Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information
This addresses the challenge of efficient sensor placement for emergency response in industrial settings, but it is incremental as it builds on existing methods for a specific domain.
The paper tackles the problem of optimizing emergency sensor deployment in industrial plants by using AI techniques to identify high-value regions and applying Expected Value of Information to select sensor types and locations, resulting in a framework tested on the Tennessee Eastman process dataset.
Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios.