Distributed Mobile Sensor Processing for Trip Role Classification
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Solution Overview
Problem
Processing large datasets of remotely captured sensor data poses challenges in terms of resource efficiency and effective analysis, particularly in determining trip modes and user roles during vehicle trips.
Innovation Solution
A distributed data processing system that receives and analyzes sensor data from mobile devices using various modules to detect trip characteristics, such as vehicle mode and user roles, and generates relevant records for storage and further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a centralized system is used to process large datasets of sensor data, then processing power is sufficient, but network bandwidth consumption increases and system complexity increases
Solution Approach 1:
The system divides the data processing function into two segments: (1) edge computing devices that perform preliminary processing and filtering of sensor data locally, and (2) centralized server that processes only the filtered and aggregated data. This segmentation reduces the volume of data transmitted over the network while maintaining sufficient processing power for analyzing large datasets.
2Power
If a centralized system is used to process large datasets of sensor data, then processing power is sufficient, but device complexity increases
Solution Approach 1:
The system architecture is segmented into distinct functional components with clear responsibilities: edge devices handle data collection and preliminary processing, while the centralized server handles complex analysis and aggregation. This segmentation distributes complexity across multiple devices rather than concentrating it all in one system, making the overall architecture more manageable and scalable.
3Measurement precision
If more computing resources are deployed to process sensor data, then analysis accuracy improves, but resource efficiency decreases
Solution Approach 1:
The system performs preliminary filtering, aggregation, and preprocessing of sensor data at the edge computing devices before transmitting to the centralized server. This preliminary action reduces the volume of data requiring intensive processing, allowing the system to maintain high analysis accuracy for critical data while improving overall resource efficiency by avoiding unnecessary computation on redundant or low-value data.
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.