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Title

Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing

  Jayasudha A. R. 1 ,   Ramya S. 2 ,   Vairaprakash S. 3 ,   N. Kannaiya Raja 4

1  Department of computer applications, Hindusthan college of engineering and technology
    (jayasudhacbe71@gmail.com)

2  Department of ECE, Sri Krishna College of Technology, India
    (ramyaprasadphd@gmail.com)

3  Department of Electronics and Communication Engineering, Ramco Institute of Technology, India
    (vairaprakashklu@gmail.com)

4  Department of Computer Science, Ambo University, Ambo, Ethiopia
    (kannaiya.raja@ambou.edu.et)


Doi   :   https://doi.org/10.54216/IJWAC.080101

Received: September 07, 2023 Revised: December 12, 2023 Accepted: March 01, 2024

Abstract :

Wireless Sensor Networks (WSN) play a crucial role in diverse data gathering applications, but face a significant challenge in the form of limited energy reserves within sensor nodes. Enhancing the network's Quality of Service, particularly its lifetime, is paramount. Prolonging the network's operational span hinges on mitigating energy consumption, with communication accounting for a substantial portion of nodal power usage. By reducing data transmission, not only can energy consumption be curtailed, but also bandwidth requirements and network congestion can be minimized.  In the context of Wireless Sensor Networks, the Distributed Similarity-based Clustering and Compressed Forwarding (DSCCF) approach strives to construct data-similar iso-clusters with minimal communication overhead. This technique involves extracting trend and magnitude components from lengthy data series using an LMS filter, resulting in what is termed "data projection." Data similarity between nodes is assessed by measuring the Euclidean distance between these data projections, thereby facilitating efficient and low-overhead iso-cluster formation. To further economize intra-cluster communication, an adaptive-nLMS-based dual prediction framework is employed. During each data collection round, the cluster head holds instantaneous data for each cluster member, using either prediction or direct data communication. Furthermore, inter-cluster data is reduced via a multi-level lossless compressive forwarding technique. Impressively, this proposed approach has achieved an 80% reduction in data while maintaining optimal data accuracy for the collected information. The transmission of inter-cluster data exclusively occurs through a network backbone comprised solely of cluster heads. Initially, the cluster heads establish this network backbone. Each cluster head dispatches a link request query towards the sink through the backbone, receiving a link reply message containing path length and the weakest link of the path. The cluster head repeats this process for each available path, subsequently selecting the most optimal path based on the acquired information and its reliability in terms of link quality

Keywords :

Wireless Sensor Networks (WSN); Energy Efficiency; Data Clustering; Data Projection; Communication Overhead; Network Lifetime Extension; Machine Learning.

References :

[1]     Ciancio, A, Pattem, S, Ortega, A &Krishnamachari, B efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm’, the 5th international conference on Information processi networks, pp. 309

[2]     Debono, CJ & Borg, NP reduction technique for wireless sensor networks,’ IEEE International Symposium on Signal Processing and Information Technology, pp. 402-406.

[3]     Janarthanan, R.; Maheshwari, R.U.; Shukla, P.K.; Shukla, P.K.; Mirjalili, S.; Kumar, M. Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems. Energies 2021, 14, 6584. https://doi.org/10.3390/en14206584

[4]     Baljon, M.; Sharma, S.K. Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques. Water 2023, 15, 826. https://doi.org/10.3390/w15040826

[5]     Majed Alowaidi, Sunil Kumar Sharma, Abdullah AlEnizi, Shivam Bhardwaj,” Integrating artificial intelligence in cyber security for cyber-physical systems”, ‘Electronic Research Archive’, vol. 31, no. 4, pp-1876-1896.

[6]     Garg, V., Kaur, B., Kumar, T., Alowaidi, M., Sharma, S.K. ‘PIRAP: Chaotic Fuzzy Encryption (CFE) Technique and Greedy Chemical Reaction Optimization (GCRO) Algorithm-Based Secured Mobi-Cloud Framework’ International Journal of Cooperative Information Systems, 2023, 32(1-2), 2250002

[7]     Alzahrani, A., Alshehri, M., AlGhamdi, R., Sharma, S.K. ‘Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning’ Healthcare (Switzerland), 2023, 11(3), 384

[8]     Kaur, S., Kaur, N., Bhatia, K.S., ...Sharma, N.K., Sharma, S.K. ‘Node localization and data aggregation scheme using cuckoo search and neural network’ Expert Systems, 2023, 40(4), e13033

[9]     Jitendra Singh, Maninder Singh Arora, Sunil Sharma, Jang B. Shukla. Modeling the variable transmission rate and various discharges on the spread of Malaria[J]. Electronic Research Archive, 2023, 31(1): 319- 341. doi: 10.3934/era.2023016

[10]    R. AlGhamdi and S. K. Sharma, “IoT-Based Smart Water Management Systems for Residential Buildings in Saudi Arabia,” Processes, vol. 10, no. 11, p. 2462, Nov. 2022, doi: 10.3390/pr10112462. [Online]. Available: http://dx.doi.org/10.3390/pr10112462

[11]   S. K Sharma* , Waseem Ahmad Khan, Cheon-Seoung Ryoo, and Ugur Duran. (2022) "Diverse Properties and Approximate Roots for a Novel Kinds of the (p,q)-Cosine and (p,q)-Sine Geometric

[12]   Polynomials" Mathematics 10, no. 15: 2709. https://doi.org/10.3390/math10152709

[13]    S K Sharma, M. Alwanian, M. Alowaidi, H. Alsagier (2022) “Mobile Healthcare (M-Health) based on Artificial Intelligence in Healthcare 4.0” Expert Systems DOI: https://doi.org/10.1111/exsy.13025

[14]   Alshehri M* (2023) ‘Blockchain-assisted internet of things framework in smart livestock farming’ Internet of Things, Volume 22, 2023, 100739, ISSN 2542-6605, https://doi.org/10.1016/j.iot.2023.100739.

[15]    Alzahrani, A.; Alshehri, M.; AlGhamdi, R.; Sharma, S.K. (2023) ‘Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning’ Healthcare, 11, 384. https://doi.org/10.3390/healthcare11030384

[16]    Padhy S, Alowaidi M, Dash S, Alshehri M, Malla PP, Routray S, Alhumyani H. (2023) “AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain” Processes. 11(3):757. https://doi.org/10.3390/pr11030757

[17]   Alshehri, M (2023) “Blockchain-assisted cyber security in medical things using artificial intelligence” Electronic Research Archive, 31(2): 708-728. doi: 10.3934/era.2023035


Cite this Article as :
Style #
MLA Jayasudha A. R., Ramya S. , Vairaprakash S. , N. Kannaiya Raja. "Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing." International Journal of Wireless and Ad Hoc Communication, Vol. 8, No. 2, 2024 ,PP. 08-22 (Doi   :  https://doi.org/10.54216/IJWAC.080101)
APA Jayasudha A. R., Ramya S. , Vairaprakash S. , N. Kannaiya Raja. (2024). Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing. Journal of International Journal of Wireless and Ad Hoc Communication, 8 ( 2 ), 08-22 (Doi   :  https://doi.org/10.54216/IJWAC.080101)
Chicago Jayasudha A. R., Ramya S. , Vairaprakash S. , N. Kannaiya Raja. "Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing." Journal of International Journal of Wireless and Ad Hoc Communication, 8 no. 2 (2024): 08-22 (Doi   :  https://doi.org/10.54216/IJWAC.080101)
Harvard Jayasudha A. R., Ramya S. , Vairaprakash S. , N. Kannaiya Raja. (2024). Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing. Journal of International Journal of Wireless and Ad Hoc Communication, 8 ( 2 ), 08-22 (Doi   :  https://doi.org/10.54216/IJWAC.080101)
Vancouver Jayasudha A. R., Ramya S. , Vairaprakash S. , N. Kannaiya Raja. Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing. Journal of International Journal of Wireless and Ad Hoc Communication, (2024); 8 ( 2 ): 08-22 (Doi   :  https://doi.org/10.54216/IJWAC.080101)
IEEE Jayasudha A. R., Ramya S., Vairaprakash S., N. Kannaiya Raja, Enhancing Wireless Sensor Network Lifetime through Energy-Efficient Data Clustering and Compressed Forwarding in video Processing, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 8 , No. 2 , (2024) : 08-22 (Doi   :  https://doi.org/10.54216/IJWAC.080101)