Crowdsourced Sensor Data Aggregation for Real-Time Incident Detection
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Solution Overview
Problem
Early detection of events in crowded public venues is challenging, making it difficult to prevent or respond to negative incidents effectively, as existing systems primarily focus on individual sensor data rather than collective telemetry data from a cohort.
Innovation Solution
A system and method for crowdsourcing data analysis that aggregates and maps data from multiple devices to infer the likelihood of potential incidents, binding data to physical or logical locations, and presents visual cues through graphical representations, enabling real-time monitoring and early warning systems for minor, major, and critical events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual sensor data is used to detect events, then the system complexity is low, but the ability to detect potential incidents in crowded venues is insufficient
Solution Approach 1:
The patent combines data from multiple sensors and multiple individuals into a unified cohort-level analysis. The system aggregates sensor data from numerous devices (smartphones, wearables) and integrates it with venue information to create a comprehensive view of crowd behavior, enabling detection of potential incidents that would be invisible to individual sensors alone.
Solution Approach 2:
The system serves multiple functions simultaneously: it monitors individual behaviors, detects crowd patterns, identifies potential incidents, and provides real-time alerts. By making the system multi-functional, it justifies the increased complexity through enhanced capability to handle diverse detection tasks across different venue types and incident scenarios.
2Loss of time
If real-time monitoring of cohort data is implemented, then early detection of incidents is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary processing by pre-defining cohorts, establishing baseline behaviors, and pre-identifying risk indicators. Venue-specific parameters and individual baseline data are processed in advance, allowing the real-time monitoring phase to focus only on detecting deviations from established patterns, thereby reducing the computational burden during critical detection moments.
Solution Approach 2:
The data processing is segmented into distinct stages: data collection from multiple sources, cohort assignment, baseline establishment, real-time anomaly detection, and alert generation. This segmentation allows each stage to be optimized independently and enables parallel processing of different data streams, managing overall complexity while maintaining real-time capability.
3Quantity of substance
If multiple sensors and devices are integrated, then the quantity of data available for analysis increases, but the difficulty of detecting and measuring potential incidents increases
Solution Approach 1:
The system applies different analysis methods to different data sources and different cohorts based on their specific characteristics. Venue-specific parameters, individual baseline behaviors, and crowd density levels are used to locally optimize detection algorithms, making the system adaptable to varying conditions rather than using a single uniform approach across all data.
Solution Approach 2:
The patent introduces intermediate processing layers including cohort-level aggregations, baseline comparison mechanisms, and anomaly detection algorithms that act as mediators between raw sensor data and final incident detection. These intermediaries translate complex multi-source data into meaningful patterns, reducing the difficulty of detecting incidents despite the increased quantity and variety of data sources.
Data Source
AI summary
A crowdsourcing data analysis operation receives data from a plurality of crowd sourced devices, aggregates the data received from the plurality of crowd sourced devices and maps the data received from the plurality of crowd sourced devices to a cohort (i.e., a group of individuals used in a study who have something in common). In certain embodiments, the crowdsourcing data analysis operation analyzes the data received from the plurality of crowd sourced devices to provide a deterministic analysis to infer a likelihood of potential incidents related to a group of individuals at any given time. Additionally in certain embodiments, the mapping of the data received from the plurality of crowd sourced devices includes binding the data to a physical or logical location.