Distributed Sensor Data Processing for Trip Detection at Scale
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Solution Overview
Problem
Processing large datasets of remotely captured sensor data poses challenges in terms of resource efficiency, particularly in deploying and configuring systems that can effectively analyze such data while optimizing computing power and network bandwidth.
Innovation Solution
A distributed data processing system that receives and analyzes sensor data from mobile devices, utilizing various modules to detect trip characteristics, such as vehicle mode and user activities, and generates trip records and user data, while also updating geo-fence configurations and tracking vehicle locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a system is deployed to process large datasets of sensor data, then the processing capability is improved, but the resource consumption (computing power and network bandwidth) increases
Solution Approach 1:
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 parallel operations that reduce the burden on any single system and minimize centralized network bandwidth requirements.
Solution Approach 2:
The patent introduces edge computing capabilities as an intermediary between sensor data collection and centralized processing. Mobile devices with onboard processors and machine learning models act as intermediaries that pre-process and filter sensor data before transmitting only essential information to centralized systems, reducing network bandwidth consumption while maintaining processing capability.
2Productivity
If distributed processing nodes are deployed across multiple mobile devices, then the processing capacity is improved, but the system complexity increases
Solution Approach 1:
The patent employs universal machine learning models that can run on various mobile device platforms with different hardware configurations. These standardized processing algorithms provide consistent data analysis capabilities across diverse devices, simplifying system integration and management despite the distributed architecture.
Solution Approach 2:
The patent implements feedback mechanisms where centralized systems monitor and coordinate the distributed processing nodes, adjusting model parameters and data collection strategies based on performance metrics. This feedback loop maintains system efficiency and reduces complexity by providing centralized oversight of the distributed operations.
Data Source
AI summary
Aspects of the disclosure relate to processing remotely captured sensor data. A computing platform having at least one processor, a communication interface, and memory may receive, via the communication interface, from a user computing device, sensor data captured by the user computing device using one or more sensors built into the user computing device. Subsequently, the computing platform may analyze the sensor data received from the user computing device by executing one or more data processing modules. Then, the computing platform may generate trip record data based on analyzing the sensor data received from the user computing device and may store the trip record data in a trip record database. In addition, the computing platform may generate user record data based on analyzing the sensor data received from the user computing device and may store the user record data in a user record database.