Topologically-Resilient IoT Sensor Networks: Applying Neuromorphic Collective Memory to Edge-Based Healthcare and Agricultural Monitoring

Distributed IoT sensor networks deployed in healthcare and agricultural monitoring applications share a fundamental vulnerability: individual node failure produces coverage gaps that compromise the safety and continuity of monitoring a patient goes unobserved, an incubation fault goes unreported. Existing approaches address node failure reactively, replacing or rerouting after failure is observed, rather than anticipating failure before it disrupts monitoring coverage. We propose a topological resilience framework for IoT sensor networks inspired by the NeuroTopo-Swarm distributed neuromorphic memory architecture for swarm robotics. Our framework encodes the structural health of a sensor network as Betti number invariants of the inter node communication graph, enabling predictive fault detection and preemptive coverage reconfiguration before observable node failure. We demonstrate the framework across two application domains drawn from our prior deployed systems: healthcare fall detection using edge AI on Raspberry Pi [1] and IoT-based agricultural incubation monitoring [26]. Simulation results show that topological health monitoring detects node degradation a mean of 10.3 ± 3.5 timesteps before observable failure, reducing coverage gap duration by 71% compared to reactive approaches, while adding less than 4% computational overhead on Raspberry Pi 4B hardware. This work establishes a principled bridge between distributed swarm robotics resilience theory and practical IoT deployment, demonstrating that topological fault tolerance is computationally feasible on resource-constrained edge hardware.