Swarm-Intelligent Adaptive Sampling for Distributed Environmental Monitoring Under Uncertain Spatiotemporal Dynamics

Abstract

Environmental monitoring increasingly relies on dense and frequent measurements of spatiotemporally evolving fields such as air quality, water contamination, ecological indicators, and urban microclimates. Traditional fixed sensing infrastructures often exhibit limited spatial coverage and poor adaptability to rapid changes in the underlying phenomena. Mobile sensor swarms, composed of many low-cost platforms, offer a complementary approach by physically repositioning sensing resources in response to inferred uncertainty and operational constraints. Designing principled adaptive sampling strategies for such swarms is challenging due to stochastic dynamics, partial observability, communication limits, and heterogeneous sensing capabilities. This work studies swarm-intelligent adaptive sampling for distributed environmental monitoring under uncertain spatiotemporal dynamics, focusing on linear modeling and coordination mechanisms that remain computationally tractable at scale. The methodology integrates linear state-space representations of environmental fields, uncertainty-aware observation models, and linear optimization formulations that approximate information-driven objectives while respecting motion and resource constraints. A distributed control structure is adopted in which individual agents exploit local predictions and neighborhood exchanges to update sampling decisions with limited communication. Simulation-based analyses on stylized but representative scenarios illustrate how the proposed framework shapes sensor trajectories, balances exploration and exploitation, and responds to unmodeled disturbances or structural model errors. The study also examines sensitivity to network topology, communication sparsity, and model mismatch, and discusses how these factors influence achievable performance. Overall, the results highlight conditions under which swarm-intelligent adaptive sampling can systematically improve coverage of dynamically evolving environmental fields relative to static or non-adaptive baselines, while maintaining moderate complexity and retaining robustness to uncertainty in the spatiotemporal dynamics.