Extensible Body Sensor Network Platform for Edge Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Body Sensor Networks (BSNs) face limitations in processing real-time sensor data due to limited capacity and resource constraints, necessitating improved systems and methods for data processing and transmission.
Innovation Solution
The proposed solution involves generating lifted datasets using a lifting scheme, applying sensor-specific thresholds, performing run-length encoding, and encrypting packets for efficient data compression and transmission, utilizing lightweight cryptography like SPARX for microcontroller-based systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor data processing is performed in BSN, then monitoring capability is improved, but energy consumption increases and resource capacity is exceeded
Solution Approach 1:
The patent applies preliminary action by performing data compression and processing operations before data transmission. The lifting wavelet transform and run-length encoding are executed at the sensor node level prior to transmission, reducing the volume of data that needs to be transmitted and processed remotely, thereby decreasing overall energy consumption while maintaining real-time monitoring capability
Solution Approach 2:
The patent segments the data processing function across multiple levels: local preprocessing at sensor nodes using lifting wavelet transform, followed by compression using run-length encoding, then selective transmission of compressed data. This segmentation distributes computational load and reduces the energy burden on individual components while maintaining system-wide real-time monitoring capability
2Use of energy by moving object
If data compression is applied to reduce transmission size, then energy consumption decreases, but data processing complexity increases
Solution Approach 1:
The patent uses lifting wavelet transform to create a transformed version of the original sensor data that preserves essential information while reducing data size. This mathematical transformation creates a compressed representation (copy) of the data that can be transmitted efficiently while maintaining the ability to reconstruct the original data when needed
Solution Approach 2:
The patent changes the parameters of data representation by applying lifting wavelet transform to convert sensor data into a different domain (wavelet coefficients), then applies run-length encoding to further compress the data by exploiting spatial or temporal redundancy. These parameter changes enable efficient compression while managing processing complexity through algorithmic optimization
3Reliability
If multiple sensor nodes are deployed to improve monitoring coverage, then monitoring capability increases, but system resource requirements exceed available capacity
Solution Approach 1:
The patent implements self-service by enabling each sensor node to autonomously perform data compression and preprocessing operations using the lifting wavelet transform and run-length encoding algorithms. Each node independently manages its own data processing needs, reducing the burden on central system resources and enabling scalable deployment of multiple nodes without proportionally increasing system resource requirements
Data Source
AI summary
Methods and systems for body monitoring are disclosed. The methods and systems include: obtaining one or more sensor data arrays from one or more sensors. A first sensor data array of the one or more sensor data arrays includes multiple samples, the first sensor data array corresponding to a first sensor of the one or more sensors. generating a lifted dataset for the plurality of samples in the first sensor data array based on a lifting scheme; determine a sensor-specific threshold based on the first sensor; filtering the lifted dataset based on the sensor-specific threshold to produce a filtered dataset; generating a plurality of run-length encoding arrays based on the filtered dataset; concatenating the plurality of run-length encoding arrays to produce a packet; encrypt the packet; and transmit the encrypted packet. Other aspects, embodiments, and features are also claimed and described.