Floor Determination Using RSSI Probability Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless asset tracking systems face challenges in accurately determining the location of wireless devices across multiple floors, particularly due to unreliable RSSI measurements and varying environmental conditions, which affect the precision of floor determination.
Innovation Solution
An apparatus comprising a communication interface to acquire RSSI data from multiple access points and floor determination logic that calculates the probability of a wireless device's location by adjusting RSSI values and employing a Gaussian probability distribution to select the most likely floor based on combined probabilities from each access point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If RSSI measurements from multiple access points are used to determine floor location, then location coverage is improved, but measurement reliability deteriorates due to signal interference and environmental factors
Solution Approach 1:
The system segments the location determination problem by floor level. It calculates separate probability scores for each floor based on RSSI measurements from access points on that floor, then selects the floor with the highest probability. This segmentation allows the system to handle multi-floor environments effectively while maintaining reliability by evaluating each floor independently rather than treating all measurements as a single pooled dataset.
Solution Approach 2:
The system transforms raw RSSI measurements into probability values using a mathematical transformation. By converting signal strength readings into probabilistic floor likelihoods, the system changes the parameter representation from absolute signal strength to relative probability, which better handles the uncertainty and interference inherent in wireless measurements across multiple floors.
2Measurement precision
If probability-based floor determination is implemented, then location accuracy is improved, but computational complexity increases
Solution Approach 1:
The computational process is segmented into discrete steps: collecting RSSI values from access points, calculating probability scores for each floor separately, summing probabilities per floor, and selecting the maximum. This segmentation of the computational process makes the complexity manageable and systematic, allowing the system to achieve high accuracy through structured probability calculations rather than complex algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of wireless device location determination across multiple floors by mitigating the impact of unreliable RSSI measurements and environmental factors, ensuring reliable floor identification even in dynamic environments.
Implementation Method 1
a communication interface configured to acquire received signal strength indicator (RSSI) data for a wireless device from a plurality of access points on a plurality of floors
Implementation Method 2
floor determination logic configured to determine a floor that the wireless device is located by analyzing the RSSI data acquired by the communication interface
Data Source
Figure 1~3
Figure 2
Figure 4~5
AI summary
An apparatus configured to acquire received signal strength intensities (RSSIs) for a wireless device from a plurality of access points (APs) located on a plurality floors. The apparatus is configured to determine which floor the wireless device is on by analyzing the RSSIs. In an example embodiment, the RSSIs are adjusted, and the adjusted RSSIs for each floor are summed. The floor with highest sum of adjusted RSSIs is determined to be the floor the wireless device is on. In an example embodiment, the floor that the wireless device is on is determined by calculating the probability that the wireless device is within the cell of each AP on the network, and combining the probabilities for each floor. Known RSSIs between APs can be employed for comparing measured RSSIs with the known RSSIs to determine the probability that the wireless device is within the cell of each AP.