Distributed Sensor Data Processing for Bandwidth-Efficient Trip Analysis
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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 effectively utilize processing power and network bandwidth.
Innovation Solution
A distributed data processing system that receives and analyzes sensor data from mobile devices, employing various data processing modules to detect trip-related characteristics such as vehicle mode, user roles, and places of interest, and generates relevant trip records and user data for storage in dedicated databases.
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 consolidated, but network bandwidth consumption increases and system complexity increases
Solution Approach 1:
The system segments the data processing function into two parts: (1) edge devices perform local preprocessing and filtering of sensor data to reduce data volume, and (2) the centralized server processes only the essential aggregated data. This segmentation reduces network bandwidth consumption while maintaining centralized processing power for critical operations.
Solution Approach 2:
The system performs preliminary data processing and filtering at the edge devices before data is transmitted to the centralized server. This preliminary action reduces the volume of data that needs to be transmitted over the network, thereby reducing network bandwidth consumption while still utilizing centralized processing power for final analysis.
2Loss of energy
If a distributed system is used to process sensor data locally, then network bandwidth is reduced, but processing power at individual devices is insufficient
Solution Approach 1:
The system segments processing tasks by complexity: simple filtering and aggregation are performed locally at edge devices, while complex analysis and pattern recognition are performed at the centralized server with sufficient processing power. This segmentation allows the system to reduce network bandwidth while still handling computationally intensive tasks.
Solution Approach 2:
The system merges the processing capabilities of multiple edge devices with the centralized server to create a hybrid processing architecture. This combination allows the system to leverage both the local processing power of individual devices and the aggregate processing power of the centralized system, overcoming the limitations of either approach alone.
3Measurement precision
If all sensor data is transmitted to a centralized server, then data analysis accuracy is improved, but network bandwidth consumption and system complexity increase
Solution Approach 1:
The system extracts only the essential features and aggregated statistics from raw sensor data at the edge devices before transmission to the centralized server. This extraction process maintains data analysis accuracy by preserving critical information while removing redundant data, thereby reducing network bandwidth consumption.
Solution Approach 2:
The system performs preliminary data aggregation and feature extraction at edge devices before centralized analysis. This preliminary action ensures that the most important data characteristics are preserved and prepared for accurate analysis at the server, while reducing the overall data volume that consumes network bandwidth.
4Speed
If data processing is performed in real-time, then responsiveness is improved, but processing power requirements and system complexity increase
Solution Approach 1:
The system segments real-time processing into two stages: (1) immediate local processing at edge devices for time-critical operations, and (2) subsequent centralized processing for less time-sensitive analysis. This segmentation enables the system to maintain responsiveness for critical operations while reducing overall processing power requirements by distributing tasks appropriately.
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.