5 Things To Remember While Executing Industrial IoT
Industrial Internet of Things (IIoT) or Industry 4.0 uses the power of smart machines as well as real-time analytics to gain the advantages of data in industrial and business aspects. According to this report, manufacturing will be the biggest IoT platform by 2021, reaching $438 million as IIoT increases efficiency. The study by Accenture says that the IIoT could help reduce machinery breakdowns by 70% and reduce overall maintenance costs by 30%. In this article, we list down five crucial points to treasure while planning for architecting the IIoT. 1| Large Data Landscape The IIoT promises to transform the industries, enterprises, manufacturing, etc. with modernised services, cutting-edge infrastructures, etc. The data is growing exponentially and it genuinely needs a much larger space to store the gathered data. A fair IIoT infrastructure must have the ability to handle large volumes of data and also make it reusable when it is needed. 2| Public Key Infrastructure Public Key Infrastructure or PKI is a set of technologies and services which is based on the digital certificate or X.509 certificates for managing strong authentication, data encryption, digital signatures, etc. in computer systems. This infrastructure provides the specific tools and methods which are required to issue certificates to all the IIoT devices. It provides some set of permissions, identity which is issued by a trusted entity. An IIoT device thus can verify the identity by the digital certificates issued by the trusted entity which ensures the security to the machines. 3| Scalable And Secured Design IIoT is all about data and with a large amount of data in an organisation, sometimes it becomes difficult in the scalability part. You must make sure while designing the IIoT that it is scalable enough to develop networking technologies and other such standards where data is collected from numerous devices. To make a design secured as much as possible, you must think all the negative ways one can try to breach your data from your organisation. Identifying the targets as well as the probability of the area that can be breached can help you to mitigate the risks before it occurs. 4| Find The Right Fit The IIoT enterprise needs to set a fair plan in every step of the project which should be used while proceeding with the project. The commitment of IIoT is huge and one must need a proper plan to step ahead in this journey. In 2017, a survey by Cisco stated that 60% of respondents stressed that IoT initiatives often look good on paper but prove much more difficult than anyone expected where the challenges involved such as time to completion, limited internal expertise, quality of data, integration across teams, and budget overruns. Henceforth, it is crucial to engage the IoT partner ecosystem at every stage, implying that strong partnerships throughout the process can smooth out the learning curve. Also, having skilled enough people to build an IoT solution with generalised knowledge of every possible aspect can surely help to step ahead in this journey. 5| Security The IIoT has just started its transition phase and it will still need time to be used across sectors. We all know that data is growing in a very fast manner and as the data grows, the security threats are also incrementing. More breaches, thefts, leakage, etc. have started for the last few years. While building an IIoT infrastructure, it is to be concerned that there is fair end-to-end encryption for security and privacy means.
Industrial Internet of Things (IIoT) or Industry 4.0 uses the power of smart machines as well as real-time analytics to gain the advantages of data in industrial and business aspects. According to this report, manufacturing will be the biggest IoT platform by 2021, reaching $438 million as IIoT increases efficiency. The study by Accenture says […]
Industrial Internet of Things (IIoT) or Industry 4.0 uses the power of smart machines as well as real-time analytics to gain the
["IIoT", "industrial IoT"]
["Go", "programming_languages:R", "AI", "industrial IoT", "Scala", "Git", "analytics", "real-time analytics", "Rust", "GAN", "R", "IIoT"]
["AI", "analytics", "R", "Go", "Rust", "Scala", "Git", "real-time analytics", "GAN", "programming_languages:R"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.