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A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous
Roger Cesarié Ntankouo Njila1, Mir Abolfazl Mostafavi1, Jean Brodeur2
1Centre for Research in Geospatial Data and Intelligence, Department of Geomatics Sciences, Université Laval, Quebec, QC G1V 0A6, Canada.
Sensors (Basel, Switzerland)
|October 26, 2021
Summary
This study introduces a decentralized fuzzy spatial reasoning approach for sensor networks (SN) to understand interactions between dynamic phenomena with uncertain boundaries. This method enhances real-time decision-making by analyzing spatial relations and evolution of environmental events.
Area of Science:
- Environmental Science
- Computer Science
- Spatial Computing
- Sensor Networks
Background:
- Sensor networks (SN) are vital for monitoring spatiotemporal phenomena like pollution and forest fires.
- Existing decentralized spatial computing methods struggle to infer interactions of dynamic phenomena with uncertain spatial extents at the sensor node level.
- Simultaneous detection of multiple phenomena is key to understanding their spatial and temporal interactions.
Purpose of the Study:
- To develop a decentralized fuzzy rule-based spatial reasoning approach for analyzing spatial relations between evolving spatial phenomena with fuzzy boundaries.
- To address the limitations of current methods in handling uncertainty in the spatial extents of dynamic phenomena within sensor networks.
- To enable more effective reasoning on spatial interactions and evolution of environmental phenomena.
Main Methods:
- Proposed a decentralized fuzzy rule-based spatial reasoning approach.
- Introduced a fuzzy-crisp representation for dynamic phenomena, dividing them into five zones (kernel, conjecture, exterior, and their boundaries).
- Sensor nodes report their location relative to these zones, enabling aggregation and reasoning on spatial relations and phenomenon evolution.
Main Results:
- The approach effectively depicts spatial relations between two evolving spatial phenomena with fuzzy boundaries.
- Aggregation of sensor node data allows for reasoning on the spatial interactions and evolution of observed phenomena.
- The fuzzy-crisp representation enhances the analysis of dynamic phenomena in sensor networks.
Conclusions:
- The developed decentralized fuzzy spatial reasoning approach improves the inference of spatial relations and interactions between dynamic phenomena with fuzzy boundaries.
- This method provides valuable near real-time information on the state and evolution of environmental phenomena.
- Enhanced spatial information supports more informed decision-making in environmental monitoring and management.
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