Dynamic Bloom Filter for Wireless Service Discovery
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Solution Overview
Problem
Current service discovery techniques in wireless communication systems require IP connection setup before service detection, which can be inefficient and cumbersome, especially in wireless communication contexts where devices need to associate with a network to determine available services.
Innovation Solution
Implementing a dynamically configurable Bloom filter to compactly advertise available services, providing a 'service hint map' to other devices, allowing them to determine service availability before association, with adjustable false positive rates and hash functions to optimize efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional service discovery techniques are used, then service detection can be performed, but IP connection setup is required before service discovery which reduces efficiency and increases complexity
Solution Approach 1:
The Bloom filter data structure is generated and configured in advance with predetermined parameters (number of services, false positive rate, hash functions) before service discovery operations. This preliminary configuration enables devices to perform service discovery without establishing IP connections first, as the Bloom filter provides pre-computed service availability information that can be queried directly
Solution Approach 2:
The Bloom filter acts as an intermediary data structure between service providers and service seekers. Instead of requiring direct IP connections for service discovery, the Bloom filter serves as a mediator that compactly represents service availability information, allowing devices to query service presence without establishing full network connections
2Measurement precision
If Bloom filter parameters are increased to reduce false positive rate, then service indication accuracy improves, but Bloom filter size and processing complexity increase
Solution Approach 1:
The patent establishes specific parameter relationships and formulas to determine optimal Bloom filter configuration. The number of hash functions (k) is calculated based on the number of services (n) and desired false positive rate (p) using the formula k = -ln(p) * n / ln(2). This parameterization allows accuracy to be adjusted independently of overall complexity by simply changing the false positive rate threshold
3Quantity of substance
If Bloom filter size is increased to accommodate more services, then service advertisement capacity improves, but memory usage and transmission overhead increase
Solution Approach 1:
Instead of linearly increasing Bloom filter size with the number of services, the patent uses dimensional optimization by adjusting the false positive rate parameter and hash function count. The Bloom filter size (m) is calculated as m = -n * ln(p) / ln(2), allowing the system to accommodate more services (n) while controlling memory usage through the false positive rate (p) parameter rather than simply expanding filter size proportionally
Data Source
AI summary
This disclosure relates to service discovery using a dynamically configurable Bloom filter. According to some embodiments, various parameters of the Bloom filter may be determined by a first wireless device. The parameters may include a number of services advertised using the Bloom filter, a false positive rate of the Bloom filter, a set of hash functions used with the Bloom filter, and/or a size of the Bloom filter. The Bloom filter may be generated according to the determined parameters. The Bloom filter, along with some or all of the parameters of the Bloom filter, may be transmitted by the first wireless device. A second wireless device may use the Bloom filter as part of a service discovery process to determine whether or not a desired service is available via the first wireless device.