Adaptive Mobile Sensor Positioning for Environmental Monitoring
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Solution Overview
Problem
The insufficient number of mobile sensors available to monitor environments comprehensively at all times necessitates dynamic repositioning to ensure effective data collection.
Innovation Solution
A method involving the creation of a spatio-temporal representation of sensor measurements, which is then applied to an environment state prediction model and a sensor position determination model. This model determines new positions for mobile sensors based on predicted future measurements and uncertainty values, facilitating adaptive sensor repositioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of mobile sensors is increased to monitor every portion of the environment, then measurement coverage is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements dynamic sensor repositioning where mobile sensors automatically move to different locations based on real-time environmental conditions and prediction uncertainties. This allows a limited number of sensors to dynamically cover different portions of the environment, replacing the need for a large static sensor network.
Solution Approach 2:
The system employs self-organizing behavior where sensors autonomously determine their own repositioning decisions based on local uncertainty information and global objectives. Each sensor contributes to reducing overall prediction uncertainty through its measurements, creating a self-optimizing monitoring network.
2Measurement precision
If mobile sensors are repositioned frequently to improve monitoring coverage, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system uses an environment state prediction model to forecast future environmental conditions and identify areas where measurements will be most valuable. Sensors are repositioned in advance to optimal locations based on these predictions, ensuring measurements are taken where they will have maximum impact on reducing uncertainty before conditions change.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where measurement uncertainties are continuously updated based on new sensor data, and these updated uncertainties drive subsequent repositioning decisions. This ensures sensors move only when and where necessary to maintain optimal monitoring effectiveness.
Data Source
AI summary
Techniques are provided for adaptive sensor position determination for multiple mobile sensors. One method comprises obtaining a spatio-temporal representation of sensor measurements, from multiple mobile sensors, wherein the spatio-temporal representation comprises multiple layers each corresponding to a different point in time, wherein a given layer comprises multiple positions, and wherein each position in the given layer corresponds to a possible location for at least one of the multiple mobile sensors in an environment; applying the spatio-temporal representation to an environment state prediction model that generates a prediction of at least one future sensor measurement value for multiple positions in the spatio-temporal representation; applying the predictions of the at least one future sensor measurement value to a sensor position determination model that determines a new position for each of one or more of the multiple mobile sensors; and initiating a movement of the one or more of the multiple mobile sensors to the new position.