Neighborhood Data Aggregation for Automated Homes
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Solution Overview
Problem
There is a need for a system that can aggregate and analyze data from multiple automated environments across a neighborhood to optimize resource usage and behavior patterns, as existing solutions lack the ability to efficiently collect and share information on a neighborhood scale.
Innovation Solution
An automated environment system that generates environment data bundles containing resource consumption and user activity patterns, which are then aggregated by a server using neighborhood keys and environment identification keys, allowing for statistical analysis and pattern recognition to provide optimized behavior insights to participating environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from multiple automated environments across a neighborhood, then the quantity and quality of behavior pattern information is improved, but the system complexity and data aggregation infrastructure requirements increase
Solution Approach 1:
A server acts as an intermediary to collect environment data bundles from multiple automated environments, aggregate the data, and distribute neighborhood data bundles back to participants. This mediator handles the complexity of cross-environment data collection and analysis, allowing individual environments to benefit from aggregated insights without directly managing the complex aggregation infrastructure.
Solution Approach 2:
The system segments data collection and processing into distinct components: individual automated environments generate their own environment data bundles locally, the server performs centralized aggregation and analysis, and then distributes results. This segmentation allows each component to operate independently at its own level of complexity while contributing to the overall system functionality.
2Loss of energy
If neighborhood-level aggregation is implemented, then resource consumption optimization opportunities are improved, but the loss of privacy and location information increases
Solution Approach 1:
The system uses anonymized copies of environment data for aggregation purposes. Environment data bundles containing resource consumption and behavior pattern information are collected and processed, but the original identifying information is stripped or obscured. The resulting neighborhood data bundles provide optimization insights without revealing specific location or personal information about individual environments.
Solution Approach 2:
The system transforms detailed environment-specific data into aggregated statistical parameters that preserve useful behavioral patterns while removing identifying information. By changing the representation from specific environment records to generalized neighborhood statistics, the system maintains energy optimization value while protecting privacy.
Data Source
AI summary
Behavior information can be aggregated across multiple automated environments (e.g., across homes in a neighborhood). The automated environments can provide information about detected environment-level behavior patterns to a server. The server can aggregate the patterns across environments in a defined neighborhood and can provide neighborhood-level information back to the participating automated environments. The neighborhood-level information can be used to drive decisions and behavioral changes in individual automated environments.