Improving Energy Efficiency in Three-Tier IoT Sensor Networks Through NDFT-based Power Optimization
Pallavi Joshi, Anamika Kumari, Navodit Bhardwaj, Hitesh Tekchandani, Praveen Kumar Shukla · 2024
The data transmission process in the Internet of Things based sensor network is crucial as many applications require data movement from one device to another. As the network size grows, the volume of data transmission increases, reducing the overall energy efficiency of the network and leading to higher communication costs. Additionally, The execution time also increases as a result of this problem. Some researchers work on reducing the communication cost by using the clustering approach but these methods have not fully achieved the desired improvements. This paper explores the implementation of the discrete Fourier transform (DFT) on nonequidistant sensor nodes within a clustered sensor network to address these challenges. The method produces Fourier coefficients with estimation in error limits. Once the clusters become communication cost-efficient, the data routing is performed using the power constraints of the nodes, cluster leaders, and the base station. The nodes in every cluster transmit their measurements to a CL. CLs fuse the signals received by sensor nodes and send them to a base station on orthogonal fading channels. The channel estimation can be done by sending pilot signals to BS from CL. The Bayesian Cramér-Rao bound is obtained from mean square error and linear minimum mean square error estimator at BS. The proposed system demonstrates effective performance across various constraint parameters and proves to be power efficient. It has reduced the transmission cost by 35.2% and execution time by 24.13%.