Open Access

ARTICLE

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

by S. Siamala Devi¹, K. Venkatachalam², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

1 Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, 641042, India
2 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Králové, 50003, Czech Republic
3 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea
4 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
5 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

* Corresponding Author: Yunyoung Nam. Email:

Computer Systems Science and Engineering 2023, 44(3), 1869-1880. https://doi.org/10.32604/csse.2023.025505

Received 26 November 2021; Accepted 11 January 2022; Issue published 01 August 2022

Abstract

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.

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Keywords

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