IoT Tag Localization Using Environmental Sensing Clusters
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Solution Overview
Problem
IoT tags lack means to provide their current location due to the absence of GPS or sophisticated localization hardware, which increases cost and power consumption, and integrating such hardware is challenging due to size and form factor constraints.
Innovation Solution
A method and system that utilize a cloud-based processing system to compare sensing data from IoT tags with known clusters to determine their location using statistical models and machine learning algorithms, associating unknown tags with known clusters based on environmental sensing data like temperature, humidity, and ambient light.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or sophisticated localization hardware is integrated into IoT tags, then localization accuracy is improved, but cost and power consumption increase
Solution Approach 1:
The patent introduces gateway devices and cloud-based processing systems as intermediaries between IoT tags and the localization function. Instead of embedding GPS in tags, the system uses gateways to receive signals from tags and perform localization computations in the cloud, thereby eliminating the need for power-intensive localization hardware in the tags themselves.
Solution Approach 2:
The patent replaces physical localization hardware (GPS receivers, accelerometers, gyroscopes) with a software-based solution using statistical models and machine learning algorithms. The localization function is transitioned from a hardware-intensive mechanical system to a computation-based system that processes sensing data through statistical comparisons.
2Measurement precision
If GPS or sophisticated localization hardware is integrated into IoT tags, then localization accuracy is improved, but device size and form factor increase
Solution Approach 1:
The patent extracts the localization functionality from the IoT tag itself and places it in external infrastructure (gateways and cloud systems). By taking out the sophisticated localization hardware from the tag, the tag maintains its small form factor while still achieving accurate localization through the external system.
Solution Approach 2:
The gateway devices and cloud-based system serve multiple functions: they act as communication intermediaries, perform localization computations for multiple tags simultaneously, and provide a centralized platform for statistical model comparisons. This multi-functional approach eliminates the need for dedicated localization hardware in each tag.
3Measurement precision
If sensing data is collected and transmitted to determine location, then localization capability is achieved, but energy consumption increases
Solution Approach 1:
The patent implements periodic sensing data collection rather than continuous transmission. IoT tags collect sensing data periodically and transmit it to gateways at scheduled intervals, reducing the energy consumption associated with constant data transmission while still maintaining effective localization capability through the accumulated periodic measurements.
Data Source
AI summary
A system and method of detecting the location of objects attached to IoT tags are provided. The method includes receiving data packets from a gateway, wherein the received data packets include sensing data derived from signals transmitted by an IoT tag having an unknown location; comparing the sensing data of the unknown IoT tag to sensing data of a cluster of IoT tags having a known established location, wherein the comparison applies at least one statistical model; associating the unknown IoT tag with the cluster of IoT tags having the known established location based on the comparison; and determining a location of the unknown IoT tag based on the associated cluster.