Wilderness Preservation AI Sensor Networks
The CHALLENGE:
Setting policies aimed at preserving and recapturing vital natural areas is simply the starting point. To have impact, such policies require cost-effective approaches to monitor and enforce to make real progress. The remoteness and lack of human impact that defines these regions also make them some of the most challenging to effectively monitor. Low to zero-impact technologies are needed to fulfill the data insight and policy enforcement objectives without doing more harm than good.
Where SensiML Can Help
By using TinyML sensor models, complex sensor information can be analyzed where it is captured using ultra-low powered solar and long-life battery powered devices that require little or no intervention nor the support of intrusive high-power network infrastructure to pipe volumes of data to the cloud for processing.
Smart Home Energy Conservation
The CHALLENGE:
Devices and technology aimed at reducing energy consumption usually succeed or fail based on often fragile return-on-investment/payback analysis. Modest gains over time are quickly turned upside down by offsetting initial device costs needed to realize the promised gains.
Where SensiML Can Help
By combining today’s low-cost digital sensors and capable microcontrollers with adaptive machine learning algorithms capable of extracting multi-model insight from low-cost hardware, not only can the energy-saving ROI persuasion become stronger, but also the ability to transform such equipment into smart home goods with multi-benefit consumer selling points. For example, an HVAC virtual mechanic monitoring device can track equipment efficiency, recognize and advise on known fault patterns, and provide new capabilities like multi-zone control leveraging common hardware adders to provide sellable consumer benefits as well as energy savings.
Smart Sensor Aided Disaster Response and Preparedness
The CHALLENGE:
The expanding scope of climate driven disasters demands new solutions that can scale with the dramatically increased scope of coverage needed for both early warning of events and response to those events. Without effective technology to aid the battle, local, state, and federal budgets are being rapidly drained in efforts to keep pace with the rate of change of such disasters.
Where SensiML Can Help
The application of smart edge sensing solutions can play a role on multiple fronts of this disaster response battle. For early warning, remote low-power sensor networks and sensor equipped drones can provide monitoring and critical minutes in identification of wildfires at their outset. First responders can benefit from gesture and activity recognition devices that aid in the coordination and communication in chaotic fast response settings.
Use Cases
Ready for the Real World?
Try Proto Edition which comes enabled by default to generate ML models for motion, vibration, gesture recognition, and countless other subsonic sensor (i.e. requiring <10kHz sample rate) real world applications.
