Wireless Device Network Size Estimation Using Bloom Filter
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Solution Overview
Problem
Current methods for estimating the number of neighboring devices in wireless networks are inefficient, leading to high memory requirements and complex sorting processes, and there is a need for a more efficient and less complex approach.
Innovation Solution
A wireless device uses a Bloom filter to estimate the number of neighboring devices by adding received identifiers and calculating the number of distinct strings based on the number of zeros in the filter, reducing the need for storing individual identifiers and simplifying the estimation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a wireless device stores all received identifiers to track the number of devices in the network, then the device can accurately track the number of neighboring devices, but the memory requirements and sorting complexity increase significantly
Solution Approach 1:
The patent extracts only the essential information (number of distinct devices) from the complete set of identifiers, rather than storing all identifiers. This is achieved by using a Bloom filter that processes identifiers and produces a compact representation that enables estimation of the number of distinct elements without retaining the original data
Solution Approach 2:
The patent changes the parameter representation from storing complete identifiers to storing a compact probabilistic data structure (Bloom filter) that estimates the number of distinct identifiers. This parameter transformation allows the system to work with a fixed-size data structure regardless of the number of devices, thereby reducing memory requirements while maintaining acceptable accuracy
2Measurement precision
If a wireless device stores all received identifiers to track the number of devices in the network, then the device can accurately track the number of neighboring devices, but the complexity of sorting and processing identifiers increases
Solution Approach 1:
The patent extracts only the essential information (number of distinct devices) from the complete set of identifiers, rather than storing all identifiers. This is achieved by using a Bloom filter that processes identifiers and produces a compact representation that enables estimation of the number of distinct elements without retaining the original data
Solution Approach 2:
The patent employs a Bloom filter, which is a disposable probabilistic data structure that can be efficiently updated and discarded. Instead of maintaining complex sorted lists of identifiers, the system uses a simple bit array with hash functions that can be quickly updated and provides sufficient accuracy for network size estimation
Data Source
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AI summary
Systems and methods for monitoring the number of neighboring wireless devices in a wireless network are described herein. In one aspect, the method includes receiving a message from one of the neighboring wireless devices having an identifier associated with the neighboring wireless device and adding the identifier into a Bloom filter. The method may further include estimating the number of distinct strings that have been added into the Bloom filter based on the number of zeros in the Bloom filter, the number of distinct strings representing an estimate of the number of neighboring wireless devices in the wireless network.