Bloom Filter Device Update Transmission
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Solution Overview
Problem
Conventional systems face significant delays and resource inefficiencies when processing device update requests for large numbers of computerized devices, such as V2X vehicles, due to the need for linear or binary searches, which require substantial processing time and power, leading to latency in determining whether a device belongs to a campaign for updates.
Innovation Solution
The implementation of a bloom filter data structure that uses hash values to quickly determine if a device belongs to a campaign, reducing processing time and resource usage by allowing device updates to be provided in approximately constant time, even with a large number of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear or binary search methods are used to determine device campaign membership, then accurate device identification is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The system pre-computes and stores hash values of device identifiers in a bloom filter data structure before update campaigns begin. This preliminary action allows O(1) average-time membership queries during actual update operations, eliminating the need for linear or binary searches at runtime and thus resolving the contradiction between identification accuracy and processing time.
Solution Approach 2:
Instead of storing and searching actual device identifiers directly, the system creates hash copies of these identifiers in the bloom filter. This copying approach maintains the ability to accurately identify devices while dramatically reducing the time required for membership determination, as hash comparisons are far faster than linear or binary searches through large device lists.
2Reliability
If conventional search methods are used for large device campaigns, then complete device coverage is maintained, but computational power and processing resources are excessively consumed
Solution Approach 1:
The bloom filter is pre-populated with hash values of all devices in the campaign before updates begin. This preliminary setup enables rapid O(1) membership checks during update distribution, avoiding the high computational energy costs of linear or binary searches while maintaining reliable device identification for update delivery.
Solution Approach 2:
The system transforms the device identifier from its original form into a hash value representation in the bloom filter. This parameter transformation allows for extremely efficient membership queries with constant time complexity, dramatically reducing computational energy consumption while preserving the reliability needed to identify all devices that should receive updates.
3Quantity of substance
If the number of devices in a campaign increases, then broader update coverage is achieved, but processing time and resource requirements grow proportionally
Solution Approach 1:
The bloom filter data structure is pre-filled with hash values of all campaign devices before the update campaign begins. This preliminary action creates a constant-time lookup mechanism that maintains high processing efficiency regardless of how large the device campaign grows, preventing productivity degradation as device quantities increase.
Solution Approach 2:
The system transitions from one-dimensional linear or binary search through device lists to a different dimensional approach using hash-based bloom filter membership testing. This dimensional change enables processing efficiency to remain constant even as the quantity of devices increases, breaking the direct proportionality between device count and processing time.
Data Source
AI summary
Systems, devices, and methods for updating computerized devices. Functions and operations can include: obtaining a filter data structure (e.g., a bloom filter data structure) that may include hash values corresponding to each of the computerized devices to be updated; determining whether a computerized device is to obtain a device update based on a hash value associated with the computerized device matching a hash value of the filter data structure; and providing the device update to the computerized device when there is a match. The provided device update may modify the operation of the computerized device that receives it.