Arpita Biswas,Abhishek Majumdar,K. L. Baishnab,



Cloud Computing, Clustering, MCDM, IoT,


IoT is a globally accepted smart technology that has the ability to connect each and almost every physical devices through the network. It acts as a bridge between cloud environment and physical environment. It is mainly used to connect the hardware devices like sensors, actuators, storage, hardware, and software to acquire or exchange data. These devices collect the information from the physical world and convert this into useful information that can help in decision making. Since IoT connects everything to the network, so it may face the problem of a large amount of energy loss. In this respect, this paper mainly focuses on reducing the energy loss problem and designing of an energy efficient data transfer scenario between cloud and IoT devices. For this reason, a Complex Proportional Assessment (COPRAS) based clustering approach has been proposed in this work to select the cluster premier effectively and form the set of best clusters for maximizing the network lifetime. The proposed work deals with data transmission model between IoT and cloud that confirms the improvement in energy efficiency, network lifetime, and latency. Furthermore, the sensitivity analysis has also been carried out and satisfactory results has been obtained.


I. A. Majumdar, T. Debnath, S. K. Sood, K. L. Baishnab, “Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment”, Journal of medical systems, Springer, vol. 42, no.10, pp.187, 2018.
II. A. Majumdar, A., Biswas, K. L. Baishnab, S. K. Sood, “DNA Based Cloud Storage Security Framework Using Fuzzy Decision Making Technique”, KSII Transactions on Internet & Information Systems, vol.13, no.7, pp. 3794-3820, 2019.
III. A. Majumdar, N. M. Laskar, A. Biswas, S. K. Sood, K. L. Baishnab, “Energy efficient e-healthcare framework using HWPSO-based clustering approach”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 36, no. 5, pp. 3957-3969, 2019.
IV. A. Biswas, A. Majumdar, S. Nath, A. Dutta, K. L. Baishnab, “LRBC: a lightweight block cipher design for resource constrained IoT devices”, Journal of Ambient Intelligence and Humanized Computing, Springer pp.1-15, 2020.
V. A. V. Dhumane and R. S. Prasad, “Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT”, Wireless Personal Communications, Springer, vol. 102, no. 1, pp. 411-36, 2018.
VI. A. V. Dhumane, R. S. Prasad, and J. R. Prasad, “An optimal routing algorithm for internet of thing enabling technologies”, International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 4, no. 3, pp. 1-16, 2017.
VII. A. Orsino, G. Araniti, L. Militano, J. Alonso-Zarate, A. Molinaro, A. Iera,. “Energy efficient IoT data collection in smart cities exploiting D2D communications”, Sensors, vol. 16, no. 6, p.836, 2016.
VIII. D. Wei, S. Kaplan, H.A. Chan, “Energy efficient clustering algorithms for wireless sensor networks”, In Communications Workshops, 2008. ICC Workshops’ 08. IEEE International Conference on, pp. 236-240, 2008.
IX. G. L. da Silva Fré, J. de Carvalho Silva, F.A. Reis, and L.D.P. Mendes, “Particle Swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the internet of things,” in International Workshop on Telecommunications (IWT), pp. 1–7, 2015.
X. I. Yaqoob, E. Ahmed, I.A.T. Hashem, A.I.A. Ahmed, A. Gani, M. Imran, M. Guizani, “Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges”, IEEE wireless communications, vol. 24, no. 3, pp.10-16, 2017.
XI. J. H. Kwon, M. Cha, S. B. Lee, and E. J. Kim, “Variable-categorized clustering algorithm using fuzzy logic for Internet of things local networks”, Multimedia Tools and Applications, Springer, vol. 78, no.3, pp. 2963-82, 2019.
XII. J. A. Martins, A. Mazayev, N. Correia, G. Schütz, and A. Barradas, “GACN: Self-clustering genetic algorithm for constrained networks”, IEEE Communications Letters, vol. 21, no. 3, pp. 628-31, 2017.
XIII. J.M. Liang, J.J. Chen, H.H. Cheng, Y.C. Tseng, “An energy-efficient sleep scheduling with qos consideration in 3gpp lte-advanced networks for internet of things,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 1, pp.13-22, 2013.
XIV. J. Tang, Z. Zhou, J. Niu, Q. Wang, “An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things”, Journal of Network and Computer Applications, vol. 40, pp.1-11, 2014.
XV. L. Song, K. K. Chai, Y. Chen, J. Loo, S. Jimaa, and J. Schormans, “QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems”, in Wireless Communications and Networking Conference, IEEE, April 2016, pp. 1-6.
XVI. L. Song, K.K. Chai, Y. Chen, J. Schormans, J. Loo, A. Vinel, “QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems”, IEEE Systems Journal, vol. 11, no. 3, pp.1447-1455, 2017.
XVII. M. P. K. Reddy and M. R. Babu, “Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things”, Cluster Computing, Springer, pp. 1-12, 2018.
XVIII. M. P. K. Reddy and M. R. Babu, “Energy Efficient Cluster Head Selection for Internet of Things”, New Review of Information Networking, Taylor & Francis, vol. 22, no. 1, pp. 54-70, 2017.
XIX. M. P. K. Reddy and M. R. Babu, “An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things”, International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 337-46, 2017.
XX. N. T. Van, T. T. Huynh, and B. An, “An energy efficient protocol based on fuzzy logic to extend network lifetime and increase transmission efficiency in wireless sensor networks”, Journal of Intelligent & Fuzzy Systems, IOS Press, vol. 35, no. 6, pp. 5845-5852, 2018.
XXI. N. Kaur, and S.K. Sood, “An Energy-Efficient Architecture for the Internet of Things (IoT)”, IEEE Systems Journal, vol.11, no.2, pp.796-805, 2017.
XXII. Ö.U. Akgül, B. Canberk, “Self-Organized Things (SoT): An energy efficient next generation network management,” Computer Communications, vol. 74, pp.52-62, 2016.
XXIII. S. K. Singh, M.P. Singh, D.K. Singh, “Energy-efficient homogeneous clustering algorithm for wireless sensor network”, International Journal of Wireless & Mobile Networks (IJWMN), vol. 2, no. 3, pp.49-61, 2010.
XXIV. S. Rani, R. Talwar, J. Malhotra, S.H. Ahmed, M. Sarkar, H. Song, “A novel scheme for an energy efficient Internet of Things based on wireless sensor networks”, Sensors, vol. 15, no. 11, pp.28603-28626, 2015.
XXV. S. D. Muruganathan, D. C. Ma, R. I. Bhasin, A. O. Fapojuwo, “A centralized energy-efficient routing protocol for wireless sensor networks”, IEEE Communications Magazine, vol. 43, no. 3, pp. S8-13, 2005.
XXVI. T. Ayesha, S. Sadaf, D. Sinha, and A. K. Das. “Secure Anti-Void Energy-Efficient Routing (SAVEER) Protocol for WSN-Based IoT Network”, In Advances in Computational Intelligence, pp. 129-142. Springer, Singapore, 2020.
XXVII. Z. Zhou, J. Tang, L.J. Zhang, K. Ning, Q. Wang, “EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things”, Personal and Ubiquitous computing, vol.18, no.4, pp.951-966, 2014.
XXVIII. A. Majumdar, T. Debnath, K. L. Baishnab, S. K. Sood, “An Energy Efficient e-Healthcare Framework Supported by HEO-µGA (Hybrid Extremal Optimization Tuned Micro-GeneticAlgorithm)”, Information System Frontiers, Springer, 2020, DoI:
XXIX. A. Majumdar, M. Sharma, “Enhanced information security using DNA cryptographic approach. International Journal of Innovative Technology and Exploring Engineering”, vol.4, no.2, pp. 72-76, 2020.

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