Submit Your Paper
  1. Home
  2. Journals
  3. Fusion: Practice and Applications

Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)
Submit Your Paper

Journal Menu

Homepage Journal Overview Editorial Board Submitting Articles Indexing & Abstracting

Journal Volumes

Fusion: Practice and Applications

Volume 15 , Issue 2 , PP: 187-195, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency

Deepak Dasaratha Rao 1 * , Bala Dhandayuthapani V. 2 , Ch. Subbalakshmi 3 , Murlidhar Prasad Singh 4 , Prashant Kumar Shukla 5 , Shraddha V. Pandit 6

  • 1 Indian Institute of Technology, Patna, India - (deepakrao@ieee.org)
  • 2 Department of IT, College of Computing and Information Sciences, University of Technology and Applied Sciences, Shinas campus, Oman - (bala.veerasamy@utas.edu.om)
  • 3 Professor, Department of Computer Science & Engineering, Guru Nanak Instructions Technical Campus (Autonomous), Ibrahimpatnam, Ranga Reddy district, Hyderabad - 501506, Telagana State, India - (subbalakshmichatti@gniindia.org)
  • 4 Assistant Professor, Department of, C.S.& E., B. P. Mandal College of Engineering, Madhepura, Bihar, India - (singhmurlidhar@gmail.com)
  • 5 Associate Professor (Research), Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522302, Andhra Pradesh, India - ( prashantshukla2005@kluniversity.in)
  • 6 Professor, Dept.of Artificial Intelligence and Data Science, PES Modern College of Engineering, Shivajinagar, Pune-411005, India - (shraddha.pandit@moderncoe.edu.in)

    • Doi: https://doi.org/10.54216/FPA.150217

    Received: August 12, 2023 Revised: December 07, 2023 Accepted: April 17, 2024
    Abstract

    Recently, wireless sensor networks on several challenging topics have piqued researchers’ attention. Maximising a network's lifetime requires just the right combination of cluster size and number of nodes. Data transmission from nodes to cluster leaders is energy intensive, even for a modest number of clusters. If there are several clusters, many leaders will be chosen, and many nodes will rely on long-distance transmission to communicate with the home base. Therefore, in order to maximise efficiency, it is necessary to strike a balance between these two factors. WSN's major challenge is improving its energy efficiency. This is because their energy consumption defines their lifespan, and it is difficult, if not impossible, to recharge their batteries. Therefore, it is crucial to develop algorithms that consume as little energy as possible in order to maximise the network's potential. The perfect clusters are essential for the longevity of the network. Therefore, an algorithm called statistical centre energy efficient clustering approach (SEECA) is presented to increase the network's lifetime while decreasing its energy consumption. The experimental findings show that the proposed methodology SCEECA outperforms the LEACH method by a wide margin, with gains of 32% in Residual energy, 16% in Network Lifetime, and 12% in Throughput.

    Keywords :

    Fusion Analysis, WSN , SCEECA , Throughput , LEACH.

    References

    [1]. Carlos-Mancilla, M, López-Mellado, E & Siller, M 2016, ‘Wireless Sensor Networks Formation: Approaches and Techniques’, Journal of Sensors, vol. 2016, pp. 1–18.

    [2]. Channamma Patil & Ishwar Baidari, 2019, ‘Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth’, Data Science and Engineering, vol. 4, pp. 132-140.

    [3]. Chatterjee, P & Das, N 2014, ‘Coverage constrained non-uniform node deployment in wireless sensor networks for load balancing’, in Proceedings of the 1st International Conference on Applications and Innovations in Mobile Computing (AIMoC ’14), pp. 126–132.  

    [4]. Ding, Y, Hu, Y, Hao, K & Cheng, L 2015, ‘MPSICA: An intelligent routing recovery scheme for heterogeneous wireless sensor networks’, Information Sciences, vol. 308, pp. 49–60.

    [5]. Dongyao Jia, Huaihua Zhu, Shengxiong Zou & Po Hu 2016, ‘Dynamic cluster head selection method for Wireless Sensor Networks’, IEEE Sensors Journal, vol. 16, no. 8.

    [6]. Jain, B, Brar, G & Malhotra, J 2018, ‘EKMT-k-means clustering algorithmic solution for low energy consumption for wireless sensor networks based on minimum mean distance from base station’, Networking Communication and Data Knowledge Engineering, Springer, pp. 113–123. 44.

    [7]. Jan, B, Farman, H, Javed, H, Montrucchio, B, Khan, M & Ali, S 2017, ‘Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey’, Wireless Communications and Mobile Computing, vol. 2017, pp. 1–14.

    [8]. Jia, D, Zhu, H, Zou, S & Hu, P 2016, ‘Dynamic cluster head selection method for wireless sensor network’, IEEE Sensors Journal, vol. 16, issue 8, pp. 2746–2754.

    [9]. Kingrani, Suneel Kumar, Levene, Mark & Zhang Dell, 2018, ‘Estimating the number of clusters using diversity’, Artificial Intelligence Research, vol. 7, issue 1, pp. 15-22.

    [10]. Logambigai, R & Kannan, A 2016, ‘Fuzzy logic based unequal clustering for wireless sensor networks’, Wireless Networks, vol. 22, pp. 945–957.

    [11]. Smys S, Abul Bashar, Wang Haoxiang (2021), “Taxonomy Classification and Comparison of Routing Protocol Based on Energy Efficient Rate”, Journal of ISMAC, Vol.03, issue no.2, pp: 96-110.

    [12]. Weidang Lu , Xiaohan Xu, Guoxing Huang , Bo Li , Yuan Wu , Nan Zhao and F. Richard Yu , 2021, “Energy Efficiency Optimization in SWIPT Enabled WSNs for Smart Agriculture”, Ieee Transactions On Industrial Informatics, Vol. 17, issue no. 6, pp. 4335 – 4344.

    [13]. Khan, A.; Khan, F. A Cost-Efficient Radiation Monitoring System for Nuclear Sites: Designing and Implementation. Intell. Autom. Soft Comput. 2022, 32, 1357–1367.

    [14]. 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. 

    [15]. Deng, D.; Yuan, H. Distributed wireless sensor network system for electric field measurement. In Proceedings of the 2016 IEEE AUTOTESTCON, Anaheim, CA, USA, 12–15 September 2016.

    [16]. Cui, Y.; Song, X.; Wang, C.; Wu, G.; Zhao, L. Ground-level DC electric field sensor for overhead HVDC/HVAC transmission lines in hybrid corridors. IET Gener. Transm. Distrib. 2020, 14, 4173–4178.

    [17]. Rasool, F.; Drieberg, M.; Badruddin, N.; Sebastian, P.; Qian, C.T.J. Electrical battery modeling for applications in wireless sensor networks and Internet of Things. Bull. Electr. Eng. Inform. 2021, 10, 1793–1802.

    [18]. He, J.; Duan, X.; Cheng, P.; Shi, L.; Cai, L. Accurate clock synchronization in wireless sensor networks with bounded noise. Automatica 2017, 81, 350–358.

    [18]. Pal, A. Coverage sensitivity analysis of a wireless sensor network with different sensing range models considering boundary effects. Mater. Today Proc. 2022, 49, 3640–3645.

    [19]. Jadaa, K.J.; Kamarudin, L.M.; Hussein, W.N.; Zakaria, A.; Zakaria, S.M.M.S. Multi-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities. J. Phys. Conf. Ser. 2021, 1755, 012043.

    [20]. Alobaidy, H.A.; Singh, M.J.; Behjati, M.; Nordin, R.; Abdullah, N.F. Wireless Transmissions, Propagation and Channel Modeling for IoT Technologies: Applications and Challenges. IEEE Access 2022, 10, 24095–24131.

    [21]. Li, Q.; Liu, N. Monitoring area coverage optimization algorithm based on nodes perceptual mathematical model in wireless sensor networks. Comput. Commun. 2020, 155, 227–234.

    [22]. Das, S.K.; Kapelko, R. On the range assignment in wireless sensor networks for minimizing the coverage-connectivity cost. ACM Trans. Sens. Netw. (TOSN) 2021, 17, 1–48.

    [23]. Choi, H.H.; Lee, K. Cooperative Wireless Power Transfer for Lifetime Maximization in Wireless Multi-hop Networks. IEEE Trans. Veh. Technol. 2021, 70, 3984–3989.

    [24]. Mazloomi, N.; Gholipour, M.; Zaretalab, A. Efficient configuration for multi-objective QoS optimization in wireless sensor network. Ad Hoc Netw. 2022, 125, 102730.

     

     

     

     

    Cite This Article As :
    Dasaratha, Deepak. , Dhandayuthapani, Bala. , Subbalakshmi, Ch.. , Prasad, Murlidhar. , Kumar, Prashant. , V., Shraddha. An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice and Applications, vol. , no. , 2024, pp. 187-195. DOI: https://doi.org/10.54216/FPA.150217
    Dasaratha, D. Dhandayuthapani, B. Subbalakshmi, C. Prasad, M. Kumar, P. V., S. (2024). An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice and Applications, (), 187-195. DOI: https://doi.org/10.54216/FPA.150217
    Dasaratha, Deepak. Dhandayuthapani, Bala. Subbalakshmi, Ch.. Prasad, Murlidhar. Kumar, Prashant. V., Shraddha. An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice and Applications , no. (2024): 187-195. DOI: https://doi.org/10.54216/FPA.150217
    Dasaratha, D. , Dhandayuthapani, B. , Subbalakshmi, C. , Prasad, M. , Kumar, P. , V., S. (2024) . An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice and Applications , () , 187-195 . DOI: https://doi.org/10.54216/FPA.150217
    Dasaratha D. , Dhandayuthapani B. , Subbalakshmi C. , Prasad M. , Kumar P. , V. S. [2024]. An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency. Fusion: Practice and Applications. (): 187-195. DOI: https://doi.org/10.54216/FPA.150217
    Dasaratha, D. Dhandayuthapani, B. Subbalakshmi, C. Prasad, M. Kumar, P. V., S. "An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency," Fusion: Practice and Applications, vol. , no. , pp. 187-195, 2024. DOI: https://doi.org/10.54216/FPA.150217

    Article Statistics

    View
    55496
    Download
    628

    Download