Collaborative Sensing

Collaborative sensing integrates data from multiple sensors or agents to improve perception, accuracy, and robustness beyond the capabilities of individual sensors. Current research focuses on optimizing data fusion techniques, including Bayesian methods for trust management and efficient algorithms like autoencoders for data compression and transmission in resource-constrained environments, often employing neural networks for feature extraction and fusion. This field is crucial for applications like autonomous vehicles, environmental monitoring, and multi-agent systems, enabling improved decision-making and enhanced situational awareness in complex scenarios.