IIoT Sensor Data Compression for Bandwidth-Limited Wireless LANs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless IIoT sensors face challenges in coexisting at high densities on networks with limited bandwidth, leading to congestion and increased energy consumption, while also requiring frequent machine health assessments and minimizing battery replacements in hard-to-access locations.
Innovation Solution
A compression method that reduces the volume of data sent over wireless LANs by exploiting similarities in sequential data sets, using bit stream and CARL compression techniques to minimize energy consumption and data packets, thereby improving network reliability and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high density devices are deployed on wireless LAN, then device coverage and monitoring capability are improved, but network congestion and energy consumption increase
Solution Approach 1:
The patent extracts only the essential information from sensor data by identifying and transmitting only data points that exceed thresholds or represent significant changes, rather than transmitting all raw sensor data. This selective extraction reduces network traffic and energy consumption while maintaining monitoring effectiveness.
Solution Approach 2:
Instead of transmitting all data and filtering at the destination, the patent inverts the approach by filtering and compressing data at the source (sensor node) before transmission. This reduces the burden on the network and destination devices, lowering overall energy consumption.
2Measurement precision
If more data is transmitted for frequent health assessments, then assessment accuracy is improved, but network throughput and reliability deteriorate
Solution Approach 1:
The patent applies different data transmission strategies based on local conditions at each sensor node. Data is compressed and filtered according to local thresholds and patterns, with only significant local variations transmitted. This maintains assessment accuracy by capturing locally relevant information while reducing overall network traffic.
Solution Approach 2:
The patent transmits only the necessary portion of data required for accurate health assessment, rather than all raw data. By identifying and transmitting only data points that contribute meaningfully to health assessments (those exceeding thresholds or representing anomalies), the system maintains accuracy while reducing network load.
3Use of energy by moving object
If data compression is applied to reduce transmission volume, then energy consumption is reduced, but data precision and reliability may be compromised
Solution Approach 1:
The patent performs preliminary filtering and threshold-based selection at the data source before compression and transmission. By pre-identifying and marking only the data points that require precise transmission (those exceeding thresholds or representing significant changes), the system ensures data precision is maintained for critical information while compressing overall transmission volume.
Solution Approach 2:
The patent changes the parameters of data representation by transforming raw sensor readings into compressed formats that preserve essential information. Threshold-based filtering and selective transmission alter the data parameters to focus on significant variations, maintaining measurement precision for critical events while reducing overall data volume and energy consumption.
Data Source
AI summary
A compression method for resource constrained local area networks (LANs) of an Industrial Internet of Things (IoT) reduces the volume of raw data sent from “Things” to connection points on the LAN. Applications include industrial processes, and typically include multiple sensor nodes. Sensors on machines wirelessly send data to a base station using a wireless LAN. A computer or server in communication with the wireless LAN computes the health of a machine based on the data received. The method operates by taking advantage of unique similarities between sequential groups of certain types of data that can be sent on the LAN. Mathematical operations are performed on the baseline and subsequent data sets to determine similarities. A difference is taken between the baseline and subsequent data sets, and this difference is compressed and sent to the base station where the original data is reconstructed using the baseline data and uncompressed difference data.