Crowdsourced Sensor Network for Earthquake Prediction
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Solution Overview
Problem
Current systems face challenges in accurately predicting future events, such as weather or earthquakes, due to numerous unknown variables, leading to frequent errors and inadequate warnings, which can result in damage and inefficiencies.
Innovation Solution
A network of distributed sensors, including smart-home devices like thermostats and hazard detectors, transmit data to a central server for aggregation and analysis, enabling the identification of sensor malfunctions, stationary events, or moving events, and predicting future characteristics of events like earthquakes, allowing for timely alerts and preparations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a network of distributed sensors is deployed to improve event prediction accuracy, then measurement precision and reliability are improved, but device complexity and infrastructure requirements worsen
Solution Approach 1:
The patent repurposes existing smart-home devices (thermostats, security systems, smoke detectors) for their primary functions plus event detection. These universal devices serve multiple purposes: climate control, security monitoring, and earthquake/weather event detection, eliminating the need for dedicated sensor infrastructure while improving measurement precision through crowdsourced data from numerous locations
Solution Approach 2:
The system leverages the existing ubiquity and deployment of smart-home devices throughout communities. These devices are already installed and operational in residences, providing self-service event detection capabilities without requiring additional infrastructure installation. The devices automatically transmit detection data to the central server, enabling accurate event prediction while avoiding the complexity of deploying and maintaining a dedicated sensor network
2Reliability
If more sensors are deployed to reduce false alarms, then reliability is improved, but loss of time for data processing and analysis worsens
Solution Approach 1:
The patent segments the data processing workload by distributing sensors across many independent locations. Each sensor independently monitors its local environment and transmits data asynchronously to the central server. This segmentation allows parallel processing of multiple data streams, reducing the time penalty associated with analyzing data from numerous sensors while maintaining high reliability through the aggregated coverage of many distributed detection points
Solution Approach 2:
The system implements feedback mechanisms where the central server analyzes incoming sensor data and sends alerts back to relevant devices and users. The feedback loop enables real-time event detection and notification, allowing the system to process and act on sensor data efficiently. By using feedback to trigger selective processing based on actual events rather than continuously analyzing all data, the system reduces false alarms while minimizing data processing time
Data Source
AI summary
Systems and methods for forecasting events can be provided. A measurement database can store sensor measurements, each having been provided by a non-portable electronic device with a primary purpose unrelated to collecting measurements from a type of sensor that collected the measurement. A measurement set identifier can select a set of measurements. The electronic devices associated with the set of measurements can be in close geographical proximity relative to their geographical proximity to other devices. An inter-device correlator can access the set and collectively analyze the measurements. An event detector can determine whether an event occurred. An event forecaster can forecast a future event property. An alert engine can identify one or more entities to be alerted of the future event property, generate at least one alert identifying the future event property, and transmit the at least one alert to the identified one or more entities.