Optimizing Signal Transmission in Underground WSN: Addressing Multipath Fading with Random Walk Kalman Filter and Diffusion LMS

The self-organizing method of distributed Wireless Sensor Networks (WSN) has an energy-efficient feature that can detect and assign channels to each node at different frequencies to avoid inter-node collision. The fading and interference of the channel may disconnect the wireless linkage between connected nodes. Most of the published work on this topic to date use digital filters to eliminate noise and interference for the wireless sensor network topology. However, estimation using a digital filter may not be the perfect candidate for communication in an underground mine, where node-to-node transmission is greatly affected by multipath fading. We propose a power control -based decision criteria for node transmission in case of deep fading to save energy. The and the node can also automatically re-establishment connection by adjusting its transmission power. The goal of the auto power control mechanism is to ensure the target Mean Squared Error performance in case of a random fading channel. The main objective of this work is to use a Random Walk -based Kalman Filtering scheme with Diffusion Least Mean Square (LMS) algorithm that can reduce the multi-path fading effect more accurately for successful signal transmission in in an underground environment. Our simulation results show that the proposed method outperforms the Mean Squared Error performance at low power consumption over the existing schemes.