Skip to main content

Azure IoT




Azure IoT Hub is a scalable, multi-tenant cloud platform (IoT PaaS) that includes an IoT device registry, data storage, and security. It also provides a service interface to support IoT application development. Azure IoT helps you to securely connect millions of Linux, iOS, Android, Windows, and real-time operating system (RTOS) devices to reliably send telemetry and receive commands from application back-end in the cloud


  • IoT Hub helps connect your devices to Azure
  • Millions of simultaneously connected devices
  • Per-device authentication
  • High throughput D2C messaging
  • Reliable C2D messaging



Overview of Azure IoT Hub


Internet Protocol capable devices can be connected directly to IoT Hub. Private Area Network devices (devices connected through Bluetooth, NFC, RFID, Zeegbee, Home automation through PLC) are connected through Field gateway to IoT Hub / Cloud protocol gateway.  

Role of IoT Hub


  • Event processing and insight
  • Device business logic
  • Connectivity and monitoring
  • Application device provisioning and management



Comments

Popular posts from this blog

ABOD and its PyOD python module

Angle based detection By  Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek  Ludwig-Maximilians-Universität München  Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By  Yue Zhao   Zain Nasrullah   Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada  Zheng Li jk  Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that  uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing.  Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based sim...

Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems

  Ranwa Al Mallah , Godwin Badu-Marfo , Bilal Farooq image Courtesy: Comparitech Abstract Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The threat is due to the intrinsic characteristics of the wireless medium and is caused by the characteristics of the vehicular networks such as high node-mobility and rapidly changing topology. Most cyber defense techniques depend on highly reliable and connected networks. This paper explores falsified informat...

MLOps Drivenby Data Quality using ease.ml techniques

 Cedric Renggli, Luka Rimanic, Nezihe Merve Gurel, Bojan Karlas, Wentao Wu, Ce Zhang ETH Zurich Microsoft Research Paper Link ease.ml reference paper link Image courtesy 99designes Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong dependency between the quality of a machine learning model and the quality of the data used to train or perform evaluations. In this work, we demonstrate how different aspects of data quality propagate through various stages of machine learning development. By performing joint analysis of the impact of well-known data quality dimensions and the downstream machine learning process, we show that different components of a typical MLOps pipeline can be efficiently designed, providing both a technical and theoretical perspective. Courtesy: google The term “MLOps” is used when this DevOps process is specifically applied to ML. Diffe...