Chama is an open source Python package that uses mixed integer linear programming to determine the best location and technology for your sensor network. Optimization can help maximize monitoring effectiveness and reduce the overall cost of sensor networks. The methods in Chama can be applied to many applications, including environmental monitoring, process safety, and asset protection.
How does it work?
Optimize your sensor placement in five basic steps.
Basic steps in sensor placement optimization using Chama
- Simulation: Generate an ensemble of simulations which represent the system where the sensors will be deployed. Chama includes basic atmospheric dispersion models. The user can also load simulations results from third party software.
- Sensor technology: Define your sensor technologies’ position and mode of detection, such as stationary or mobile sensors, point detectors or cameras. Simulated technologies can include detection threshold, sensor cost, and sampling times.
- Impact assessment: Extract the impact of your scenario given your set of sensor technologies. The Chama impact module can extract detection times and convert detection time to other damage metrics.
- Optimization: For a given budget, optimize your sensor layout to minimize detection time, minimize damage, maximize scenario coverage, or maximize geographic coverage.
- Graphics: Generate maps of your site that include the optimal sensor layout and information about scenarios that were and were not detected.
Users can enter the workflow at any stage. For example, if the impact assessment was determined using other methods, that data can be loaded into Chama and used in sensor placement optimization.
For more information, contact Katherine Klise.