Spatio-Temporal Sensor Selection for Mobile Wireless Networks
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Solution Overview
Problem
Existing sensor selection methods in wireless networks and participatory sensing scenarios face challenges in efficiently selecting a minimal subset of relevant sensor nodes to cover a deployment region, especially with mobile nodes, where energy conservation and accurate data reconstruction are critical, and coverage-based selection is impractical for sensors without a natural coverage area.
Innovation Solution
A method that divides a region of interest into a grid with adaptable cell sampling frequencies, allowing for efficient selection and tracking of mobile nodes, considering energy constraints and other factors, to achieve the required sampling frequency while extending battery life and reducing data processing burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If coverage-based selection is used to select minimal subset of sensor nodes, then energy consumption is reduced, but the solution does not scale well with mobile nodes and is not practical for sensors without natural coverage area
Solution Approach 1:
The patent divides the deployment region into a grid of cells, where each cell can independently select sensor nodes. This segmentation allows the system to handle mobile nodes more effectively by reassigning them to different cells as they move, rather than requiring complete reselection of coverage subsets. Each cell maintains its own minimal sensor subset, enabling localized optimization that scales better with mobility.
Solution Approach 2:
The patent introduces dynamic sensor selection within each cell based on current sensor positions and cell sampling frequency requirements. Instead of static coverage subsets that must be completely reselected when nodes move, the system dynamically adjusts which sensors serve which cells, allowing seamless adaptation to mobile node scenarios while maintaining energy efficiency.
2Measurement precision
If high spatio-temporal resolution of sensor data is achieved, then accurate reconstruction of observed phenomenon is improved, but computational effort and data processing burden increase
Solution Approach 1:
The patent applies different cell sampling frequencies to different spatial cells based on local requirements. Rather than uniformly high sampling across all sensors, each cell receives the minimum necessary sampling frequency to achieve accurate reconstruction locally, reducing overall data volume and computational burden while maintaining necessary precision where needed.
Solution Approach 2:
The system selects only the minimal subset of sensors needed for each cell to achieve the required sampling frequency, avoiding the excessive action of collecting data from all sensors at all times. This partial action approach maintains measurement precision by ensuring sufficient sampling where needed while reducing computational effort by excluding unnecessary sensors from each cell's data collection.
3Measurement precision
If cell sampling frequency is increased to improve data accuracy, then reconstruction accuracy is improved, but energy consumption of sensor nodes increases
Solution Approach 1:
By segmenting the region into cells with independent sampling frequency requirements, the system can optimize energy consumption locally. Each cell's sampling frequency is set to the minimum necessary for accurate reconstruction of that specific region, preventing unnecessary high-rate sampling in areas where lower rates suffice, thus reducing overall energy consumption while maintaining necessary accuracy.
Solution Approach 2:
The patent changes the sampling frequency parameter dynamically for each cell based on local phenomenon characteristics and accuracy requirements. Rather than using a fixed high sampling rate across all sensors, the system adjusts the sampling frequency parameter for each cell to achieve the necessary reconstruction accuracy with minimal energy expenditure, balancing precision and power consumption.
Data Source
AI summary
A computer implemented method, computer program product and computer system for sensor selection. The computer system can run the computer program to execute the method by dividing a two-dimensional area into cells, wherein the cells are arranged in a grid; receiving a selection trigger for a subset of cells of the grid, wherein at least one cell of the subset has at least one sensor and the at least one cell has a cell sampling frequency associated; determining a set of constraints for the at least one sensor; selecting the at least one sensor if the at least one sensor complies with the set of constraints; and calculating a sampling frequency of the at least one sensor dependent on the cell sampling frequency of the at least one cell.