Cloud Building Monitoring Architecture for Private, Scalable Sensor Data

The patent introduces standardized data collection modules and communication protocols (MQTT, HTTP, RESTful APIs) as intermediaries between diverse sensor devices and the central platform. These intermediaries translate various sensor data formats into a unified structure, enabling accurate data aggregation without requiring complex custom integration for each device type. The standardized modules handle data validation, time-stamping, and formatting, thereby maintaining measurement precision while reducing system complexity.

The system employs universal data collection modules that can interface with multiple types of sensor devices through standardized protocols. The cloud-based platform provides multi-functional capabilities including data collection, storage, analysis, and visualization through a single unified system. This universality allows the system to handle diverse sensor inputs (environmental metrics, occupancy data, energy consumption) without requiring separate specialized systems for each function, thereby improving measurement precision across multiple parameters while controlling overall device complexity.

The patent segments data processing into multiple hierarchical levels: edge devices perform initial data filtering and preprocessing, local servers conduct intermediate analysis, and the cloud platform executes comprehensive analytics. This segmentation allows real-time processing of critical data at the edge with minimal energy consumption, while more intensive computations are distributed to remote servers. The segmented architecture maintains high productivity for time-sensitive operations while reducing overall energy usage by avoiding centralized real-time processing of all data.

The system implements periodic data aggregation and batch processing for non-critical analytics, complemented by event-driven real-time processing for urgent matters. Instead of continuously processing all data at maximum intensity, the system uses periodic updates for routine monitoring and triggers intensive processing only when specific events occur (e.g., anomaly detection, threshold breaches). This periodic action maintains productivity for time-sensitive operations while significantly reducing average energy consumption compared to continuous full-scale processing.

The patent extracts and separates personally identifiable information (PII) from monitoring data through automated anonymization processes. The system removes or generalizes identifying characteristics (names, facial features, device identifiers) while retaining occupancy patterns and environmental metrics needed for precise measurement. Extracted anonymized data is stored separately from raw data, allowing comprehensive monitoring of space usage patterns without capturing individual identities, thereby improving measurement precision while mitigating data privacy risks.

The system introduces data anonymization and aggregation modules as intermediaries between raw sensor data and storage/analysis systems. These intermediaries process occupancy and environmental data to remove identifying information before data is stored or analyzed. The intermediary layers enable precise tracking of occupancy patterns and environmental conditions while preventing direct identification of individuals, thereby resolving the contradiction between measurement precision and data privacy protection.

The patent implements universal communication protocols (MQTT, HTTP, RESTful APIs) and standardized data formats that enable diverse sensor devices to integrate seamlessly with the platform. The system provides multi-functional adaptation layers that automatically detect device types and apply appropriate integration templates, eliminating the need for custom integration code for each device. This universality allows new devices to be integrated quickly through configuration rather than complex programming, improving adaptability while controlling integration complexity through standardization.

The system employs automatic device discovery and self-configuration capabilities that reduce manual integration complexity. When new devices are added, the platform automatically detects them, retrieves their capabilities through standardized interfaces, and configures them appropriately without requiring extensive manual setup. This self-service approach enables seamless integration of new devices while keeping the integration process simple for end users, as the system handles the complex adaptation automatically through automated provisioning and configuration management.