Crowdsourced Wireless Fingerprint Location Confirmation
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Solution Overview
Problem
Current location confirmation methods, such as GPS, face limitations in urban areas and indoors, leading to inaccuracies and difficulties in identifying the correct delivery location, resulting in customer and driver frustration, as well as increased costs due to delivery defects.
Innovation Solution
The use of crowdsourced wireless fingerprints to generate location confirmation models, leveraging existing wireless infrastructure like Wi-Fi, Bluetooth, and NFC, which involves collecting and analyzing signal strength and device attributes to create accurate location confirmation models that adapt to changes in wireless infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS devices are used for delivery location identification, then delivery routing is enabled, but location accuracy deteriorates in urban areas and indoors
Solution Approach 1:
The patent introduces wireless access points as intermediary devices that create location fingerprints. These fingerprints serve as mediators between the delivery system and the physical location, enabling accurate indoor and urban area location identification where GPS fails. The system compares real-time wireless fingerprints with stored reference fingerprints to confirm delivery locations.
Solution Approach 2:
The patent replaces the GPS satellite-based positioning system with a wireless infrastructure-based fingerprinting system. This substitution enables location confirmation in environments where GPS signals are blocked or inaccurate, such as indoors and dense urban areas, by using local wireless signal characteristics instead of satellite signals.
2Adaptability or versatility
If wireless infrastructure changes occur, then environmental adaptability improves, but location confirmation accuracy deteriorates due to outdated fingerprints
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors wireless infrastructure changes and automatically updates location fingerprints. When changes in the wireless environment are detected, the system receives feedback and regenerates fingerprints to maintain location confirmation accuracy despite infrastructure modifications.
Solution Approach 2:
The patent makes the location confirmation system dynamic by enabling automatic updates of wireless fingerprints when infrastructure changes are detected. Instead of using static fingerprints, the system adapts to changing wireless environments by regenerating fingerprints based on current signal characteristics, ensuring continued accuracy.
3Measurement precision
If crowdsourced wireless fingerprints are collected, then location confirmation accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling mobile devices to automatically collect, store, and transmit wireless fingerprint data without requiring manual intervention. The system autonomously crowdsources location data from multiple devices, processes the information, and generates updated fingerprints, reducing operational complexity despite the sophisticated data collection process.
4Reliability
If delivery confirmation accuracy is improved, then delivery defects are reduced, but operational time increases due to additional verification steps
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing wireless fingerprints from multiple sources before delivery attempts. This advance preparation creates a reference database that enables rapid comparison and confirmation during actual deliveries, improving accuracy without adding significant time to the delivery process itself.
Data Source
AI summary
Disclosed are various embodiments for generating location confirmation models using crowdsourced wireless fingerprints. Wireless fingerprints can be generated and associated with a given location during events that use proximity to specific locations. The wireless fingerprints can be processed to generate location confirmation models that can be used for location confirmation. Periodically, the collected wireless fingerprints can be analyzed and compared to previously collected wireless fingerprints to detect a change in a wireless infrastructure at the given location. Upon determining that a previously generated location confirmation model is invalid according to a level of significance of the change, the outdated wireless fingerprints can be identified and removed from storage or otherwise ignored for future models. An updated location confirmation model can be generated using the up-to-date wireless fingerprints.