Enhancing Energy Efficiency in Wireless Sensor Networks through I-LEACH: A Data Clustering and Routing Protocol

Main Article Content

Sushil Lekhi, Satvir Singh

Abstract

The inclusion of innovative technology into sensor networks has resulted in notable advancements in several industries, including mining, healthcare, military surveillance, and more. Using a multi-hop approach, nodes are placed strategically across the Region of Interest (RoI) in order to transfer data to the Base Station (BS). In order to address issues with energy consumption, effective cluster head selection, packet loss, routing algorithms, and energy efficiency, the Wireless Sensor Network (WSN) has become a key field of study. The objectives of this research are to increase nodes' Residual Energy, and lengthen the network lifetime. Using an effective clustering mechanism and Cluster Head  election procedure is the suggested method. The goal of choosing a CH is to minimize the distance from the base station while optimizing the node's remaining energy. The protocol acts in two stages: firstly, computing a new Threshold value for the cluster head election process, and secondly, applying a data fusion mechanism based  energy model is used in the network to reduce redundant energy transfer.

Article Details

Section
Articles
Author Biography

Sushil Lekhi, Satvir Singh

[1]Sushil Lekhi,

Dr. Satvir Singh

 

[1]Research Scholar IKGPTU, Associate Professor, IKGPTU rieit.cse.sushil@gmail.com, satvir.singh@ptu.ac.in

Copyright © JES 2024 on-line : journal.esrgroups.org

References

ROY, NIHAR RANJAN AND CHANDRA, PRAVIN. Analysis of data aggregation techniques in wsn. Cluster Computing, 22(3), pp. 571–581,(2020). Springer.

V. NARAYAN, A. DANIEL, Novel protocol for detection and optimization of overlapping coverage in wireless sensor networks (2019).

Faiz, M., & Daniel, A. K. (2021, July). Wireless sensor network based distribution and prediction of water consumption in residential houses using ANN. In International Conference on Internet of Things and Connected Technologies (pp. 107-116). Cham: Springer International Publishing.

NARAYAN, VIPUL AND DANIEL, AK AND RAI, ASHOK KUMAR. Energy Efficient Two Tier Cluster Based Protocol for Wireless Sensor Network. Cluster Computing, 22(3), pp. 574–579,(2020). Springer.’

CHATURVEDI, POOJA AND DANIEL, AK. Trust based node scheduling protocol for target coverage in wireless sensor networks. Cluster Computing, 22(3), pp. 163–173,(2015). Springer.

CHATURVEDI, POOJA AND DANIEL, AK. Trust based energy efficient coverage preserving protocol for wireless sensor networks. Cluster Computing, 22(3), pp. 860–865,(2015). Springer.

Fatima, N., Faiz, M. A. R. M., & Sandhu, R. Machine Learning Functions in Data Mining and Analytics in the Process Industry.

TRIPATHI, ABHISHEK AND GUPTA, HARI PRABHAT AND DUTTA, TANIMA AND MISHRA, RAHUL AND SHUKLA, KK AND JIT,SATYABRAT. Coverage and connectivity in WSNs: A survey, research issues and challenges. I EEE Access, 6(3), pp. 26971–26992,(2018). IEEE.

RAJPOOT, PRINCE AND DWIVEDI, PRAGYA. Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches. W ireless Networks, 26(1), pp. 215–251,(2020). Springer.

YADAV, RAVI AND DANIEL, AK. Fuzzy based smart farming using wireless sensor network. Cluster Computing, 22(3), pp. 1–6,(2018). Springer.

LU, YU-DING AND CHEN, YAO-DONG AND CHEN, MENG-YUAN. The improvement and simulation research of wireless sensor network LEACHprotocol. Journal of Anhui Polytechnic University, 22(4), pp. 13,(2012). Springer.

SHOKOUHIFAR, MOHAMMAD AND JALALI, ALI. A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU-International Journal of Electronics and Communications, 69(1), pp. 432–441,(2015). Elsevier.

ZHENXING, WANG AND WEILI, XIONG AND BAOGUO, XU. A LEACH Cluster Tree Network Routing Algorithm Research [J]. Computer Measurement & Control, 11(3), pp. 5811–5823,(2008). Springer.

JIANG, JIANMING AND SHI, GUODONG AND ZHAO, DEAN AND LI, ZHENGMING AND SHI, BING AND ZHAO, YIGANG ET AL. Intelligent monitoring system of aquaculture parameters based on LEACH protocol.. N ongye Jixie Xuebao= Transactions of the Chinese Society for Agricultural Machinery, 45(11), pp. 286–291,(2014). Chinese Society for Agricultural Machinery.

LI, FANGFANG AND WANG, JING. A New LEACH-Based Routing Algorithm for Wireless Sensor Networks [J]. Chinese Journal of Sensors and Actuators, 10(3), pp. 5811–5823,(2012). Springer.

WAN, CHUANFEI AND DU, SHANGFENG. Improvement and simulation of leach in wireless sensor networks. Jisuanji Yingyong yu Ruanjian, 28(4), pp. 113–116,(2011). Shanghai Institute of Computing Technology.

ROSHAN, KOMAL AND SHARMA, KRITIKA RAI. Improved LEACH protocol with cache nodes to increase lifetime of wireless sensor networks. Cluster Computing, 22(3), pp. 903–908,(2018). Springer.

ZHANG, LI. The improvement and simulation of LEACH clustering routing protocol for WSNs. W uhan University of Technology, Wuhan, 22(3), pp. 1–75,(2009). Springer.

ZAYOUD, MAHA AND ABDULSALAM, HANADY M AND AL-YATAMA, A AND KADRY, SEIFEDINE. Split and merge leach based Routing algorithm for wireless sensor networks. I nternational Journal of Communication Networks and Information Security, 10(1), pp. 155–162,(2018). Springer.

XU, YAN AND YUE, ZHANWEI AND LV, LINGLING. Clustering routing algorithm and simulation of internet of things perception layer based on energy balance. I EEE Access, 7(3), pp. 145667–145676,(2019). IEEE.

GAWADE, ROHIT D AND NALBALWAR, SANJAY L. A centralized energy efficient distance based routing protocol for wireless sensor networks. Journal of Sensors, 2016(3), pp. 5811–5823,(2016). Hindawi.

WU, WENLIANG AND XIONG, NAIXUE AND WU, CHUNXUE. Improved clustering algorithm based on energy consumption in wireless sensor networks. I et Networks, 6(3), pp. 47–53,(2017). IET.

DHAND, GEETIKA AND TYAGI, SS. SMEER: Secure multi-tier energy efficient routing protocol for hierarchical wireless sensor networks. W ireless Personal Communications, 105(1), pp. 17–35,(2019). Springer.’

Faiz, M., & Daniel, A. K. (2023). A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas. International Journal of Nanotechnology, 20(5-10), 851-879.

SUN, GUILING AND ZHANG, ZIYANG AND ZHENG, BOWEN AND LI, YANGYANG. Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics. Sensors, 19(9), pp. 2139,(2019). Multidisciplinary Digital Publishing Institute.

KOLOMVATSOS, KOSTAS AND ANAGNOSTOPOULOS, CHRISTOS AND HADJIEFTHYMIADES, STATHES. Data fusion and type-2 fuzzy inference in contextual data stream monitoring. I EEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), pp. 1839–1853,(2016). IEEE.

KARTHICK, SUYAMBU. TDP: A novel secure and energy aware routing protocol for wireless sensor networks. International Journal of Intelligent Engineering and Systems, 11(2), pp. 76–84,(2018). Springer.

SHARMA, RICHA AND VASHISHT, VASUDHA AND SINGH, UMANG. eeTMFO/GA: a secure and energy efficient cluster head selection in wireless sensor networks. Telecommunication Systems, 22(3), pp. 1–16,(2020). Springer.

MISHRA, MUKESH AND GUPTA, GOURAB SEN AND GUI, XIANG. Trust-Based Cluster Head Selection Using the K-Means Algorithm for Wireless Sensor Networks. Cluster Computing, 22(3), pp. 819–825,(2019). Springer.

AL-HUMIDI, NADA AND CHOWDHARY, GIRISH V. Energy-aware approach for routing protocol by using centralized control clustering algorithm in wireless sensor networks. Cluster Computing, 22(3), pp. 261–274,(2019). Springer.

GOPALAKRISHNA, ARAVIND KOTA AND PAI, MANOHARA MM. Multi-service adaptable routing protocol for wireless sensor networks. Cluster Computing, 22(3), pp. 5811–5823,(oct ” 23” 2012). Google Patents.

HEINZELMAN, WENDI BETH. Application-specific protocol architectures for wireless networks. Cluster Computing, 22(3), pp. 5811–5823,(2000). Springer.

SMARAGDAKIS, GEORGIOS AND MATTA, IBRAHIM AND BESTAVROS, AZER. SEP: A stable election protocol for clustered het- erogeneous wireless sensor networks. Cluster Computing, 22(3), pp. 5811–5823,(2004). Springer.

Kumar Mall, Pawan, et al. "Self-Attentive CNN+ BERT: An Approach for Analysis of Sentiment on Movie Reviews Using Word Embedding." International Journal of Intelligent Systems and Applications in Engineering 12.12s (2024): 612-623.

Narayan, Vipul, et al. "7 Extracting business methodology: using artificial intelligence-based method." Semantic Intelligent Computing and Applications 16 (2023): 123.

Narayan, Vipul, et al. "A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction." Wireless Personal Communications 132.3 (2023): 1819-1848.

Mall, Pawan Kumar, et al. "Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification: AN APPROACH TOWARD TAGGING UNLABELED MEDICAL DATASET." Journal of Scientific & Industrial Research (JSIR) 82.08 (2023): 818-830.

Mall, Pawan Kumar, et al. "FuzzyNet-Based Modelling Smart Traffic System in Smart Cities Using Deep Learning Models." Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities. IGI Global, 2023. 76-95.

Saxena, Aditya, et al. "Comparative Analysis Of AI Regression And Classification Models For Predicting House Damages İn Nepal: Proposed Architectures And Techniques." Journal of Pharmaceutical Negative Results (2022): 6203-6215.

Kumar, Vaibhav, et al. "A Machine Learning Approach For Predicting Onset And Progression"“Towards Early Detection Of Chronic Diseases “." Journal of Pharmaceutical Negative Results (2022): 6195-6202.

Chaturvedi, Pooja, A. K. Daniel, and Vipul Narayan. "A Novel Heuristic for Maximizing Lifetime of Target Coverage in Wireless Sensor Networks." Advanced Wireless Communication and Sensor Networks. Chapman and Hall/CRC 227-242.

Chaturvedi, Pooja, Ajai Kumar Daniel, and Vipul Narayan. "Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches." (2021).

Narayan, Vipul, and A. K. Daniel. "A novel approach for cluster head selection using trust function in WSN." Scalable Computing: Practice and Experience 22.1 (2021): 1-13.