IoT Data Anomaly Detection

AI-based IoT Network Data Analytics

IoT Data Anomaly Detection is an application that can ingest data from numerous IoT data sources in network and alert the user of anomalous data and hence predict if the corresponding equipment has failed or is about to fail in a network.

Use Cases

  • Identification of anomalies in real time data using time series analysis and fingerprints of devices & sensors

  • Providing descriptive, predictive, and prescriptive insights related to these anomalies

  • Alerting the user of potential equipment failure

  • Automated monitoring of all equipment

Concentio Serves


Real Time IoT Network Health Detection

IoT Data Anomaly Detection by Scry Analytics Discovers correlation between sensors and devices of various types for flagging spurious data by using multiple sensors & devices close to each other, to detect anomalies in network data.

More Detail

Concentio has been built using
the following technologies

  • Bayesian networks

  • Reinforcement learning

  • Generic rule-based machine learning

  • Heuristic algorithms

  • Data mining

  • Big data integration

  • Big data analytics

  • Reverse engineering

Key Differentiators &
Business Benefits

  • Ready to Use UI

    Pre-built graphical user interface (GUI) & APIs for quick deployment & integration with clients’ existing workflow.

  • Customizable Alerts

    Customizable graphs and alerts based on prescriptive analysis as per client requirements.

  • Fingerprint

    Generates an IoT network fingerprint based on time-series data to predict anomalies in the incoming data.

  • Data Integrity

    Real-time check for missing, out of range, abrupt changes in gradient or lack of correlation with other data.