An efficient neural network LEACH protocol to extended lifetime of wireless sensor networks

被引:1
作者
El-Sayed, Hamdy H. [1 ]
Abd-Elgaber, Elham M. [1 ]
Zanaty, E. A. [1 ]
Alsubaei, Faisal S. [2 ]
Almazroi, Abdulaleem Ali [3 ]
Bakheet, Samy S. [4 ]
机构
[1] Sohag Univ, Fac Comp & Artificial Intelligence, Dept Comp Sci, Sohag 82524, Egypt
[2] Univ Jeddah, Coll Comp Sci & Engn, Dept Cybersecur, Jeddah, Saudi Arabia
[3] King Abdulaziz Univ, Fac Comp & Informat Technol Rabigh, Dept Informat Technol, Rabigh 21911, Saudi Arabia
[4] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Al Kharj, Saudi Arabia
关键词
WSNs; Neural networks; Energy hole; LEACH; ILEACH; NN_ILEACH;
D O I
10.1038/s41598-024-75904-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions-representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol's potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs.
引用
收藏
页数:21
相关论文
共 32 条
[1]   Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications [J].
Abu Alsheikh, Mohammad ;
Lin, Shaowei ;
Niyato, Dusit ;
Tan, Hwee-Pink .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2014, 16 (04) :1996-2018
[2]  
Amirthalingam K, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER APPLICATIONS (ICACA), P255, DOI 10.1109/ICACA.2016.7887961
[3]   Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia [J].
Ayehu, Getachew ;
Tadesse, Tsegaye ;
Gessesse, Berhan ;
Yigrem, Yibeltal ;
Melesse, Assefa M. .
SENSORS, 2020, 20 (11) :1-25
[4]  
Banal P., 2016, Int. J. Emerg. Technol, V7, P59
[5]   Energy-efficient modified LEACH protocol for IoT application [J].
Behera, Trupti Mayee ;
Samal, Umesh Chandra ;
Mohapatra, Sushanta Kumar .
IET WIRELESS SENSOR SYSTEMS, 2018, 8 (05) :223-228
[6]   Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks [J].
Bhola, Jyoti ;
Soni, Surender ;
Cheema, Gagandeep Kaur .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (03) :1281-1288
[7]  
Daanoune I, 2021, Int J Electr Comput Eng, V11, P3106, DOI [10.11591/ijece.v11i4.pp3106-3113, DOI 10.11591/IJECE.V11I4.PP3106-3113]
[8]   TORM: Tunicate Swarm Algorithm-based Optimized Routing Mechanism in IoT-based Framework [J].
Dogra, Roopali ;
Rani, Shalli ;
Verma, Sandeep ;
Garg, Sahil ;
Hassan, Mohammad Mehedi .
MOBILE NETWORKS & APPLICATIONS, 2021, 26 (06) :2365-2373
[9]  
El-sayed Hamdy H., 2021, International Journal of Advanced Networking and Applications, P4884
[10]  
Elsadig M.A., 2019, Int. J. Adv. Trends Comput. Sci. Eng, V8, P1551, DOI [10.30534/ijatcse/2019/78842019, DOI 10.30534/IJATCSE/2019/78842019]