Adaptive Data Compression for Low-Latency M2M Bandwidth Savings
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Solution Overview
Problem
Existing data compression schemes for M2M communications face challenges in conserving bandwidth without introducing latency or requiring expensive hardware, as they often involve trade-offs between compression degree, distortion, and computational resources.
Innovation Solution
An adaptive compression scheme that selects compression methods based on static and dynamic attributes of remote devices, such as processing capability and battery power, to optimize data transmission while considering inter-packet and intra-packet correlations, allowing for dynamic adjustment of compression algorithms to suit the capabilities of remote devices and communication channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to conserve bandwidth, then bandwidth usage is reduced, but latency increases and expensive hardware is required for fast decompression
Solution Approach 1:
The system dynamically selects compression schemes based on real-time device attributes and communication conditions. The server receives attributes from remote devices and adapts the compression scheme accordingly, allowing the system to optimize between compression ratio and decompression speed based on current capabilities and requirements
Solution Approach 2:
The patent changes multiple parameters including compression ratio, algorithm complexity, and processing depth based on device attributes such as processing capability, memory availability, and battery status. This allows the system to adjust the balance between bandwidth savings and processing overhead dynamically
2Quantity of substance
If high-degree compression is applied, then bandwidth usage is reduced, but computational resources required for compression and decompression increase
Solution Approach 1:
The system applies compression selectively based on data characteristics and device capabilities. Rather than always applying maximum compression, it uses partial compression or no compression when the data characteristics or device state make it unnecessary, thereby reducing computational overhead while still achieving bandwidth savings when beneficial
Solution Approach 2:
The server receives feedback from remote devices about their current state (battery level, processing capability, memory availability) and uses this information to adapt the compression scheme. This feedback loop allows the system to optimize computational resource usage by matching compression intensity to actual device capabilities
3Adaptability or versatility
If compression schemes are selected based on device attributes, then adaptability is improved, but device complexity increases
Solution Approach 1:
The server acts as an intermediary that manages the complexity of compression scheme selection. Rather than requiring each remote device to have complex compression management logic, the server receives device attributes, determines the appropriate compression scheme, and communicates it back to the device, thereby centralizing the complexity management
Data Source
AI summary
Methods and systems are disclosed for adaptive compression is disclosed. The compression is adaptive based on one or more criteria related to the system, including but not limited to: the centralized authority, one or more remote devices, the communication channel, and/or the data communicated. For example, the compression may be adaptive based on one or more criteria related to the remote device, including dynamic and/or static criteria of the remote device. In this way, the adaptive compression may be reduce the amount of data transmitted from the remote device to the centralized authority, thereby conserving bandwidth.