Wireless Zone Presence Detection Using RSSI Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing location determination methods using wireless signals are prone to reliability issues due to signal jitter and lack of knowledge about physical structures, making it difficult to accurately determine if a device is in a particular zone and count the number of devices within a zone.
Innovation Solution
A location/zone tracking server that utilizes RSSI information, combined with knowledge of physical structures like doorway locations, to reliably determine device presence and count within zones by setting specific threshold criteria for entry and exit, and accounting for passageways to reduce false determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RSSI information is used for location determination, then location estimation can be made, but reliability is reduced due to signal jitter
Solution Approach 1:
The system performs preliminary actions by establishing threshold criteria for zone entry and exit before actual location determination occurs. These pre-defined thresholds (e.g., minimum RSSI difference, time window requirements) prepare the framework for reliable zone presence determination, allowing the system to filter out false determinations caused by signal jitter before they affect accuracy.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring zone presence determinations and using this information to refine future determinations. The server tracks device movement patterns, entry/exit events, and signal strength variations over time, using this feedback to adjust threshold criteria and improve the reliability of subsequent location determinations.
2Reliability
If threshold criteria are set for zone entry and exit, then zone presence determination reliability is improved, but device complexity increases
Solution Approach 1:
The system manages complexity by dynamically adjusting threshold parameters based on environmental conditions and device behavior patterns. Rather than using fixed complex rules, the system modifies parameters such as RSSI thresholds, time windows, and signal stability criteria in response to observed conditions, simplifying the overall system logic while maintaining high reliability.
3Measurement precision
If knowledge of physical structures is incorporated, then location determination accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses an intermediary approach by introducing a database or knowledge base of physical structure information (floor plans, doorway locations, zone boundaries) that mediates between raw RSSI measurements and final location determinations. This intermediary layer translates complex physical knowledge into simple lookup tables and reference frameworks, improving accuracy without significantly increasing system complexity.
Data Source
AI summary
Methods and apparatus relating to the detection of one or more devices in zones, e.g., non-overlapping areas, are described. Individual device locations are made based on RSSI information. Whether a user is determined to be in a zone or not is determined based on location determinations corresponding to the device. Thresholds used to determine whether a device is to be considered as being within a zone differs depending on whether the device is newly detected in the zone or is already determined to be in the zone. In some embodiments it is easier to be determined to be in a zone than to be determined to have left a zone. A device may be determined to be in two non-overlapping zones at the same time thereby increasing the chance that devices in edge areas will be counted with regard to the number of devices for which resources should be provided.