Filter Data Structure for Device Update Campaign Verification
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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 extensive data storage and processing time, especially when determining whether a device belongs to a specific update campaign.
Innovation Solution
The implementation of a campaign management service that generates a filter data structure using hash functions to quickly verify if a device belongs to an update campaign, utilizing data structures like cuckoo filters or Xor filters, which reduces processing time and resource usage by allowing devices to determine their eligibility in constant time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems process device update requests for large campaigns, then all device requests can be handled, but significant delays and resource consumption occur
Solution Approach 1:
The system segments the large campaign into smaller batches using filter data structures. Each filter structure contains a subset of device identifiers, allowing the system to process requests in manageable chunks rather than handling all devices simultaneously, thus reducing latency while maintaining throughput.
Solution Approach 2:
Filter data structures are generated in advance before processing device update requests. These pre-computed filters enable rapid membership testing by checking if a device identifier exists in the filter structure, eliminating the need for time-consuming database queries during actual update processing.
2Reliability
If conventional systems store extensive device data for campaign verification, then accurate membership determination is achieved, but hardware resources increase significantly
Solution Approach 1:
The system creates compact filter data structures that are simplified copies of the full device database. These filters contain only the essential information needed for membership testing (device identifiers mapped to filter positions), dramatically reducing memory requirements while preserving verification accuracy.
Solution Approach 2:
The system transforms device identifiers into hash values that map to positions in the filter structure. This parameter transformation compresses the large set of device IDs into a compact representation that maintains the ability to accurately determine campaign membership through simple position checks.
Data Source
AI summary
A system includes a campaign management service to detect a campaign initiation request indicating a number of computerized devices to be updated for a campaign and store data corresponding to the computerized devices to be updated. The campaign management service can generate a filter data structure comprising hash values based on the data for each of the computerized devices to be updated and transmit the filter data structure to a network edge. The system can include the network edge that can use the filter data structure from the campaign management service to determine whether a computerized device is to obtain a device update from the campaign management service. The network edge can retrieve the device update and modify the computerized device by transmitting the device update to the computerized device, which then installs it.