Intermediate Server Packet Aggregation for Real-Time IoT Compression
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Solution Overview
Problem
Existing data compression systems face challenges in reducing communication traffic and the number of packets while maintaining real-time performance, particularly in IoT applications where hundreds of millions of devices generate small, frequently transmitted data, leading to increased network load and connection management issues.
Innovation Solution
A data compression transmission system is introduced, featuring an intermediate server that performs hashing, buffering, and compression encoding to aggregate and compress data from multiple devices before transmitting it to a central server, thereby reducing communication traffic and packet count without delaying real-time data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If packet caching is used to compress short packets, then communication traffic is reduced, but real-time performance deteriorates
Solution Approach 1:
The system segments the compression function into two parts: device-side packet caching for immediate compression, and server-side aggregation for batch processing. This segmentation allows devices to maintain real-time performance while the server handles bulk compression, resolving the contradiction between traffic reduction and real-time performance.
Solution Approach 2:
The server acts as an intermediary between devices and the central system. It receives compressed packets from devices, performs aggregation and decompression, then transmits to the central server. This intermediary approach allows devices to use simple packet caching while the server handles complex aggregation, maintaining real-time performance at the device level.
2Adaptability or versatility
If data is collected from a large number of sensors, then comprehensive monitoring is achieved, but network load increases
Solution Approach 1:
The system merges data from multiple devices at the server before transmission to the central server. By aggregating packets from multiple sources into consolidated data structures, the system maintains comprehensive monitoring coverage while significantly reducing the total network load through combined transmission.
Solution Approach 2:
The server creates compressed representations (copies) of the aggregated data before transmitting to the central server. This copying approach allows the system to maintain complete monitoring data while transmitting only essential information, reducing network load while preserving monitoring versatility.
3Quantity of substance
If packet caching is implemented, then temporal redundancy is eliminated, but system complexity increases
Solution Approach 1:
Instead of implementing complex compression algorithms at the device level, the system inverts the approach by using simple packet caching at devices and moving complex aggregation and decompression operations to the server. This inversion reduces device complexity while achieving the same packet size reduction goal.
Solution Approach 2:
Devices perform self-service compression using simple packet caching with hash-based identification, eliminating the need for complex compression algorithms at the device level. The server then performs aggregation and decompression, allowing devices to maintain simplicity while achieving effective compression.
Data Source
AI summary
A technique for compressing and transmitting data without hampering real-time performance can be offered. In a data compression transmission system for collecting data generated by a plurality of devices at a central server via a network, an intermediate server is arranged between the devices and the central server. Each of the devices includes a packet cache processing unit for converting the generated data to a hash value based on a cache. The intermediate server includes a packet cache processing unit for decoding the hash value to original data based on the cache, a buffering unit for aggregating the data and outputting the data as a long packet, and a compression encoding unit for compressing the data and generating encoded data.