Cloud-Bot Occupancy Detection Using Multi-IoT Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional home automation systems provide limited support for end users due to the lack of integration and analysis of disparate IoT device data from different manufacturers, leading to inefficiencies in controlling and managing smart home environments.
Innovation Solution
A cloud-based system aggregates and normalizes sensor data from multiple IoT devices, using cloud-bots to analyze the data and initiate responsive actions based on machine learning models, enabling comprehensive control and management of smart home environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple disparate IoT devices is aggregated and analyzed using cloud-bots with machine learning models, then occupancy detection accuracy and automated control capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces cloud-bots as intermediary components that mediate between disparate IoT devices and the control system. These cloud-bots aggregate sensor data from multiple devices, normalize the data formats, and apply machine learning models to detect occupancy states. This intermediary layer resolves the contradiction by handling the complexity of data integration centrally in the cloud rather than requiring complex local processing at each device, thereby improving detection accuracy while managing system complexity through centralized architecture.
2Productivity
If comprehensive sensor data from multiple IoT devices is collected and analyzed, then automated energy management and safety control are enhanced, but data transmission and processing energy consumption increase
Solution Approach 1:
The patent replaces local mechanical processing and decision-making at IoT devices with cloud-based computational processing. Instead of each device independently processing data and consuming energy for local analysis, the system transmits raw sensor data to cloud-bots that perform machine learning inference. This substitution shifts the computational burden to the cloud, reducing energy consumption at edge devices while maintaining high productivity in automated energy management through sophisticated cloud-based analysis.
3Ease of operation
If real-time sensor data from multiple sub-regions is processed to detect occupancy states, then responsive control of IoT devices is improved, but data aggregation and analysis time increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models offline and deploying them to cloud-bots before real-time operation. The cloud-bots are pre-configured with occupancy detection algorithms that can quickly process incoming sensor data without requiring complex real-time training. This preliminary preparation reduces the time required for data aggregation and analysis during actual operation, enabling responsive control while minimizing time loss in processing.
Data Source
AI summary
Gathering first data and second data over a communication network, the first data being detected by a first sensor device deployed in a first sub-region of a region, the second data being detected by a second sensor device deployed in a second sub-region of the region. Executing a state detection cloud-bot. Generating a likelihood the region is in a particular state. If the likelihood satisfies a first threshold condition associated with the particular state, initiating one or more first response actions for controlling one or more device actions of at least one Internet-of-Things (IoT) device of a first set of IoT devices. If the likelihood satisfies a second threshold condition associated with the particular state, initiating one or more second response actions for controlling one or more device actions of at least one IoT device of a second set of IoT devices.