Design of wireless sensor nodes (WSNs) and implemented algorithms for more efficient condition monitoring systems
Plan, test, and optimize the availability of WSNs, taking into account the algorithms and resulting data transmission before the first device is deployed in the field.
The problem
The availability (uptime) of WSNs depends on their energy consumption and the energy obtained from the environment. Energy consumption, in turn, depends on the algorithms executed (local computing) and the amount of data transmitted. Typically, state monitoring problems can be solved using a wide variety of algorithms – but these can have very different execution times and energy requirements. Typically, the classification accuracy of various variants is assessed in advance. However, due to the effort involved in implementation, only a few algorithms are actually implemented on the WSN and the execution time and energy consumption are compared. This leads to decisions based on insufficient information and suboptimal availability.Our capability
The MCL maintains a WSN simulation and analysis framework that can be used to automatically execute algorithms on microcontrollers and measure their execution time and energy consumption. These results are then fed into a realistic simulation of wireless sensor nodes (WSNs) under realistic application conditions.Your benefits
We map your (WSN & algorithm) solution space – efficiently and accurately. You receive a comprehensive report that can serve as the basis for a product decision. You can then concentrate on the best solution options.
Algorithms
- Compiling Scikit-learn algorithms into executable ARM binaries
- Automated measurement of execution time and energy consumption on selected microcontrollers
- Automated reporting
- Automatic generation of description files for WSN simulation
WSN simulation
- Realistic energy models: Simulation of energy flows with solar harvesting (PV), simplified battery charging/discharging behavior, and sleep/wake states.
- Communication costs at a glance: Simplified RF energy model per bit/packet
- Cycles with real data: The core of the WSNE is the combination of different operating cycles (e.g., measurement, transmission, and sleep phases) with real-world data. Currently, historical solar irradiance data is automatically downloaded from the Internet and integrated into the energy model.
Uptime optimization — We provide metrics that allow you to evaluate how stable the system runs over days, weeks, and seasons—including worst-case weather and peak load options.
| Dr. Julien Magnien | services(at)mcl.at | +43-676 848883 640 |
