The startup develops machine learning-based environmental data monitoring software that utilizes smartphone sensors to collect localized measurements of temperature, humidity, air quality, and other microclimate factors. This technology enables municipalities to monitor environmental conditions, optimize energy usage, and enhance public health planning through precise hyper-local weather reporting.
Funding
$9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional environmental monitoring relies on sparse networks of fixed sensors, resulting in limited spatial resolution and an inability to capture hyperlocal variations in temperature, air quality, and other microclimate factors. This lack of granular data hinders effective urban planning, energy optimization, and public health initiatives.
Solution
Mobile Physics leverages the ubiquity of smartphones to create a dense, real-time environmental monitoring network. The company's EnviroMeter SDK transforms smartphones into personal environmental sensors, collecting hyperlocal measurements of temperature, humidity, air quality, light intensity, UV exposure, and noise levels without requiring additional hardware. Data is aggregated from diverse sources, including satellites and digital appliances, and analyzed using machine learning algorithms to uncover patterns and trends. This enables the creation of a high-resolution environmental database that provides actionable insights for various applications, including smart cities, utilities, and personal health and well-being.
Target Audience
The primary target audience includes municipalities seeking to track and manage their environment, utilities aiming to optimize energy consumption, and individuals interested in monitoring their personal exposure to various environmental conditions.
Features
- EnviroMeter SDK that utilizes existing smartphone sensors to measure environmental variables
- Hyper-local accuracy achieved through a dense network of smartphone-based sensors
- Real-time (nowcast) and predictive (forecast) air quality assessments
- Machine learning algorithms for generating actionable analytics and insights from environmental data
- Environmental data warehouse accessible to weather forecasting companies, utilities, and municipalities
- Integration with Qualcomm's Always Sensing Hub and ST's DTOF sensor
- ISO17025 certified measurements, ensuring accuracy comparable to state-of-the-art devices