Mesh Network Location Estimation via RSSI Probability Maps
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Solution Overview
Problem
Existing location estimation methods for emitting devices in mesh networks are flawed due to variations in signal strength caused by factors like orientation and environmental obstructions, leading to inaccurate distance calculations and location determination.
Innovation Solution
A system and process that utilizes a mesh network with a location estimation and prediction controller to generate a probability map by processing relative signal strength indicators (RSSIs) from multiple listener nodes, combining them with a statistical model and historical tracking data to determine the most likely location of a transmitting device, while accounting for obstructions and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized triangulation methods utilizing relative signal strength indicator values are used to determine location, then location estimation can be performed, but measurement precision deteriorates due to signal strength variations caused by orientation and environmental obstructions
Solution Approach 1:
The patent combines multiple listener nodes to collectively estimate the location of an emitting device. Instead of relying on a single listener's signal strength measurements, the system aggregates data from multiple listeners to triangulate the device's position, thereby compensating for individual signal variations caused by orientation and obstructions.
Solution Approach 2:
The system pre-establishes a mesh network of listener nodes throughout the environment before location estimation is needed. These listeners continuously monitor and report signal strength values, building a historical data foundation that enables more accurate location prediction even when individual measurements are affected by environmental factors.
2Measurement precision
If multiple listener nodes are utilized to improve location estimation accuracy, then measurement precision improves, but device complexity increases due to the need for mesh network coordination and data processing
Solution Approach 1:
Each listener node in the mesh network serves multiple functions: it acts as a signal receiver, a data transmitter, and a potential relay node for other listeners. This multi-functionality reduces the need for separate dedicated components for each role, simplifying the overall system architecture despite the increased number of nodes.
Solution Approach 2:
The mesh network operates with a degree of autonomy where listener nodes automatically report their signal strength measurements and the system autonomously processes this data to estimate device location. The distributed nature of the network allows it to self-organize and maintain functionality without requiring complex centralized control mechanisms.
Data Source
AI summary
A system for estimating the location of a node in a mesh network and predicting future location of the same. Historical RSSI of nodes in a mesh network may be used to determine relative location amongst other nodes. The system may employ statistical approaches to predict future node locations.