Aggregating Sensor Data for Localized Environment Scores
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Solution Overview
Problem
Existing weather monitoring systems, relying on sparse and distant weather stations, provide imprecise and irrelevant weather data at specific locations due to variations in environmental conditions such as cloud cover and rainfall within short distances.
Innovation Solution
A system that collects sensor data from low-cost sensors at various locations, aggregates data to generate accurate and localized environment condition scores for areas, using communication components to process and transmit data to a remote management system for aggregation and action-based decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If weather station data is used for general area weather information, then broad coverage is achieved, but precision at specific locations deteriorates
Solution Approach 1:
The system divides the broad weather monitoring area into multiple discrete sensor locations (homes, buildings) throughout the neighborhood. Each location independently measures local environmental conditions, transforming a single large-scale measurement system into multiple localized measurement points that collectively provide both broad coverage and location-specific precision.
Solution Approach 2:
The system transitions from a single-point weather station measurement to a distributed spatial network of sensors across the neighborhood. By adding the spatial dimension of multiple measurement locations, the system simultaneously achieves broad area coverage while providing precise local measurements through aggregation of data from nearby sensors.
2Device complexity
If a single weather station is used, then system complexity is reduced, but reliability of data at distant locations deteriorates
Solution Approach 1:
The system merges data from multiple independent sensor locations into a unified aggregated environment dataset. By combining measurements from numerous distributed sensors, the system achieves both structural simplicity (a single aggregation system) and high reliability (multiple data sources) simultaneously.
Solution Approach 2:
The system implements feedback through continuous collection and aggregation of environmental data from multiple sensor locations. This feedback loop enables the system to maintain reliable data by continuously updating the aggregated environment scores based on real-time measurements from the distributed sensor network.
3Measurement precision
If multiple sensors are deployed at various locations, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system employs universal multi-functional sensors at each location that can measure multiple environmental parameters (light, temperature, humidity, etc.). This universality allows precise localized measurements without proportionally increasing system complexity, as each sensor performs multiple functions rather than requiring separate specialized sensors for each parameter.
Solution Approach 2:
The system uses identical or similar sensor units deployed across multiple locations, creating a replicated sensing architecture. This copying approach standardizes the measurement process and simplifies system management while achieving high localized precision through the replicated network of sensors.
4Measurement precision
If aggregated environment data is provided for an area, then location-specific accuracy improves, but loss of individual location information increases
Solution Approach 1:
The system preserves local quality by maintaining and utilizing environment scores from each individual sensor location while generating the aggregated area dataset. Each location's unique environmental characteristics are preserved in the local scores, which feed into the broader aggregated dataset, ensuring both local specificity and area-wide accuracy are maintained.
Data Source
AI summary
Described are systems, methods, and apparatus that gathers environment condition data from different sensors at various locations within an area, aggregates the environment condition data to produce aggregated environment condition scores for the area and provides the aggregated environment condition scores to different locations within the area. While sensor data from a single sensor/device, such as a camera may provide low quality environment information, by collecting and aggregating information from multiple sensors and/or locations in the area, highly accurate aggregated environment condition scores for environment conditions may be realized. The aggregated environment condition scores may be provided to various locations within the area as representative of the environment condition at that point in time within the area, regardless of whether those locations have sensors. The aggregated environment condition scores may be used by other devices at those locations to automate one or more actions, such as adjusting lighting conditions, closing garage doors, adjusting window blind positions, etc.