INTELLIGENT GREEN TECHNOLOGY
connected and start understanding how much each one has deteriorated with time ,” says Shekhar .
The previous world of high fidelity systems is changing now . The development of edge computing through intelligent and efficient sensors distributed across industrial networks is allowing data processing within the closed network itself . Both network speeds and compute processing have progressed exponentially since the 1940s , when this innovation cycle was first initiated .
But now it is being fed into machine learning algorithms . This is boosting the development of high fidelity models and taking them to the next level of performance .
Bhanu Shekhar , Chief Digital Officer , GE Power Middle East and Africa .
Improvement in network and compute speeds are now allowing vendors such as GE Power to build new strategies . According to this process , select data is analysed through edge devices , that reside on the operational technology side of control systems . Edge devices provide millisecond compute and help to rapidly optimise the asset . The rest of the data that is not required for optimisation of the asset is sent to the cloud for analytics .
“ Some of the information needs to have a feedback loop . You do not want some information to go the cloud , where somebody analyses and then it comes back . A lot of these functionalities are sitting with the edge device , and a lot of analytics are getting built on those edge devices . Whatever is needed to be addressed within milliseconds we put it on the edge device , and wherever it is long term analyses you do it on the cloud ,” adds Shekhar .
GE Power ’ s strategy , therefore delivers a two-fold benefit for industrial end users . When data is streamed into cloud based analytical applications , they gain from the insights . But the biggest value is the real-time optimisation of the industrial asset , generated by the edge device itself .
The next level of integration is the ability to bring together , high fidelity models of each asset into a much bigger set up . “ Today the biggest value for the customer is can you take these high-fidelity systems which exist , and can you put it so that the whole set up gets optimised ,” points out Shekhar . •
Architecture of an IoT platform
Device management is for endpoint provisioning , remote configuration , data monitoring , software updates , and error reporting . Device management ensures the ongoing ability of the endpoint to send and receive data .
Software is often deployed via an agent client installed on endpoints . Some solutions may also include an identity management component that stores device information and device identities .
Connectivity management ensures data flows from the edge to the cloud and is managed and secured in transit with encryption capabilities . This may be limited to IP communication via a cloud gateway , which communicates bidirectionally with endpoints typically through protocols such as MQTT , AMQP , CoAP , or REST APIs . For deployments relying on cellular connectivity , some IoT platform vendors can provide SIM management , including billing and SIM alerts . Partnerships are common in this area to meet the requirements of global deployments running over various communication networks .
Data ingestion , processing , and management . IoT platforms often include rules engines that route incoming data to the correct destination . Typical destinations include storage mechanisms , other applications , or web services . They may also perform basic anomaly detection by comparing incoming data to a set of rules defined by an organisation . Data transformation , aggregation , and enrichment , and complex event processing capabilities may also be included in some IoT platform products .
Visualisation tools and dashboards allow companies to manipulate IoT data or visualise it in real time .
Application enablement is often in the form of APIs to platform services . With APIs fully documented , organisations or third parties can push and customise IoT platform data according to their requirements . Marketplaces are emerging in this area , and partnerships are often localised to a country or verticalised by industry . Some vendors also package application development tools as a standardised platform component .
Advanced Analytics , such as machine learning and predictive analytics tools , are most often not a standard platform component today but offer a differentiation opportunity for IoT platform vendors .
Source : IDC MarketScape , Worldwide IoT Platforms , Software Vendors , 2017 Vendor Assessment .
42 Issue 17 INTELLIGENT TECH CHANNELS