The tiny, battery-powered sensor nodes of the wireless sensor networks (WSNs) sense and send reports to a processing center called sink or base station. The sensor nodes require more energy while gathering information for longer durations. This study proposes a protocol heterogeneous in energy which analyzes basic distributed clustering routing protocol low-energy adaptive clustering hierarchy (LEACH) with BAT optimization algorithm to be used for cluster formation and cluster-head (CH) selection. Pipelining is used for packet scheduling. Simulations show that the energy consumption gets reduced significantly.
The sensor nodes used in Wireless Sensor Networks (WSN) perform close-range sensing in any environment and are compact, battery-powered, light-weight devices. The overall network performance depends on the routing protocols in the network layer and the flow control protocols at the data link layer. This study proposes a novel routing protocol by adapting the Minimum Spanning Tree (MST), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Clustering with One-Time Setup (COTS) to save energy and maximize the network life time and reduce the network delay. The intercluster communication among Cluster Heads (CH) has been proposed based on the Distance Energy-based MST (DE-MST) technique and a novel pipelining technique was introduced for effective channel utilization. Simulations showed an improvement over LEACH, MST-based clustering, and COTS techniques by this method.
Wireless experts worldwide have become interested in using Autoencoders (AEs) for modelling communication systems as an end-to-end reconstruction task. This approach optimizes both the transmitter and receiver components simultaneously, offering flexibility and convenience for representing complex channel models. Traditional communication systems rely on conventional models and assumptions that limit their utilization of limited frequency resources and hinder their ability to adapt to new wireless applications. However, with the rise of Artificial Intelligence, new wireless systems are capable of learning from wireless spectrum data and optimizing their performance. In this paper, the use of deep learning with autoencoders is explored to create an end-to-end communication system that replaces traditional transmitter and receiver activities. The autoencoder architecture effectively addresses channel impairments and enhances overall performance. Simulation results indicate that autoencoders surpass conventional communication systems in terms of Block Error Rate performance, even when facing impairments in the autoencoder's channel layer and using different neural network optimization algorithms.
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