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Title Analysing Neural Network Techniques For Efficient Routing In Wireless Sensor Networks
ID_Doc 9026
Authors Ahmer M.; Sajid A.; Bin Anwar M.H.; Verma T.; Khare P.; Shafeeq M.
Year 2024
Published 2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024
DOI http://dx.doi.org/10.1109/IC3TES62412.2024.10877581
Abstract Wireless Sensor Networks (WSNs) play a crucial role in fields such as environmental monitoring, healthcare, and smart city infrastructure. Effective data routing is vital for enhancing network performance and prolonging the lifespan of network nodes. This paper presents a comparative analysis of neural network-based routing algorithms versus traditional protocols in WSNs. We assess their performance based on energy consumption, packet delivery ratio, and end-to-end delay under various network conditions. The findings highlight the potential of machine learning techniques in improving WSN routing protocols, leading to more intelligent and adaptive network management solutions. Future research could investigate the integration of other advanced machine learning models and their applications in large-scale and heterogeneous WSN environments. © 2024 IEEE.
Author Keywords Data Routing Efficiency; End-to- End Delay; Energy Consumption; Machine Learning; Neural Network-Based Routing; Packet Delivery Ratio; Wireless Sensor Networks (WSNs)


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