT . IIoT will enable data-driven manufacturing , where process and floor-wide monitoring are able to optimise efficiency and quality , through the application of machine learning to big data . This is being heralded as the revolution that will introduce huge productivity boosts to the industry .
As the UK launches its first state of the art , fourth industrial revolution ( Industry 4.0 ) factory – AMRC Sheffield Factory 2050 – the potential for Edge Computing within Industrial IoT is accelerating . Dedicated to conducting collaborative research into reconfigurable robotic , digitally assisted assembly and machining technologies , there will be a need for a high variation and mass customisation of manufacturing throughout a diverse range of engineering sectors . This will shorten lead times and optimise costs accumulated throughout the supply chain , and that can rapidly ramp production up or down to meet demand . Big data technology processes large volumes of information , collected by sensors on each machine , cell and the building itself to enable automation – without forsaking the need for humans . Look at how Fujitsu , with its UBIQUITOUSWARE , takes in an immersed reality with its products that enable humans to do a better job with the use of real-time analytics and data collected from other scenarios . If you fell in the factory , or if there ’ s a potential danger , edge technology and sensors can feed and receive this information directly to and from the worker . Machines can cause harm – robots that know when humans are close and slow down to protect them are an important part of the Industry 4.0 or IIOT revolution .
With the evolution of IoT and the vastly larger data sets that are streaming into the cloud , it would be impractical to try and process that quantity of data in real-time . However , you can use AI and event sourcing to summarise and generate actionable insights . Gartner wrote that 59 % of organisations are still gathering information to build their AI strategies , while the remainder has already made progress in piloting or adopting AI solutions .
For businesses wanting to make use of structured and unstructured data to stimulate intelligent decisions and spot trends across all departments , it ’ s time to focus on data engineering , data lakes and ML in a practical way to identify data sets that would provide the most benefit from building a machine learning capability . This could include things such as fraud or purchase recommendations and up / cross-sell . Furthermore , businesses need to adopt viable cloud services that will benefit the business in the long-term – and already conversations with organisations are shifting to discuss how all these technologies will enable better cloud performance without breaking the bank . n
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