Sensor Network Bandwidth Optimization via Sliding Window Statistics
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Solution Overview
Problem
Sensor networks face challenges in managing bandwidth usage and conserving computing resources, particularly in scenarios where multiple processing functions require different data processing rates, leading to inefficient data processing and increased resource consumption.
Innovation Solution
Implementing a system that uses sliding time windows to obtain both longer-term and shorter-term statistical information to dynamically assign sensors to a reduced activity state, adjusting the data rate based on this information, thereby reducing bandwidth usage and processing loads by selectively processing sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensors continuously provide sensor data at a constant high rate, then the system can respond quickly to changes in the observed system, but bandwidth usage and processing resource consumption increase
Solution Approach 1:
The sensor network dynamically adjusts the data provision rate of sensors based on current activity levels and statistical information about the observed system. Sensors transition between active state (providing data at original rate) and reduced activity state (providing data at reduced rate), allowing the system to respond quickly when needed while conserving bandwidth during stable periods.
Solution Approach 2:
The system changes the parameter of data provision rate based on statistical analysis. By computing statistical information about the observed system and comparing it against thresholds, the system adjusts whether sensors provide data at the original rate or reduced rate, optimizing the balance between response speed and bandwidth consumption.
2Measurement precision
If sensors continuously provide sensor data at a constant high rate, then the system maintains high measurement precision, but processing resource consumption increases
Solution Approach 1:
The processing system dynamically adjusts its operation by receiving sensor data at variable rates. When sensors are in reduced activity state, they provide data at a reduced rate, directly lowering the processing resource consumption. The system maintains measurement precision by transitioning sensors back to active state when statistical analysis indicates changes in the observed system.
Solution Approach 2:
The system changes the data processing load by adjusting the rate at which sensors provide data. Based on statistical information and activity level assessments, the system modifies the data flow rate into the processing system, reducing processing resource consumption during stable conditions while maintaining the capability for precise measurement when needed.
3Loss of information
If the system processes all sensor data from all sensors, then complete information is available for analysis, but the complexity of data management increases
Solution Approach 1:
The system applies different data collection strategies to different sensors based on local conditions. Sensors are individually assessed for activity levels and transitions between active and reduced activity states based on their specific statistical information and observed system changes, allowing differentiated data management rather than uniform processing of all sensor data.
Solution Approach 2:
The data management system dynamically adjusts which sensors provide data and at what rates. By continuously assessing activity levels and statistical information, the system adapts the data collection strategy in real-time, simplifying data management during stable periods while maintaining information completeness when changes occur.
4Loss of energy
If sensors provide data at a reduced rate to conserve bandwidth, then resource consumption decreases, but the system's ability to detect rapid changes deteriorates
Solution Approach 1:
The system uses feedback from statistical analysis of sensor data to control sensor activity states. By continuously computing statistical information and comparing it against thresholds, the system detects changes in the observed system and provides feedback to transition sensors between active and reduced activity states, ensuring reliable change detection while conserving bandwidth during stable periods.
Solution Approach 2:
The system performs preliminary statistical analysis on sensor data to predict future states and determine optimal sensor activity levels. By analyzing statistical information before making decisions about sensor activation, the system prepares in advance for potential changes, maintaining detection reliability while optimizing bandwidth consumption.
Data Source
AI summary
According to an example aspect of the present invention, there is provided an apparatus configured to obtain longer-term statistical information from output data of plural sensors of a sensor network, using a sliding time window of a first length, obtain shorter-term statistical information from output data of the plural sensors of the sensor network, using a sliding time window of a second length which is shorter than the first length, and assign at least a subset of sensors of the sensor network into a reduced activity state based on sensor data from each respective one of the at least the subset of the sensors of the sensor network, wherein the apparatus is configured to select a rate at which sensors in the reduced activity state provide sensor data based at least on the longer-term statistical information and the shorter-term statistical information.