Streetlight Sensor Fusion for Accurate Incident Location Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensors face challenges in accurately detecting illegal activities due to false positives or missed detections, making it difficult to effectively monitor and respond to incidents such as crimes, accidents, or emergencies in an environment.
Innovation Solution
A sensor device equipped with various sensors and machine learning algorithms processes data to identify incident types and locations, selects additional sensors for detailed information, and communicates with other devices to refine incident data, using machine learned models to differentiate between safe and illegal activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sensor is used to detect illegal activity, then device complexity is reduced, but measurement precision and reliability of incident detection deteriorate due to false positives and missed detections
Solution Approach 1:
The system segments the detection task by deploying multiple sensors at different locations rather than using a single complex sensor. Each sensor captures data from its local area, and the computing device integrates these segmented data sources to achieve comprehensive incident detection with improved accuracy while keeping individual sensor complexity low
Solution Approach 2:
The system merges data from multiple sensors and combines it with incident data from various sources (police reports, news articles, social media) to create a comprehensive view of the environment. This merging of multiple data streams improves measurement precision by cross-validating information and reducing false positives
2Measurement precision
If multiple sensors at different locations are deployed to improve incident detection accuracy, then measurement precision improves, but device complexity and data processing requirements increase
Solution Approach 1:
The computing device serves multiple functions: it receives sensor data from multiple sensors, processes incident data from various external sources, determines incident types and locations, identifies patterns, and generates alerts. This multi-functionality consolidates complexity into a single processing unit rather than distributing it across multiple specialized devices
Solution Approach 2:
The computing device acts as an intermediary that mediates between multiple sensors and the final incident detection output. It receives raw data from sensors, processes and correlates this data with external incident information, and produces refined incident detections. This intermediary role simplifies the overall system architecture by centralizing the complex processing logic
3Loss of information
If sensor data is processed to determine detailed incident information, then information completeness improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing sensor data and incident information before incidents occur. The computing device maintains an updated database of environmental data, sensor baselines, and incident patterns, so when an incident is detected, the analysis can be performed more quickly using pre-prepared reference data
Solution Approach 2:
The system replaces traditional mechanical/incident-by-incident analysis with machine learning models and pattern recognition algorithms. These computational models automatically analyze sensor data and incident information to quickly determine incident types, locations, and patterns, reducing processing time while maintaining or improving information completeness
Data Source
AI summary
Techniques for determining information about an incident by one or more sensors in a streetlight, utility meter, and/or other device are described herein. A device proximate a sensor(s) can process sensor data to determine an incident type and/or an incident location in an environment. For example, the device can detect presence of a weather event, an accident, a crime, a crowd control event, a fire, a flood, and so on. In some examples, the device can identify other sensors in a vicinity of the incident (e.g., attached to other streetlights, utility meters, transformers, etc.), and select sensors for gathering additional information about the incident based on the incident type and/or the incident location.