Distributed Sensor Data Processing for Trip Detection at Scale

The patent segments the data processing workload by deploying distributed processing nodes across multiple mobile devices. Each device processes sensor data locally using machine learning models, dividing the overall processing task into smaller independent units that can be executed in parallel, thereby improving processing capability while reducing the energy burden on any single device or central server.

The patent introduces intermediary components including edge servers and cloud platforms that act as mediators between mobile devices and central processing systems. These intermediaries coordinate data collection, model training, and result aggregation, enabling efficient resource utilization across the distributed system while maintaining high processing throughput.

The patent implements local processing capabilities on mobile devices through embedded machine learning models. Sensor data is processed locally rather than being transmitted raw to centralized servers, which reduces network bandwidth consumption. Each device maintains its own processing power for local inference while only exchanging necessary results or aggregated data with the network.

The patent performs preliminary data processing and filtering on mobile devices before transmission. Machine learning models pre-process sensor data locally to extract only relevant features or anomalies, reducing the volume of data that needs to be transmitted over the network while maintaining high processing power utilization for critical analysis.

The patent employs universal machine learning models that can be deployed across diverse mobile devices with varying hardware capabilities. The same model architecture serves multiple functions including trip detection, driver identification, and anomaly detection across different device types, simplifying the system while enabling scalability to heterogeneous environments.

The patent implements feedback mechanisms where processing results from distributed nodes are aggregated and used to refine models and coordinate system-wide operations. This feedback loop enables automatic adaptation and optimization, reducing the complexity of managing distributed processing by allowing the system to self-regulate and scale dynamically based on observed performance.