An AdaBoost-modified classifier using stochastic diffusion search model for data optimization in Internet of Things
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.
KeywordsInternet of Things (IoT) Genetic algorithm (GA) Stochastic diffusion search (SDS)
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Conflict of interest
Author A declares that he has no conflict of interest. Author B declares that he has no conflict of interest.
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 was obtained from all individual participants included in the study.
- Al-Rifaie MM, Bishop JM (2013a) Stochastic diffusion search review. Paladyn J Behav Robot 4(3):155–173Google Scholar
- Al-Rifaie MM, Bishop JM (2013b) Stochastic diffusion search review. Paladyn J Behav Robot 4(3):155–173Google Scholar
- 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
- 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
- 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
- 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
- Mandapati S (2018) A greedy stochastic diffusion search based fuzzy scheduling in cloud. J Artif Intell Res Adv 5(2):58–68Google Scholar
- 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
- 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
- Sethi P, Sarangi SR (2017) Internet of Things: architectures, protocols, and applications. J Electr Comput Eng 2017Google Scholar
- 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
- 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
- Tolba M, Moustafa M (2016) GAdaBoost: Accelerating Adaboost feature selection with genetic algorithms. arXiv preprint arXiv:1609.06260
- 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