A Pegasis-Driven Approach for Enhanced Performance for Optimizing Energy Efficiency in Wireless Sensor Networks
Keywords:
Energy efficiency, Wireless sensor networks, Optimization, Enhanced performance. Pegasus.Abstract
In order to improve the performance of wireless sensor networks (WSN), it is necessary to use energy-saving measures, cluster-based hierarchical systems, and efficient routing protocols. Protocols like IEE-LEACH and variants of the PEGASIS protocol are studied in detail because of the important roles they play in reducing energy consumption and extending the lifetime of networks. In addition, it delves into the performance analysis of algorithms like AO, showing how nodes can be more resilient and efficient in their energy consumption in different scenarios. Also included are new methods like IMA-NCS-3D that aim to make networks last longer by balancing traffic and optimizing node scheduling. The complete abstract provides insight into the dynamic.
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Abdelaal, M., Theel, O., Kuka, C., Zhang, P., Gao, Y., Bashlovkina, V., ... & Fränzle, M. (2016). Improving energy efficiency in QoS-constrained wireless sensor networks. International Journal of Distributed Sensor Networks, 12(5), 1576038.
Balasubramanian, D. L., & Govindasamy, V. (2019). Study on evolutionary approaches for improving the energy efficiency of wireless sensor networks applications. EAI Endorsed Transactions on Internet of Things, 5(20), e2-e2.
Yadav,s. k. ., & Gangwar, S. . (2023). Increase the reliabitity of the PEGASIS Mesh Network Using The Substitution Method. The Journal of Theoretical and Applied Information Technology, ISSN: 1992-8645, on 31 January 2023. Vol.101. No 2PP 912-920 (c) 2023 Little lion Scientific.
Cui, Y.; Lv, J.; Yuan, H. Development of a wireless sensor network for distributed measurement of total electric field under HVDC transmission lines. Int. J. Distrib. Sens. Netw. 2014, 10, 1406–1409.
Yadav, S. K. ., & Gangwar, S. . (2023). New Measure Routing Algorithm for PEGASIS Wireless Sensor Network. International Journal on Recent and Innovation Trends in Computing and Communication,11(5s),357–361. https://doi.org/10.17762/ijritcc.v11i5s.7044
Cui, Y.; Yuan, H.; Song, X.; Zhao, L.; Liu, Y.; Lin, L. Model, design, and testing of field mill sensors for measuring electric fields under high-voltage direct-current power lines. IEEE Trans. Ind. Electron. 2017, 65, 608–615
Yadav, S. K. ., & Gangwar, S. . (2023). Ferformance Analysis of PEGASIS and LEACH Routing Protocol in WSN. Second International Conference on Advance in S/W Engineering and Information Technology, ASIT-2023,17 June 2023.
Dickson, A.J.; Burton, S.; Shepertycky, M. Digitally controlled energy harvesting power management system. IEEE Trans. Emerg. Sel. Top. Power Electron. 2015, 4, 303–317.
Fort, A.; Mugnaini, M.; Vignoli, V. Design, modeling, and test of a system for atmospheric electric field measurement. IEEE Trans. Instrum. Meas. 2011, 60, 2778–2785.
Karakus, C., Gurbuz, A. C., & Tavli, B. (2013). Analysis of energy efficiency of compressive sensing in wireless sensor networks. IEEE Sensors Journal, 13(5), 1999-2008.
Khan, A.; Khan, F. A Cost-Efficient Radiation Monitoring System for Nuclear Sites: Designing and Implementation. Intell. Autom. Soft Comput. 2022, 32, 1357–1367.
Khan, M. N., Rahman, H. U., Almaiah, M. A., Khan, M. Z., Khan, A., Raza, M., ... & Khan, R. (2020). Improving energy efficiency with content-based adaptive and dynamic scheduling in wireless sensor networks. IEEE Access, 8, 176495-176520.
Liu, X.; Hu, S.; Li, M. Energy-efficient resource allocation for cognitive industrial Internet of Things with wireless energy harvesting. IEEE Trans. Industr. Inform. 2021, 17, 5668–5677.
Liu, Y., Wu, Q., Zhao, T., Tie, Y., Bai, F., & Jin, M. (2019). An improved energy-efficient routing protocol for wireless sensor networks. Sensors, 19(20), 4579.
Masoum, A.; Meratnia, N.; Havinga, P. An energy-efficient adaptive sampling scheme for wireless sensor networks. In Proceedings of the 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 2–5 April 2013.
Nkwogu, D.; Allen, A. Adaptive sampling for WSAN control applications using artificial neural networks. J. Sens. Actuator Netw. 2012, 1, 299–320.
Taneja, A.; Saluja, N.; Rani, S. An energy efficient dynamic framework for resource control in massive IoT network for smart cities. Wirel. Netw. 2022, 1–12.
Zhu, M.; Yi, Z.; Yang, B.; Lee, C. Making use of nanoenergy from human–nanogenerator and self-powered sensor enabled sustainable wireless iot sensory systems. Nano Today 2021 ,36, 101016
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