Anomaly Detection in Time Series Datasets of Internet of Things Devices

This presentation will describe advances in identifying anomalies in datasets of internet of things (IoT) devices found in commercial buildings. Leveraging properties specific to machine­-to­-machine communications, data models and algorithms can be constructed from the time series datasets to identify security threats (i.e. cyber attacks) and failing devices (predictive maintenance). Devices modeled include security cameras, lighting and heating control, and various sensors and actuators. 

Big data techniques can also be applied to the communication datasets, including various forms of clustering and fuzzy logic/fuzzy inferencing to identify deviations from real­-world normal operations. In such situations, modularity is key to proper implementation in order to allow for proper data processing, algorithm training and algorithm testing. 

Results of current research and future roadmap will be discussed.

About Optigo Networks

Optigo Networks is shaping the future of the commercial Internet of Things (IoT) by redefining how smart buildings are connected and operated. By applying visualization and anomaly detection to the building system, Optigo allows the IoT to scale, driving down the cost to maintain and operate the technologies that make buildings comfortable and efficient.

With its award-­winning software, Optigo Networks allows building operators to quickly identify faults and security threats in the building system, cutting troubleshooting time down from hours to minutes. Built­in analytics rein in the building IoT, reducing OpEx and maintenance costs with tools and reports to visualize the health and security of the building network.