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Modeling and Improving of Wireless Sensor Network Routing Protocols
Faculty
Engineering
Year:
2024
Type of Publication:
ZU Hosted
Pages:
Authors:
Ahmed Osman Mahmoud Eid
Staff Zu Site
Abstract In Staff Site
Journal:
Volume:
Keywords :
Modeling , Improving , Wireless Sensor Network Routing
Abstract:
It is Critical for wireless sensor networks (WSNs) to have an uninterrupted power supply. Increasing the lifetime of WSNs will need employing preservation mechanism. In many WSN applications, sensor nodes are used to collect the environmental parameters, which are transmitted to the sink node or the base station through communication link. Sensor nodes dissipate energy during wireless data communication, which is higher than consumed for collecting data. This thesis proposes an improvement for LEACH routing protocol that mange the network energy dissipation and prolongs the sensor nodes lifetime. The proposed algorithm in this thesis suggests an equation for selecting the cluster head nodes based on the equation of the sine-cosine optimization algorithm. And depend also the residual energy of each node. And the study also the location of the base station in more than one location because the location of base station is determined based on the application which WSN implemented on it, simulation tests shows that the proposed algorithm outperform LEACH routing protocol in the total number of rounds i.e. the network lifetime, the total amount of dissipated energy, the network stability and the location of the base station has small effect on the network lifetime.
Author Related Publications
Ahmed Osman Mahmoud Eid, "Improved Low Energy Adaptive Clustering Hierarchy in Wireless Sensor Network Routing Protocols", International Journal of Engineering and Technology, 2018
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Ahmed Osman Mahmoud Eid, "Comparative Study of Optimized Wireless Sensor Network Routing Protocols", International Journal of Computer Applications (0975 –8887), 2018
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Ahmed Osman Mahmoud Eid, "An Efficient Convolutional Neural Network Classification Model for Several Sign Language Alphabets", (The Science and Information Organization (SAI, 2023
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Ahmed Osman Mahmoud Eid, "Optimized Hybrid Convolution Neural Network with Machine Learning for Arabic Sign Language Recognition", IIETA, 2024
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Ibrahiem Elsayed Mohamed Zedan, "An Efficient Convolutional Neural Network Classification Model for Several Sign Language Alphabets", (The Science and Information Organization (SAI, 2023
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Amro Ahmed Ismail Morsy , "An Efficient Convolutional Neural Network Classification Model for Several Sign Language Alphabets", (The Science and Information Organization (SAI, 2023
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Ahmed Osman Mahmoud Eid, "An Efficient Convolutional Neural Network Classification Model for Several Sign Language Alphabets", (The Science and Information Organization (SAI, 2023
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