Cooperative Evidence Gathering via Distributed Sensor Aggregation
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Solution Overview
Problem
Current accident reporting systems, especially for hit-and-run accidents, face challenges in quickly and efficiently gathering evidence due to the transient nature of witnesses and the potential loss of data sources, which hinders determining fault and processing insurance claims.
Innovation Solution
A distributed data processing system that enables cooperative evidence gathering through a method where computing devices with sensors aggregate and share data using an evidence request application, allowing for crowdsourced data collection from both stationary and mobile devices within proximity to an accident, determining data applicability, and transmitting relevant evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional accident reporting systems are used, then evidence gathering is simpler, but evidence collection is slow and data sources are easily lost
Solution Approach 1:
The system divides evidence gathering into multiple independent components: event detection by second computing device, data aggregation by first computing device, and collaborative participation from multiple witnesses. Each component operates semi-independently, allowing parallel evidence collection from multiple sources simultaneously, thus improving gathering speed without requiring a fully integrated complex system.
Solution Approach 2:
The system performs preliminary actions by establishing the crowdsourcing network and sensor aggregation capabilities before accidents occur. When an event is detected, the evidence gathering process can immediately begin without setup delays, as the infrastructure for data collection and witness coordination is already in place and ready to activate.
2Loss of information
If crowdsourcing is used to gather evidence from multiple sources, then data completeness improves, but system complexity and coordination difficulty increase
Solution Approach 1:
The first computing device serves as an intermediary that receives sensor data from multiple witnesses and the second computing device. It aggregates, filters, and validates this data before submission, simplifying the coordination burden by providing a centralized processing node that manages the complexity of multi-source data integration while ensuring data completeness.
Solution Approach 2:
The system employs computing devices with multiple functions: they act as witnesses with sensors, as communicators in the crowdsourcing network, and as data aggregation points. This multi-functionality allows the same infrastructure to handle diverse data types (sensor readings, location information, witness testimony) without requiring separate specialized systems for each function.
3Measurement precision
If real-time data aggregation from multiple sensors is implemented, then evidence quality improves, but processing time and computational resources increase
Solution Approach 1:
The first computing device aggregates data from multiple sensors and witnesses, but selectively processes only the most relevant and reliable data portions. It determines whether aggregated data is sufficient for evidence submission without exhaustively processing every available data point, thus maintaining evidence quality while reducing unnecessary processing time and computational overhead.
Data Source
AI summary
In an approach to cooperative evidence gathering, a first computing device receives a request for data corresponding to an event, where a second computing device detecting the event initiates the request for data. The first computing device aggregates data from one or more sensors, the one or more sensors associated with one or more first computing devices within a proximity of a location of the event. The first computing device determines whether at least a portion of the aggregated data is applicable to the event.