Indoor positioning systems (IPS) are essential for modern smart home services, yet conventional Bluetooth Low Energy (BLE) techniques often incur high infrastructure costs and energy overhead due to the requirement for dedicated beacons. This paper proposes a opportunistic positioning framework that leverages existing operational traffic, specifically periodic heartbeat messages, within BLE Mesh automation networks. To address signal instability and the irregular traffic patterns inherent in mesh environments, we implement a data processing pipeline integrating Kalman filtering and temporal aggregation. These stabilized Received Signal Strength Indicator (RSSI) features are then utilized by a Deep Neural Network (DNN) to map sparse signal fingerprints to spatial coordinates. Experimental validation in a realistic office environment demonstrates that the proposed DNN model achieves high localization precision, with a Mean Absolute Error (MAE) of 37.5 cm and 46.7 cm for the X and Y coordinates, respectively. Our findings suggest that existing smart home infrastructures can be transformed into dual-purpose networks, enabling zero-overhead positioning while maintaining primary communication tasks.