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.
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
& equipment firms
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
- 25+ proprietary AI based algorithms
- Can simultaneously accommodate more than ten million variables (i.e., devices and sensors)
- Real time validation against system generated network fingerprint of each device and sensor
- Noise detection and reduction using specific filters
- Time-series, and frequency domain analyses
- Real time dashboards and detailed view
- Reinforcement learning via human intelligence and machine learning
Concentio has been built using
the following technologies
Generic rule-based machine learning
Big data integration
Big data analytics
Key Differentiators &
Ready to Use UI
Pre-built graphical user interface (GUI) & APIs for quick deployment & integration with clients’ existing workflow.
Customizable graphs and alerts based on prescriptive analysis as per client requirements.
Generates an IoT network fingerprint based on time-series data to predict anomalies in the incoming data.
Real-time check for missing, out of range, abrupt changes in gradient or lack of correlation with other data.