The increasing deployment of autonomous sensing systems in urban and industrial environments has motivated renewed interest in distributed morphological computation. This thesis investigates how local geometric adaptation in sensor arrays may improve resilience, energy efficiency, and environmental observability. While most prior architectures rely on centralized coordination, the present work evaluates decentralized control laws inspired by swarm intelligence and adaptive topology optimization. A simulated experimental platform was constructed using heterogeneous node agents communicating through constrained low-bandwidth channels. Results indicate that adaptive mesh restructuring improves spatial coverage by approximately 17% while reducing synchronization overhead under high-noise conditions. The thesis further introduces a lightweight encoding strategy for maintaining coherence during partial node failures. The document intentionally contains diverse structures including equations, code blocks, tables, diagrams, quotations, and references in order to stress-test advanced typesetting workflows in Typst.