4 scenarios to help jumpstart your IoT strategy
Derive value from connected devices and applications with these tips
Cloud computing acted as a catalyst in accelerating IoT adoption among enterprises. Cloud made it possible to connect devices to traditional line-of-business applications such as ERP and asset-tracking platforms.
Though the devices have been capable of generating telemetry data, it was not captured, processed and analyzed by businesses. The evolution of communication technologies such as LTE and 5G, combined with cloud, enabled organizations to take advantage of the insights delivered by applications that are connected to the devices.
Cloud-based enterprise Internet of Things (IoT) platforms are making it possible for organizations of any size to build connected applications. These modern platforms can effortlessly deal with tens of devices to millions of devices. The self-service provisioning and pay-by-use pricing remove the barriers to IoT adoption.
As enterprises start to evaluate IoT solutions, here are four scenarios that help them jumpstart their connected device strategies.
1. Device-to-device communication
Device-to-device communication or machine-to-machine (M2M) communication is the fundamental use case of IoT. By connecting two local or remote devices, organizations can achieve efficiency while avoiding disruption to production.
The IoT platform orchestrates the communication between the devices based on predefined rules and business logic. A simple example of device-to-device communication is controlling HVAC based on the ambient temperature reported by a thermostat.
In industrial scenarios, manufacturing equipment in two production units is connected to the cloud-based IoT platform. When there is a disruption detected in one of the units, the equipment in the remote site is automatically turned on to maintain the expected production level.
Contemporary IoT platforms deliver M2M capabilities out of the box. They instantly bring value to connected devices by orchestrating the workflow among devices.
2. Centralized command & control
If the previous scenario made devices to talk to each other, this use case focuses on connecting devices to software. By enabling remote access to devices from applications, engineers can control them from anywhere.
Desktop, web and mobile applications become remote controls to devices deployed across multiple geographies. In scenarios where a simple reset operation would fix a device, an application can be used to remotely initiate it. Mobile and wearable applications deliver tremendous value by empowering technicians and engineers to control the field equipment from remote locations.
Cloud-based IoT platforms expose API for applications to send commands to the remote devices. This scenario reduces support cost through remote access of devices.
3. Remote monitoring
Connected devices are capable of streaming telemetry and state information to the cloud. By ingesting the data from remote devices to a centralized repository, key stakeholders can continuously monitor the device state. When a device loses connectivity or reports an unusual usage pattern, it can be easily isolated for investigation and diagnosis.
Even if the devices do not support remote control, monitoring the state proves to be valuable for enterprises. By incorporating predefined rules to trigger actions, organizations can alert appropriate teams when a device malfunctions.
This scenario extends the previous one through intelligent actions. The rules engine may be replaced with an intelligent machine-learning algorithm that can learn from the historical data for predictive maintenance of devices.
4. Business intelligence
The telemetry data ingested by the connected devices holds key insights. It can be used for improving operational efficiency, production efficiency and resource optimization. While real-time data is used for monitoring the state of the devices, historical data can be aggregated over a period of time to discover actionable insights.
For example, the telemetry ingested by connected vehicles can be collected, aggregated, processed and analyzed to derive driving patterns, fuel efficiency, route optimization and fleet management. The historical data can be channelized to enterprise data warehouses to correlate it with existing data.
Enterprise IoT platforms are complemented by Big Data platforms powered by Apache Hadoop and Apache Spark. The data processing pipelines connect these platforms with traditional business intelligence solutions to analyze and visualize device data.
IoT applications can take advantage of one or more of the above scenarios to derive value from connected devices and applications.