Mobile Ambient Sensor Sampling for Coordinated Coverage Control
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Solution Overview
Problem
Current mobile sensing technologies using vehicles lack synchronized and coordinated sampling, leading to inadequate spatial and temporal coverage of data points, with sensors often sampling at unspecific times and locations, resulting in inefficient data collection and processing.
Innovation Solution
A method for controlling sampling at multiple mobile ambient sensors, which assigns precise sampling locations and periodicities based on desired measuring locations and refractory periods, ensuring optimal coverage and avoiding clustering, by utilizing a controller that receives data on sensor routes and refractory periods to coordinate sensor operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensors sample periodically without synchronization, then sensing frequency is maintained, but spatial and temporal coverage becomes insufficient with clustering or sparseness of samples
Solution Approach 1:
The system pre-calculates and assigns specific sampling locations to each sensor based on their routes and refractory periods before data collection begins. This preliminary assignment ensures that sensors sample at optimally spaced locations and times, preventing both clustering and sparseness of samples while maintaining the required sensing frequency.
Solution Approach 2:
The system uses refractory period information as feedback to dynamically adjust sampling assignments. By considering each sensor's refractory period (the minimum time required between consecutive measurements), the system assigns sampling locations that respect these constraints, ensuring comprehensive coverage without requiring sensors to sample too frequently at the same location.
2Loss of information
If measuring period is extended to remedy deficient coverage, then spatial coverage improves, but equipment is tied up unnecessarily and data set size inflates
Solution Approach 1:
The system pre-assigns multiple sampling locations to each sensor based on their routes and refractory periods, optimizing the measuring period to be as short as possible while still achieving comprehensive spatial coverage. This eliminates the need to extend the measuring period unnecessarily.
Solution Approach 2:
Different sensors are assigned different sampling locations and periods based on their specific routes, refractory periods, and the coverage requirements of different areas. This localized optimization allows the system to achieve comprehensive coverage with minimal total measuring time.
3Quantity of substance
If multiple sensors are deployed without coordination, then sensing capacity increases, but sampling locations become uncontrolled and data clustering occurs
Solution Approach 1:
The system uses feedback from each sensor's route information and refractory period to dynamically assign specific sampling locations. This coordinated assignment ensures that multiple sensors sample at different, optimally spaced locations rather than clustering at the same places, maintaining measurement precision while utilizing the full sensing capacity of all deployed sensors.
Solution Approach 2:
The system divides the monitoring area into multiple sampling locations and assigns different segments (locations) to different sensors based on their routes. This segmentation prevents multiple sensors from sampling at the same location simultaneously, eliminating data clustering while maintaining comprehensive coverage.
Data Source
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AI summary
A method of controlling sampling at a plurality of mobile ambient sensors, comprising: providing a plurality of ambient sensors (220, 222, 224, 226), each sensor moving periodically along a predefined route (230, 232, 234); receiving one or more desired measuring locations (240, 250); and assigning to a first one of the sensors a first sampling location (A, B, C, D), which is at or near the desired measuring location.