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RETRACTED ARTICLE: Machine learning based multi scale parallel K-means++ clustering for cloud assisted internet of things

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This article was retracted on 08 October 2022

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Abstract

Cloud assisted Internet of Things (CIoT) is the technology initiated towards the deployment of virtualization in wireless sensor networks (WSN). In the Recent times, parallel clustering routing schemes is the significant area of research to optimize energy and scalability problem in WSN on CIoT. In clustering schemes, Data collection, Aggregation and communication is the important activity which has been optimized through various conventional approaches which are not accounted to generate balanced clusters with optimum energy consumption and closer to corresponding cluster heads. In this research the novel Multi Scale Parallel K-means++ (MSPK++) clustering algorithm with balanced clustering has been proposed and improved further by applying machine learning techniques suitable for WSN on CIoT environment. The algorithm convergence has been proved globally and locally whereas the simulations are experimentally validated for the proposed algorithm in comparison with state of art algorithms in an acceptable complexity.

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References

  1. Fourati L, El-Kaffel S, Mnaouer AB, Touati F (2018, April) Investigations on recent power-aware opportunistic protocols in WSN. In: 2018 wireless days (WD). IEEE, pp 187–189

  2. Al-Kiyumi RM, Foh CH, Vural S, Chatzimisios P, Tafazolli R (2018) Fuzzy logic-based routing algorithm for lifetime enhancement in heterogeneous wireless sensor networks. IEEE Transactions on Green Communications and Networking 2(2):517–532

    Article  Google Scholar 

  3. Rodriguez-Zurrunero R, Utrilla R, Rozas A, Araujo A (2019) Process management in IoT operating systems: cross-influence between processing and communication tasks in end-devices. Sensors 19(4):805

    Article  Google Scholar 

  4. Chang C, Srirama SN, Buyya R (2019) Internet of things (IoT) and new computing paradigms. In: Fog and edge computing: principles and paradigms, pp 1–23

    Google Scholar 

  5. Rakhi, & Pahuja, G. L. (2019) A reliable solution of path optimisation in LEACH protocol by implementing trust-based neural network. International Journal of Communication Networks and Distributed Systems 22(1):55–73

    Article  Google Scholar 

  6. Baskar S, Dhulipala VR (2018) M-CRAFT-modified multiplier algorithm to reduce overhead in fault tolerance algorithm in wireless sensor networks. J Comput Theor Nanosci 15(4):1395–1401

    Article  Google Scholar 

  7. Karaboga D, Kaya E (2018) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev:1–31

  8. Baskar S, Dhulipala VR (2018) Biomedical rehabilitation: data error detection and correction using two dimensional linear feedback shift register based cyclic redundancy check. Journal of Medical Imaging and Health Informatics 8(4):805–808

    Article  Google Scholar 

  9. Huda S, Miah S, Yearwood J, Alyahya S, Al-Dossari H, Doss R (2018) A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network. Journal of Parallel and Distributed Computing 120:23–31

    Article  Google Scholar 

  10. Liu D, Yang C, Li S, Chen X, Ren J, Liu R et al (2018) FitCNN: a cloud-assisted and low-cost framework for updating CNNs on IoT devices. Futur Gener Comput Syst

  11. Peng H, Si S, Awad MK, Zhang N, Zhao H, Shen XS (Dec. 2016) Toward energy-efficient and robust large-scale WSNs: a scale-free network approach. IEEE J Sel Areas Commun 34(12):4035–4047

    Article  Google Scholar 

  12. Yuan X, Elhoseny M, El-Minir HK, Riad AM (Jan 2017) A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. J Netw Syst Manage 25(1):21–46

    Article  Google Scholar 

  13. Gupta P, Sharma AK (2019) Clustering-based optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system. Soft Comput 23(2):507–526

    Article  Google Scholar 

  14. Mohamed Shakeel P, Tobely TEE, Al-Feel H, Manogaran G, Baskar S (2019, Page(s): 1) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access 7:5577–5588

    Article  Google Scholar 

  15. Jamil F, Iqbal M, Amin R, Kim D (2019) Adaptive thermal-aware routing protocol for wireless body area network. Electronics 8(1):47

    Article  Google Scholar 

  16. Li J, Luo H, Zhang S, Li H, Yan F (2019) Design and implementation of efficient control for incoming inter-domain traffic with information-centric networking. J Netw Comput Appl 133:109–125

    Article  Google Scholar 

  17. Ahad, S. A., Padmanabhan, A., Gangoli, V., & Kumar, P. (2019). US Patent Application No 15/664,224

  18. Stamou A, Kakkavas G, Tsitseklis K, Karyotis V, Papavassiliou S (2019) Autonomic network management and cross-layer optimization in software defined radio environments. Future Internet 11(2):37

    Article  Google Scholar 

  19. Zam A, Khayyambashi MR, Bohlooli A (2019) Energy-aware strategy for collaborative target-detection in wireless multimedia sensor network. Multimed Tools Appl:1–21

  20. Preeth SKSL, Dhanalakshmi R, Kumar R, Shakeel PM (2018) An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. J Ambient Intell Humaniz Comput:1–13. https://doi.org/10.1007/s12652-018-1154-z

  21. Son J, Buyya R (2018) A taxonomy of software-defined networking (SDN)-enabled cloud computing. ACM Computing Surveys (CSUR) 51(3):59

    Google Scholar 

  22. Baskar S, Periyanayagi S, Shakeel PM, Dhulipala VS (2019) An energy persistent range-dependent regulated transmission communication model for vehicular network applications. Comput Netw 152:144–153

    Article  Google Scholar 

  23. Batra PK, Kant K (2018) An energy-aware clustering algorithm for wireless sensor networks: GA-based approach. International Journal of Autonomous and Adaptive Communications Systems 11(3):275–292

    Article  Google Scholar 

  24. Asadi M, Mazinani SM (2019) Presenting a new clustering algorithm by combining intelligent bat and chaotic map algorithms to improve energy consumption in wireless sensor network. In: Fundamental research in electrical engineering. Springer, Singapore, pp 913–929

    Chapter  Google Scholar 

  25. Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM (2018 Oct 1) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):186. https://doi.org/10.1007/s10916-018-1045-z

    Article  Google Scholar 

  26. Chang Y, Yuan X, Li B, Niyato D, Al-Dhahir N (2019) Machine-learning-based parallel genetic algorithms for multi-objective optimization in ultra-reliable low-latency WSNs. IEEE Access 7:4913–4926

    Article  Google Scholar 

  27. Chang Y, Tang H, Cheng Y, Zhao Q, Li B, Yuan B (Jul. 2017) Dynamic hierarchical energy-efficient method based on combinatorial optimization for wireless sensor networks. Sensors 17(7):1665–1679

    Article  Google Scholar 

  28. Almajidi AM, Pawar VP, Alammari A (2019) K-means-based method for clustering and validating wireless sensor network. In: International conference on innovative computing and communications. Springer, Singapore, pp 251–258

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KGiSL Institute of Technology, Coimbatore, India

    S. K. Mydhili

  2. Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India

    S. Periyanayagi

  3. Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India

    S. Baskar

  4. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    P. Mohamed Shakeel

  5. Junior Technical Assistant, Department of Scientific and Industrial Research, New Delhi, India

    P. R. Hariharan

Authors
  1. S. K. Mydhili
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  2. S. Periyanayagi
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  3. S. Baskar
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  4. P. Mohamed Shakeel
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  5. P. R. Hariharan
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Correspondence to S. Baskar.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12083-022-01395-4

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Mydhili, S.K., Periyanayagi, S., Baskar, S. et al. RETRACTED ARTICLE: Machine learning based multi scale parallel K-means++ clustering for cloud assisted internet of things. Peer-to-Peer Netw. Appl. 13, 2023–2035 (2020). https://doi.org/10.1007/s12083-019-00800-9

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  • DOI: https://doi.org/10.1007/s12083-019-00800-9

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