Doorway AP Identification for Footfall Counting
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Solution Overview
Problem
Existing Wi-Fi network deployments face inaccuracies in tracking footfall and visitor classification due to noisy signal information and incorrect AP placements, leading to misclassification of clients as visitors or passersby, especially across floors or adjacent stores, and incorrect dwell time approximations.
Innovation Solution
Implementing a system that uses Received Signal Strength Indicator (RSSI) information and dwell time thresholds to categorize clients as passersby or visitors by tracking their movement paths and associating them with specific RSSI bands, reducing noise in data and improving footfall counting accuracy by identifying doorway Access Points and differentiating between clients inside and outside the store.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static thresholds are used to classify clients as visitors or passersby, then the classification process is simple, but the accuracy is low due to noisy signal information and imprecise footfall tracking
Solution Approach 1:
The patent transitions from static classification thresholds to dynamic classification based on RSSI bands and dwell time patterns. The system dynamically adjusts classification criteria by tracking clients across multiple time periods and comparing their signal strength patterns against learned visitor behavior patterns, enabling accurate differentiation between visitors and passersby without fixed thresholds.
Solution Approach 2:
The system implements feedback by continuously monitoring client behavior patterns and using this information to refine classification accuracy. The controller compares current client behavior against historical data and adjusts classification decisions based on observed patterns, improving measurement precision through iterative learning from actual footfall data.
2Area of stationary object
If APs are placed throughout the network to improve coverage, then network coverage is enhanced, but measurement precision deteriorates due to incorrect AP placements and signal interference from adjacent stores or floors
Solution Approach 1:
The patent applies local quality by analyzing RSSI values specific to each AP's location and characteristics. Instead of using uniform classification criteria across all APs, the system determines RSSI bands individually for each AP based on its placement and surrounding environment, accounting for local variations in signal strength caused by walls, floors, and adjacent stores.
Solution Approach 2:
The system segments the network into distinct functional zones by identifying doorway APs versus interior APs. Doorway APs are specifically identified as those that first detect clients before they enter the store, allowing the system to segment client tracking into pre-entry (doorway) and post-entry (interior) phases, improving accuracy by treating different spatial zones differently.
3Measurement precision
If the system tracks all clients to improve visitor identification, then measurement precision improves, but device complexity increases due to the need to track movement paths and apply multiple classification criteria
Solution Approach 1:
The system performs preliminary action by pre-identifying doorway APs and establishing baseline RSSI bands for visitor classification before actual client tracking begins. The controller pre-processes network topology and AP characteristics to determine which APs are likely to detect clients entering the store, allowing more complex tracking to focus only on relevant APs rather than all APs in the network.
Solution Approach 2:
The patent applies partial action by focusing tracking and analysis only on clients that exhibit visitor-like behavior patterns (entering through doorway APs, maintaining certain RSSI levels), rather than processing all clients uniformly. This selective approach reduces the effective complexity by filtering out irrelevant data about clients who clearly do not enter the store.
4Measurement precision
If the system uses multiple classification criteria including dwell time and RSSI bands, then visitor prediction accuracy is improved, but the difficulty of detecting and measuring increases due to the complexity of analyzing client behavior patterns
Solution Approach 1:
The patent replaces complex mechanical tracking systems with wireless network-based detection using existing Wi-Fi infrastructure. Instead of deploying physical sensors or cameras to track clients, the system uses RSSI data from wireless APs to infer client behavior patterns, simplifying the detection mechanism while maintaining measurement precision through sophisticated data analysis of signal strength variations.
Data Source
AI summary
An example system in accordance with an aspect of the present disclosure includes a controller to store Received Signal Strength Indicator (RSSI) information for an unassociated client detected by an access point (AP) of a wireless network. The controller is to identify that the unassociated client has associated with the wireless network, and identify the AP as a doorway AP in response to the client associating to the AP.