The muIOT platform was designed for data analytics solutions in the world of Industrial Internet of Things. It is a holistic decision sciences approach integrating hardware, big data analytics software, software and analytical aspects which lets you deploy statistical machine learning algorithms, analytic processes and tools in big data over a variety of devices to analyze data and draw insights from it. With muIOT, one can easily connect, deploy, monitor and manage millions of IoT devices and applications on the edge, the cloud or the enterprise.
The muIOT architecture and Gartner’s review of IoT platform capabilities
IoT platform capabilities include
Provisioning and management of IoT endpoints (things) and gateways
Customizing and building applications (software development kit [SDK], application server, integrated development environment [IDE] and others) and big data tools and technologies.
Event processing: event stream and data aggregation, stream analytics, storage and management, and information management (often collectively referred to as “data digestion”)
Decision processing: rule engines, orchestration of workflows and business process management (BPM)
Analysis: IoT data analysis and visualization (including dashboards)
Cybersecurity: authentication, encryption, certificate management and so forth
IoT device communications (physical layer [such as Wi-Fi or cellular], and data layer, such as MQTT or HTTP)
Integration: API publish and subscribe, protocol hopping, scalable transformation and adapters to connect to business applications and data sources, cloud services, mobile apps, legacy, and so forth — see “Market Guide for IoT Integration”
Adapter (API hub as well as software on the gateway or the endpoint)
User interfaces for both end users and developers
The muIOT platform is designed to scale big data analytics and gain business value in the Enterprise and the IIoT
Powering analytics on the edge
The muIOT platform facilitates creation of contextually aware applications, manufacturing analytics orchestrating real-time and batch processing between machine-machine and man-machine to develop intelligent systems that can be deployed on the enterprise, the cloud or the edge.
Designed to scale – loyal to standards and not technology
Rapid experimentation of solutions before scaling – the maker mindset
Prototyping custom data analytics solutions on containerized devices by fusing a mixture of data sources, things, and sensors becomes easy using muIOT. It enables organizations to experiment on solutions in a cost effective manner, before scaling and going into production.
Go beyond the enterprise firewall to farm data
The muIOT platform facilitates data capture and metadata creation from beyond the firewall and ingestion into the organization’s ecosystem.
Consume and generate knowledge repository
The marketplace hosts a collection of modular repositories in various categories – data collection, machine learning, data engineering, and visualizations. It enables collaboration within an organization to generate and consume artifacts.
The IoT approach to master the new waves of production
Use cases built using muIOT™
- Mu Sigma developed a framework for retail companies which helps them determine customer behavior patterns to improve store management and customer shopping experience
- Video analytics powered by muIOT was used to track on-shelf availability and customer distribution in the store, generate heat-maps of customer movement over time
- Proximity sensors like beacons are used to track customer movement in the retail stores and monitor fresh produce refrigeration conditions and sent out event based alerts to store management
- The solution was leveraged for queue management, offering recommendations, inventory management and customer dwells in the store
- The project has opened up various avenues with newer data points that were generated and integration with existing data to bring out newer insights
- Mu Sigma helped a major airline company to improve on-time performance and customer experience through video analytics in real-time
- In the prototyping phase, custom devices were designed in the innovation labs of Mu Sigma using commodity hardware and 3D printed components which were installed in the airports and remotely monitored using the muIOT platform to capture videos from various angles
- Deep learning algorithms were trained with the video data to detect and track various classes of humans and baggage
- For scaling, videos were captured in multiple airports and the trained algorithm was deployed on the edge GPU hardware
- The video data was translated into meaningful metadata that was used in sending real-time alerts to the ground ops team and generate data that was previously absent
How can muIOT™ generate Customer Value?
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