Predictive Data-Driven Energy Efficiency in a Field-Deployed Wireless Sensor Network

The limited energy resources in wireless sensor networks (WSNs) is a critical challenge to long-term operation, particularly in remote areas where recharging batteries is not easily feasible. This article proposes an energy-efficient approach based on convolutional neural networks (CNNs) for on-device data prediction at the cluster head (CH) level. By substituting certain data transmissions with predicted sensor values, this method minimizes radio communication, the most energy-intensive task in sensor nodes (SNs), thereby extending the network’s lifetime. We evaluate the performance of this approach by analyzing two neural network structures and operational modes in an environmental monitoring context. Simulations with real-world sensor data show that the CNN model accurately predicts sensor values with minimal error. In addition, the solution is validated through real-world deployment in a WSN, with real-time monitoring, energy-efficient communication, and frame management. A detailed energy profile and consumption assessment is provided, alongside an estimation of network autonomy. Results indicate a reduction in data transmissions by over 50%, enabling $\approx 34.3\%$ of energy savings and $\approx 57.0\%$ improvement in network autonomy without compromising accuracy. This solution offers a practical approach for sustainable, long-term WSN deployments.