Estimated reading time: 9 minutes
In an Internet of Things (IoT) ecosystem, IoT platforms are a central storage and staging ground for sensor data. They manage device connectivity and secure communication. Smart devices connect through these platforms to process and analyze data. This merging of functions streamlines device onboarding and life cycle management.
IoT platforms have evolved since their first iteration. Early platforms were self-contained, with applications written inside. Modern platforms connect devices with enterprise systems and apply artificial intelligence (AI) and machine learning (ML). They simplify data transfer from edge devices to cloud-based enterprise systems and databases.
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Table of Contents
IoT Platform Types and Use Cases
The first IoT Evolution Developers Conference was held in 2015. Industrial and consumer device companies needed a way to link devices and direct their data to other systems. Attendees sought to broaden IoT potential through applications and systems that would serve as the first generation of IoT platforms.
These platforms connected disparate sensors and devices. Like early IoT devices and use cases, first-generation platforms focused on specific functionalities with limited connectivity and network bandwidth. Over time, they have become more powerful and sophisticated. They now incorporate ML with AI and edge computing to transform data into actionable insights in enterprise systems.
How the 3 Most Common IoT Platform Types Are Used
IoT Application Enablement Platform (AEP)
Application enablement platforms (AEPs) provide tools and services to build, deploy and manage IoT applications. Their primary purposes are:
- Connectivity
- Data management
- Application-building tools
AEPs are self-contained. Managers gather and store data from edge devices and write applications within the platform. Most AEPs include:
- Prebuilt templates for developing apps
- Customization options through APIs
- Cloud connectivity with the ability to integrate analytics
They typically function as software as a service (SaaS) entities and thrived in the early days of IoT. AEPs provided the base features needed for:
- Device connection
- Data collection and storage
- Data manipulation
- Visualization
Vendors created proprietary systems and commercialized them for broader appeal. They were best utilized for early-stage implementations.
Large cloud providers recently changed the role of AEPs in the IoT market. They offered more robust IT features for building next-generation business applications using data from OT systems.
AEPs are now often combined with broader offerings in more flexible IoT platforms. While the term AEP isn’t used as frequently today, its concepts are still critical to IoT use cases, including:
IoT Device Management Platform
The device management platform emerged after the AEP and expanded deployment-management capabilities. IoT device management platforms tend to be cloud-based and enable organizations to:
- Provision and configure new IoT devices on a network
- Monitor their health and performance
- Deliver firmware and security updates over the air (OTA)
IoT device management platforms can authenticate devices on a network. They provide organizations with tools to manage and monitor a fleet of devices. They’re more about the devices than the data. Device management platforms use cases include:
- Smart home products, like connected appliances
- Vehicle telematics
- Factory equipment, like predictive maintenance sensors
- Vendor-specific product support systems, like hardware gateways and computer numerical controls (CNCs)
Many device management platforms are specific to their products and don’t support multivendor solutions.
IoT Data Orchestration Platform
Vendors developed an IoT data orchestration platform as platforms became more sophisticated. These platforms collect and analyze data from IoT sensors. The information is then transformed into insights that inform decision-making in other enterprise systems.
Telit Cinterion is a leader in this space. Our IoT platforms focus on data orchestration and integration — complete IoT systems management. Data orchestration transfers data from the edge to the enterprise business application through:
- Real-time and batch data processing
- Integration with AI and ML tools
- Organizing aggregated data from multiple sources
IoT data orchestration platforms support analysis of unstructured data with AI-powered tools. These platforms now accommodate inputs like video streams and audio recordings.
With AI, the platforms process data from visual inspections and perform object recognition. This functionality is key for manufacturing applications such as product inspections.
deviceWISE® EDGE, powered by Telit Cinterion, offers comprehensive design, deployment and management features:
- Native drivers for machine and sensor data collection
- Direct cloud connectors
- Full SCADA experience with deviceWISE VIEW
Data orchestration platform use cases include:
- Customer behavior analysis in retail
- Smart farming sensors
- Predictive maintenance and visual inspection in manufacturing
- Grid optimization for energy management
Data Orchestration Platform Use Cases
Automotive Manufacturer Eases Industrial IT and OT Integration
A global automaker aimed to cut costs and simplify the upkeep and integration of legacy PLC devices into its manufacturing execution system (MES). With the deviceWISE IIoT platform, the automobile manufacturer combined systems with a proprietary MES on a Database 2 (DB2) relational database.
The deviceWISE platform provided a standard interface to the factory floor. Devices and apps communicated through the platform. It didn’t encounter any performance issues that the manufacturer’s previous interface based on Open Platform Communications (OPC) had shown. The company integrated devices into MES systems using web and messaging services for its large and small plants.
deviceWISE provides connectivity that breaks through complicated layer transfers and a fast time to value. Implementing IoT empowered the manufacturer to focus on what it does best — making innovative vehicles.
Manufacturer Connects Legacy Industrial Assets to IIoT
A U.S. manufacturer in the Midwest wanted machine data from more than 1,000 global CNC machines that were at least 30 years old. Data would help predict when the equipment would require maintenance. Connecting these machines to the IoT could prevent equipment failures and avoid costly downtime.
In connecting the CNC machines, the manufacturer sought to:
- Increase parts count accuracy
- Track downtime events and related non-productive incidents
- Provide time-based information, such as part-to-part cycle and overall equipment effectiveness (OEE)
Through deviceWISE, the company connected legacy machines from multiple manufacturers within weeks. Process effectiveness increased, including improved throughput and decreased incidents of lost production.
Each station improved by two seconds per part cycle and produced an additional 50 assemblies per day. The equipment’s technological foundation enables predictive maintenance to reduce downtime and increase overall effectiveness. Data is easily accessible for enhanced decision-making from the plant floor to the boardroom.
5 Steps to Choose the Right Platform for Your Needs
When choosing an IoT platform type, it’s important to focus on specifics. How many connected machines or sensors are in the field? Which is required: data orchestration capabilities or simple device management?
Step 1: Define Your Business Objectives and IoT Use Case
Define and list the business goals you must accomplish with your IoT use cases.
- What problems are you solving?
- What types of devices or sensors will you need to connect?
- Do you need capabilities to support AI-powered functions like visual inspection in a factory?
- Are you collecting small amounts of data with battery-powered edge sensors?
Step 2: Identify Key Platform Requirements
Once your goals are defined, consider your technical requirements for an IoT platform.
- Do you need to scale up quickly and easily?
- Which types of device connectivity should the platform support?
- Do you need a no-code environment to build out organization-specific applications and dashboards for data analytics?
Step 3: Assess Vendor Offerings and Ecosystem Compatibility
Define your goals and technical needs. Assess vendors based on how well their offerings align with your use cases and ecosystem.
- Does the platform allow you to connect legacy machines (e.g., PLCs) in an industrial setting?
- Is it compatible with communication protocols like Message Queuing Telemetry Transport (MQTT)?
- Does the platform support integration with financials and other third-party applications your organization relies on?
- Does it integrate well with the cloud-based platforms you use for data collection an
