Knowing the way ….. Industrial IoT solution implements on GCP — Part I
Industrial IoT(IIoT) is one of the booming technology in the world. More than 25 billion devices run with internet connectivity, and it is increasing day by day. Mainly IIoT is used to accelerate business agility, Machine learning on edge, Improve operational efficiency, Localization intelligence, etc.
As an example,
- Real-time asset tracking: The assets are tracked by embedded devices in real-time and perform complex analytics, and machine learning on the data collected, and assess your business's status to deliver actionable insights.
- Predictive maintenance: The self-check capability of equipment when they need maintenance is ideal. It also optimizes equipment performance in real-time, predicts downtime, detects anomalies, and tracks device status, state, and location.
- Logistics and supply chain management: Embedding cloud-connected sensors and devices in company transport vehicles can improve the management of the fleet, inventory tracking, and cargo integrity monitoring.
- Smart Cities and buildings: Embed cloud-connected sensors and devices in buildings and infrastructure. Build a comprehensive solution that spans across billions of sensors and edge devices and brings a new level of intelligence and automation to entire homes, buildings, or cities.
Challenges of IIoT
Several factors can be identified as compelling points.
- Connectivity: At present, IoT relies on a server/client model to authenticate, authorize, and connect devices to nodes in the network. Although this model works for hundreds or even thousands of devices, it will become unworkable as numbers grow to the millions and billions per network. Without proper throughput design considerations, bottlenecks may occur during the information exchange at the server. In the future, off-loading tasks to the edge will become important. This means that IoT networks will need devices capable of handling data analysis, machine learning, and data gathering.
- Security and compliance: As you may have seen, hacking IoT devices has already occurred. Everything from baby monitors to cars to refrigerators has been exploited. As networks grow, without adequate security, each added node can become a potential opening for hacking.
- Brownfield deployment (legacy infrastructure): As IoT devices and networks age and new technology emerges, brownfield deployment will become an issue. Companies will need to confront the task of integrating new devices and technologies into existing networks.
- Get actionable intelligence from data: The value of data increases as the ability to get actionable intelligence from it increases. IoT analysis will need to handle unstructured data, massive amounts of real-time data, and outliers in real-time.
IoT architectures must be capable of scaling connectivity of devices, data ingestion, data processing, and data storage. They must be able to do this quickly while still producing real-time data insights. Sending ever-increasing amounts of data to the cloud slows processor times and requires more bandwidth to transfer and store data.
To mitigate this demand, distributed computing, known as fog or edge computing, is gaining popularity. The edge refers to the geographic distribution of computing nodes in the network as the Internet of Things devices, which are at the “edge” of a network. This, in turn, increases the demand for devices that are capable of cleaning, processing, and analyzing data locally. The result is that only cleaned metadata is sent to the cloud.
Why GCP, Let’s discuss in the next article….. Stay Tuned…