Internet of Things (IoT) service

Netsis’ IoT Edge is an Internet of Things (IoT) service that builds on top of IoT Hub. This service is meant for customers who want to analyze data on devices, a.k.a. “at the edge”, instead of in the cloud. By moving parts of your workload to the edge, your devices can spend less time sending messages to the cloud and react more quickly to changes in status.


All gateway patterns provide the following benefits:

Edge analytics – Use AI services locally to process data coming from downstream devices without sending full fidelity telemetry to the cloud. Find and react to insights locally and only send a subset of data to IoT Hub.
Downstream device isolation – The gateway device can shield all downstream devices from exposure to the internet. It can sit in between an IT network which does not have connectivity and an IT network which provides access to the web.
Connection multiplexing – All devices connecting to IoT Hub through an IoT Edge device will use the same underlying connection.
Traffic smoothing – The IoT Edge device will automatically implement exponential backoff in case of IoT Hub throttling, while persisting the messages locally. This will make your solution resilient to spikes in traffic.
Limited offline support – The gateway device will store locally messages and twin updates that cannot be delivered to IoT Hub.
• Security - the device runs within your own premises, so all data stays in your own organisation unless you allow external access. You know where your information assets are, and this is critical for any compliance audit. You have full control over your information and infrastructure assets.
• Portability – Support most popular formats and users can easily clone your VM to other formats or hardware.


Edge computing offers four primary advantages over traditional networking:

  • Edge computing meets the needs of applications for real-time high performance. Delays in response time for control functions processed in the cloud will often be too long; therefore, some classes of analysis and control functions must be implemented at the network edge to meet specific, real-time service requirements. For example, in production control, the maximum delay in service control is often 10 ms or less. For automated driving, control delays must be within several milliseconds.
  • Due to increases in local storage capacity, edge computing is well-suited to handle data adaptation and aggregation tasks. This approach is useful for sensing and control layers that involve the unification of complex, heterogeneous communications technologies and data protocols.
  • If bulk IoT data were sent from edges to data centers for processing, the cost of network operations would be unnecessarily high. For instance, temperature sensors need only report abnormal changes to the data center. And for facial recognition, only a few key characteristics need to be uploaded to the data center. In warehousing, only fulfilled picking orders or abnormal delivery need to “alert” the Warehouse Management System (WMS).
  • Edge computing is reliable. Data center processing adds a level of complexity which increases risk in many industrial applications. For maximum reliability, edge systems must maintain a certain level of autonomy; for manufacturing control systems, the collaboration between distributed intelligence and autonomous systems allows network edges to help secure the survivability of individual nodes and the entire system. Even with a basic system like the warehouse autonomous mobile robots, local controllability ensures the mobile robots continues to operate even the internet connectivity goes offline.

Our focus for the IoT service is on the data collecting at the edge

1 Focus on Data


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