Cross-Modality Multivariate Regression for Energy-Bandwidth Economy in Resource-Constrained Agricultural IoTs

This paper presents a deep learning framework for energy-and bandwidth-aware time-series regression for IoT and wireless sensor networks. Leveraging cross-modality structural data dependencies, the approach uses a multivariate regression approach for predicting many different sensor modality time-series from a fewer number of sensor modality data. This reduces the communication bandwidth usage and energy consumption for resource-constrained sensor nodes. In addition, the approach can be leveraged for reducing number of sensors in a system, thus reducing the hardware and associated costs in IoT-based systems. This is done while ensuring successful reconstructions of all the original time-series for the necessary sensing modalities. One key attribute of the proposed framework is that the processing load is placed entirely at the receiver side, thus making it suitable for resource-constrained sensors and IoT based systems. The developed methodology is validated using data collected by a greenhouse sensor IoT system deployed over a period of three years. The results demonstrate the ability of the proposed approach for successful prediction of many sensor modality times series from only one time-series, representing the energy harvesting super-capacitor voltage of the sensor IoT system. The trade-offs between performance, bandwidth usage, number of sensing modalities, scalability, and computation cost are analyzed for providing design and implementation guidelines.