Wireless Proximity Detection via Signal Strength Comparison
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Solution Overview
Problem
There is a need for a method and system that allows users of computing devices to reliably and easily discover nearby people and resources in unfamiliar settings, such as at conferences or social events, using wireless communications, as existing technologies do not effectively facilitate this functionality.
Innovation Solution
A system and method that utilizes wireless signal strengths to determine proximity between devices, with a proximity server collecting and comparing signal strength data from participating devices to identify nearby clients, and optionally providing additional information about their owners or resources, such as biographical data or device capabilities, through a client-server or peer-to-peer model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If wireless signal strength comparison is used to detect proximity, then the ability to discover nearby people and resources is improved, but the system complexity and infrastructure requirements increase
Solution Approach 1:
The patent introduces base stations as intermediary elements that broadcast identification signals throughout the environment. These base stations serve as mediators between mobile devices and the proximity detection system, enabling indirect measurement of proximity through signal strength comparison without requiring direct device-to-device communication infrastructure
Solution Approach 2:
The patent replaces traditional mechanical or visual proximity detection methods with wireless electromagnetic signal-based detection. By using wireless signal strength (RSSI) measurements from base stations, the system substitutes physical infrastructure requirements with electronic field-based detection, reducing the need for complex mechanical positioning systems
2Measurement precision
If signal strength reports are collected and processed by a proximity server, then proximity detection accuracy is improved, but the data processing load and network traffic increase
Solution Approach 1:
The patent implements periodic signal strength reporting where mobile devices transmit their measurements to the proximity server at regular intervals rather than continuously. This periodic action reduces network traffic and energy consumption while still maintaining adequate proximity detection accuracy by capturing proximity changes at sufficient frequency
Solution Approach 2:
The system dynamically adjusts reporting parameters such as the interval between reports and the threshold for triggering proximity notifications. By changing these parameters based on environmental conditions and user needs, the system optimizes the balance between detection accuracy and energy consumption, reducing unnecessary network traffic when proximity conditions are stable
3Loss of information
If the system provides detailed information about nearby devices and resources, then user awareness and interaction opportunities are improved, but the information processing and display complexity increase
Solution Approach 1:
The patent segments information about nearby devices into hierarchical categories such as device type, proximity distance, and relevance level. This segmentation allows the system to present information in organized, manageable portions rather than overwhelming users with all available data simultaneously, reducing cognitive load while maintaining comprehensive information availability
Solution Approach 2:
The system applies local quality by providing different levels of information detail based on proximity distance and device type. Nearby devices receive more detailed information presentation, while farther devices receive summarized information. This selective information presentation reduces overall processing complexity by focusing computational resources on the most relevant nearby entities
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
Enables users to efficiently detect and interact with nearby individuals and resources, improving social interactions and resource discovery, while being flexible, extensible, and user-friendly, even for non-technical users.
Implementation Method 1
gather wireless signal strengths (with respect to various base stations, or access points or the like) from participating resources
Data Source
AI summary
A system and method in a wireless network for discovering which resources (e.g., other wireless computing devices) are proximate a user's wireless computing device. Wireless signal strengths with respect to various base stations are compared with the signal strengths of other network devices or resources, to determine which devices are experiencing similar signal strengths. Devices with similar signal strengths are deemed proximate. Each participating computing device may send its signal strength reports to a proximity server, which distributes proximity data to network clients. Each client may receive and process the signal strength data for determining which other clients/resources are proximate, or the server can perform proximity computations and return a list of proximate clients. Once computed, the identities of the proximate clients can be used to query for additional data about the clients, such as the names and other details of their owners, or information about the resource.