An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things

  • E. SuganyaEmail author
  • C. Rajan
Methodologies and Application


The Internet of Things (IoT) depicts the network that contains the objects or the “things” that have been embedded along with the network connectivity, the sensors, electronics or the software that enables the objects to collect and exchange data. Wireless sensor networks (WSNs) connect different sensors/things to the Internet by means of a gateway which interfaces the concept of the WSN to the Internet. They have a certain trait that collects all sensed data and duly forwards it to a gateway using a one-way protocol. Huge amount of either unstructured or semi-structured data collected by the WSN is transmitted to IoT for processing. To improve the efficacy of the storing and processing of data, it is required to classify the data. Genetic algorithm is used to find optimal solutions in IoT. Stochastic diffusion search is a heuristic algorithm which has a robust mathematical model and is distributed. This work proposed a Stochastic AdaBoost algorithm for efficient classification of data obtained from WSN and IoT network.


Internet of Things (IoT) Genetic algorithm (GA) Stochastic diffusion search (SDS) 


Compliance with ethical standards

Conflict of interest

Author A declares that he has no conflict of interest. Author B declares that he has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protocols, and applications. Commun Surv Tutor IEEE 17(4):2347–2376CrossRefGoogle Scholar
  2. Al-Rifaie MM, Bishop JM (2013a) Stochastic diffusion search review. Paladyn J Behav Robot 4(3):155–173Google Scholar
  3. Al-Rifaie MM, Bishop JM (2013b) Stochastic diffusion search review. Paladyn J Behav Robot 4(3):155–173Google Scholar
  4. Al-Rifaie MM, Bishop MJ, Blackwell T (2011) An investigation into the merger of stochastic diffusion search and particle swarm optimisation. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation. ACM, pp 37–44Google Scholar
  5. Al-Rifaie MM, Joyce D, Shergill S, Bishop M (2015) Investigating stochastic diffusion search in data clustering. In: 2015 SAI intelligent systems conference (IntelliSys). IEEE, pp 187–194Google Scholar
  6. Bhatnagar A, Shukla S, Majumdar N (2019) Machine learning techniques to reduce error in the Internet of Things. In: 2019 9th international conference on cloud computing, data science & engineering (Confluence). IEEE, pp 403–408Google Scholar
  7. Chen J, Hu K, Wang Q, Sun Y, Shi Z, He S (2017) Narrowband internet of things: implementations and applications. IEEE Internet of Things J 4(6):2309–2314CrossRefGoogle Scholar
  8. Darabian H, Dehghantanha A, Hashemi S, Homayoun S, Choo KKR (2019) An opcode‐based technique for polymorphic Internet of Things malware detection. Concurr Comput Pract Exp. CrossRefGoogle Scholar
  9. Khalil N, Abid MR, Benhaddou R, Gerndt M (2014) “Wireless sensor network for Internet of Things” In: IEEE ninth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), pp 1–6Google Scholar
  10. Kaur P, Kumar R, Kumar M (2019) A healthcare monitoring system using random forest and internet of things (IoT). Multimed Tools Appl. CrossRefGoogle Scholar
  11. Lee I, Lee K (2015) The Internet of Things (IoT): applications, investments, and challenges for enterprises. Bus Horiz 58(4):431–440CrossRefGoogle Scholar
  12. Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP (2018) Machine learning for Internet of Things data analysis: a survey. Digit Commun Netw 4(3):161–175CrossRefGoogle Scholar
  13. Mandapati S (2018) A greedy stochastic diffusion search based fuzzy scheduling in cloud. J Artif Intell Res Adv 5(2):58–68Google Scholar
  14. Prakash R, Ganesh AB (2019) Internet of Things (IoT) enabled wireless sensor network for physiological data acquisition. In: International conference on intelligent computing and applications. Springer, Singapore, pp 163–170Google Scholar
  15. Sandhu SK, Kumar A (2017) Hybrid meta-heuristics based scheduling technique for cloud computing environment. Int J Adv Res Comput Sci 8(5):1457–1465Google Scholar
  16. Sethi P, Sarangi SR (2017) Internet of Things: architectures, protocols, and applications. J Electr Comput Eng 2017Google Scholar
  17. Shaik AB, Srinivasan S (2019) A brief survey on random forest ensembles in classification model. In: International conference on innovative computing and communications. Springer, Singapore, pp 253–260Google Scholar
  18. Tiwary A, Mahato M, Chidar A, Chandrol MK, Shrivastava M, Tripathi M (2018) Internet of Things (IoT): research, architectures and applications. Int J Future Revolut Comput Sci Commun Eng 4(3):23–27Google Scholar
  19. Tolba M, Moustafa M (2016) GAdaBoost: Accelerating Adaboost feature selection with genetic algorithms. arXiv preprint arXiv:1609.06260
  20. Yalabik I, Fatos TV (2007) A pattern classification approach for boosting with genetic algorithms. In: 22nd international symposium on computer and information sciences, 2007. iscis 2007. IEEE, pp 1–6Google Scholar
  21. Zantalis F, Koulouras G, Karabetsos S, Kandris D (2019) A review of machine learning and IoT in smart transportation. Future Internet 11(4):94CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Anna UniversityChennaiIndia
  2. 2.Department of ITK S Rangasamy College of TechnologyTiruchengode, NamakkalIndia

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