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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

Effective Hybrid Deep Learning Model of GAN and LSTM for Clustering and Data Aggregation in Wireless Sensor Networks

Author(s): K. Hemalatha* and M. Amanullah

Volume 14, Issue 2, 2024

Published on: 19 January, 2024

Page: [122 - 133] Pages: 12

DOI: 10.2174/0122103279275330231217072855

Price: $65

Abstract

Background: Wireless Sensor Networks (WSNs) have emerged as a crucial technology for various applications, but they face a lot of challenges relevant to limited energy resources, delayed communications, and complex data aggregation. To address these issues, this study proposes novel approaches called GAN-based Clustering and LSTM-based Data Aggregation (GCLD) that aim to enhance the performance of WSNs.

Methods: The proposed GCLD method enhances the Quality of Service (QoS) of WSN by leveraging the capabilities of Generative Adversarial Networks (GANs) and the Long Short-Term Memory (LSTM) method. GANs are employed for clustering, where the generator assigns cluster assignments or centroids, and the discriminator distinguishes between real and generated cluster assignments. This adversarial learning process refines the clustering results. Subsequently, LSTM networks are used for data aggregation, capturing temporal dependencies and enabling accurate predictions.

Results: The evaluation results demonstrate the superior performance of GCLD in terms of delay, PDR, energy consumption, and accuracy than the existing methods.

Conclusion: Overall, the significance of GCLD in advancing WSNs highlights its potential impact on various applications.

« Previous Next »
[1]
Ravi G, Das MS, Karmakonda K. Reliable cluster based data aggregation scheme for IoT network using hybrid deep learning techniques. Measurement. Sensors 2023; 27: 100744.
[2]
Arunachalam GS. S V, Ramalingam G, Nanjappan R. A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network. Concurr Comput 2023; 35(2): e7492.
[http://dx.doi.org/10.1002/cpe.7492]
[3]
Anand J, Raja Paul Perinbam J. A survey on energy efficient biomedical wireless sensor networks. Am Int J Res Sci Technol Eng Mathem 2014; 3(7): 212-6.
[4]
Revathi A, Santhi SG. Cluster-based congestion avoidance and data aggregation routing in wireless sensor networks.Computational Intelligence: Select Proceedings of InCITe 2022. Singapore: Springer. Nat Singap 2023; 325-39.
[http://dx.doi.org/10.1007/978-981-19-7346-8_28]
[5]
Darabkh KA, Kassab WK, Khalifeh AF. LiM-AHP-GC: Life time maximizing based on analytical hierarchal process and genetic clustering protocol for the internet of things environment. Comput Netw 2020; 176: 107257.
[http://dx.doi.org/10.1016/j.comnet.2020.107257]
[6]
Anand J. Analyzing the performance of diverse LEACH algorithms for wireless sensor networks. Int J Adv Network Appl 2012; 04(03): 1610-5.
[7]
Abdollahzadeh S, Navimipour NJ. Deployment strategies in the wireless sensor network: A comprehensive review. Comput Commun 2016; 91-92: 1-16.
[http://dx.doi.org/10.1016/j.comcom.2016.06.003]
[8]
Pourghebleh B, Navimipour NJ. Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research. J Netw Comput Appl 2017; 97: 23-34.
[http://dx.doi.org/10.1016/j.jnca.2017.08.006]
[9]
Han S, Lin J, Zhao S, et al. Location privacy preserving distance computation for spatial crowdsourcing. IEEE Internet Things J 2020; 7(8): 7550-63.
[http://dx.doi.org/10.1109/JIOT.2020.2985454]
[10]
Geethalakshmi M, Venkatesh J, Mageswari RU, Mahalakshmi A, Anand J, Partheepan R. Fuzzy based route optimization in wearable biomedical wireless sensor network. AIP Conf Proc 2023; 2523: 020156.
[http://dx.doi.org/10.1063/5.0112396]
[11]
Anand J, Perinbam JRP, Meganathan D. Q-learning-based optimized routing in biomedical wireless sensor networks. J Inst Electron Telecommun Eng 2017; 63(1): 89-97.
[http://dx.doi.org/10.1080/03772063.2016.1229580]
[12]
Pourghebleh B, Hayyolalam V. A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things. Cluster Comput 2020; 23(2): 641-61.
[http://dx.doi.org/10.1007/s10586-019-02950-0]
[13]
Mohseni M, Amirghafouri F, Pourghebleh B. CEDAR: A cluster-based energy-aware data aggregation routing protocol in the internet of things using capuchin search algorithm and fuzzy logic. Peer-to-Peer Netw Appl 2023; 16(1): 189-209.
[http://dx.doi.org/10.1007/s12083-022-01388-3]
[14]
Sharmin S, Ahmedy I, Md Noor R. An energy-efficient data aggregation clustering algorithm for wireless sensor Networks using hybrid PSO. Energies 2023; 16(5): 2487.
[http://dx.doi.org/10.3390/en16052487]
[15]
Joshi S, Anithaashri TP, Rastogi R, Choudhary G, Dragoni N. IEDA-HGEO: Improved energy efficient with clustering-based data aggregation and transmission protocol for underwater wireless sensor networks. Energies 2022; 16(1): 353.
[http://dx.doi.org/10.3390/en16010353]
[16]
Sindhuja M, Vidhya S. B S J, Shajin FH. Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation. Ad Hoc Netw 2023; 140: 103037.
[http://dx.doi.org/10.1016/j.adhoc.2022.103037]
[17]
Murugeshwari B, Sabatini SA, Jose L, Padmapriya S. Effective Data Aggregation in WSN for Enhanced Security and Data Privacy. 230414654. 2023
[18]
Zhao M, Ma M, Yang Y. Efcient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks. IEEE Trans Comput 2011; 60(3): 400-17.
[http://dx.doi.org/10.1109/TC.2010.140]
[19]
Sarker A, Canto AC, Mozaffari Kermani M, Azarderakhsh R. Error Detection Architectures for Hardware/Software Co-Design Approaches of Number-Theoretic Transform. IEEE Trans Comput Aided Des Integrated Circ Syst 2023; 42(7): 2418-22.
[http://dx.doi.org/10.1109/TCAD.2022.3218614]
[20]
Kermani M M, Azarderakhsh R, Xie Jiafeng. Error detection reliable architectures of Camellia block cipher applicable to different variants of its substitution boxes. 2016 IEEE Asian Hardware- Oriented Security and Trust (AsianHOST). Yilan, Taiwan, 2016; pp. 1-6.
[http://dx.doi.org/10.1109/AsianHOST.2016.7835560]
[21]
Jea D, Somasundara A, Srivastava M. Multiple controlled mobile elements (data mules) for data collection in sensor networks. Berlin: Springer 2005; pp. 244-57.
[http://dx.doi.org/10.1007/11502593_20]
[22]
Rao J, Biswas S. Joint routing and navigation protocols for data harvesting in sensor networks. Piscataway: IEEE 2008; pp. 143-52.
[http://dx.doi.org/10.1109/MAHSS.2008.4660041]
[23]
Chakrabarti A, Sabharwal A, Aazhang B. Using predictable observer mobility for power efcient design of sensor networks. Berlin: Springer 2003; pp. 129-45.
[24]
Aslanyan H, Leone P, Rolim J. Data propagation with guaranteed delivery for mobile networks. International Symposium on Experimental Algorithms SEA 2010: Experimental Algorithms 2010; 386-97.
[http://dx.doi.org/10.1007/978-3-642-13193-6_33]
[25]
Howard A, Matarić MJ, Sukhatme GS. Mobile sensor network deployment using potential felds: A distributed, scalable solution to the area coverage problem Distributed autonomous robotic systems. Berlin: Springer 2002; 5: pp. 299-308.
[http://dx.doi.org/10.1007/978-4-431-65941-9_30]
[26]
Santini S, Romer K. An adaptive strategy for quality-based data reduction in wireless sensor networks. 2006; 29-36.
[27]
Khedo K, Doomun R, Aucharuz S. Reada: redundancy elimination for accurate data aggregation in wireless sensor networks. Wireless Sensor Network 2010; 2(4): 300-8.
[http://dx.doi.org/10.4236/wsn.2010.24041]
[28]
Hasan MN, Jan SU, Koo I. Wasserstein GAN-based digital twin-inspired model for early drift fault detection in wireless sensor networks. IEEE Sens J 2023; 23(12): 13327-39.
[http://dx.doi.org/10.1109/JSEN.2023.3272908]
[29]
Asha A, Arunachalam R, Poonguzhali I, Urooj S, Alelyani S. Optimized RNN-based performance prediction of IoT and WSN-oriented smart city application using improved honey badger algorithm. Measurement 2023; 210: 112505.
[http://dx.doi.org/10.1016/j.measurement.2023.112505]

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