The increasing complexity of low-voltage distribution assets calls for scalable, non-intrusive monitoring solutions that are sustainable in terms of energy consumption and maintenance. This contribution presents Sentinel-GRID, a distributed, beacon-based approach for multi-parameter monitoring of secondary substations and distribution transformers, built around ultra-low-power IoT sensor nodes operating in an event-driven "fit-and-forget" paradigm. The nodes integrate heterogeneous sensing and remain mostly in deep sleep, waking only when relevant conditions occur, thus drastically reducing energy draw and enabling multi-year autonomy, potentially supported by micro energy-harvesting. Data collection relies on a hybrid communication architecture: BLE beacons [2] for opportunistic acquisition through mobile gateways and longrange connectivity (e.g., LoRaWAN) for periodic reporting and resilient backhaul. In critical situations, the system enables priority alerting over high-availability channels. Alongside physical sensors, the solution also includes low-power computer-vision nodes for visual inspection: by monitoring retroreflective strips applied near tightening points, it is possible to estimate micro-displacements and movements of transformer bolts [3], providing a direct indicator of mechanical loosening and supporting multimodal diagnostics. On the backend, a cloud platform integrates a Digital Twin and ML/AI analytics tailored to sparse and asynchronous data streams, offering anomaly detection, explainable diagnostics, and remaining useful life (RUL) estimation. A representative use case is the early identification of loosened bolted connections, a typical precursor to overheating and reliability degradation. The proposed approach aims to reduce OPEX, increase safety, and accelerate predictive maintenance adoption in distribution networks.