Modular IoT Sensor Array for Enterprise Deployment
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Solution Overview
Problem
Existing IoT systems face challenges in efficiently deploying and managing IoT devices in enterprise settings, particularly in open floor plans and meeting spaces, due to complexities in sensor installation, cost, and lack of modular and wireless solutions that enhance productivity and facilities knowledge.
Innovation Solution
A modular IoT platform with wireless sensor arrays and IoT gateways that allow easy installation, swapping, and management, utilizing cloud services for data processing and integration with various sensors, enabling efficient data collection and analysis for improved productivity and space utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional sensor installation methods are used in enterprise settings, then sensor functionality is achieved, but deployment complexity and installation time increase significantly
Solution Approach 1:
The sensor system is divided into modular components including sensor arrays, wireless communication modules, and power management units that can be independently installed and configured. This segmentation allows for easier deployment in enterprise settings by enabling step-by-step installation rather than requiring complete system installation at once.
Solution Approach 2:
Traditional wired sensor installations are replaced with wireless sensor arrays that communicate via wireless protocols. This substitution eliminates the need for complex wiring infrastructure, reducing both installation time and structural complexity while maintaining sensor functionality.
2Loss of information
If comprehensive sensor arrays are deployed to monitor all spaces, then data coverage is improved, but system cost and complexity increase
Solution Approach 1:
The sensor arrays are designed with multi-functional capabilities, where a single sensor node can perform multiple sensing functions (temperature, humidity, motion, occupancy) simultaneously. This universality reduces the total number of devices needed while maintaining comprehensive data coverage across all enterprise spaces.
Solution Approach 2:
Multiple sensing functions and data processing capabilities are merged into integrated sensor array modules. By combining what would traditionally require separate devices into unified modules, the system achieves comprehensive monitoring coverage while reducing overall system complexity and deployment burden.
3Productivity
If real-time data processing is implemented across all sensors, then productivity insights are improved, but computational resources and energy consumption increase
Solution Approach 1:
Rather than processing all sensor data in real-time across the entire enterprise, the system implements selective real-time processing only for critical metrics and high-traffic areas. This partial action approach provides actionable productivity insights while significantly reducing computational overhead and energy consumption compared to comprehensive real-time processing.
Solution Approach 2:
A hierarchical data processing architecture is introduced with intermediate processing layers between sensor arrays and central analysis systems. Local edge computing nodes perform preliminary data filtering and aggregation, acting as intermediaries that reduce the volume of data requiring full real-time processing while maintaining insight quality.
Data Source
AI summary
The description relates to managing physical locations with IoT devices. One example can include a sensor array assembly comprising an enclosure and a mounting element. The mounting element can include multiple apertures spaced at different distances from the enclosure. The sensor array assembly can also include a connector that is receivable by individual apertures.