Kensor: Coordinated Intelligence from Co-Located Sensors
Internet of Things (IoT) is becoming more pervasive in many installations, including homes, manufacturing plants, and industrial facilities of all kinds. The data that IoT produces is a reflection of usual behavior such as daily routines and scheduled tasks, but also from unexpected behavior due to unintentional or undesirable abnormalities. Here, we focus on achieving coordinated intelligence about normal and abnormal phenomena from multiple sensors that are geographically colocated in close proximity, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring should detect any deviant variations over time. An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked. To address this problem, we identify the possible solution approach that can be used to solve its different variants. The implemented model is developed as a combination of rules and Markov Chain methods.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1606827
- Resource Relation:
- Conference: IEEE International Conference on Big Data: Second International Workshop on the Internet of Things Data Analytics (IOTDA) - Los Angeles, California, United States of America - 12/9/2019 10:00:00 AM-12/12/2019 10:00:00 AM
- Country of Publication:
- United States
- Language:
- English
Similar Records
Modular intelligent ventilator platform for new and existing residential building ventilation
STTR Phase 1 Final Scientific/Technical Report U. S. Department of Energy Award No. DE-SC0017768