Optimized Secure Clustering and Energy Efficient System for IIoT Data in Cloud Environment

Authors

  • Primya T. KPR Institute of Engineering and Technology image/svg+xml
  • Ajit Kumar Singh Yadav North Eastern Regional Institute of Science and Technology image/svg+xml
  • Sreeraman Y. Apollo University
  • Vivekanandan T. Apollo University

DOI:

https://doi.org/10.4108/ew.6032

Keywords:

Optimized, Secure, Clustering, Efficient System Industrial Internet of Things, Cloud Environment

Abstract

Secure and powerful Industrial Internet of Things (IIoT) statistics dealing with on cloud infrastructures is vital as commercial gadgets grow to be greater networked. IIoT systems accommodated in the cloud should shield personal statistics, make sure uninterrupted operations, use information insights to make decisions, and reduce electricity consumption. Several industries have been transformed by way of IIoT programs, which depend closely on cloud infrastructure for statistics processing and garage. Energy performance and the safety of sensitive business statistics are predominant issues. A few of the problems that need addressing are secure data transmission, invasion of privacy, and data breaches. It is not a simple task to optimize power efficiency without compromising actual-time records processing. The Optimized Dynamic Clustering and Energy-Efficient System (ODC-EES) is a unique approach for cloud-based IIoT information control and employer that uses stepped forward adaptive clustering strategies. Strengthening facts security whilst streamlining strength use, the recommended method blends present day encryption protocols, access controls, and power-aware useful resource allocation. This method promotes sustainable electricity practices even as making sure adaptability to the ever-converting IIoT information. Manufacturing, strength, logistics, and healthcare are the various few of the numerous commercial sectors that might advantage from ODC-EES. The counselled approach seeks to enhance the dependability and performance of manufacturing strategies through making IIoT information more stable and the use of less strength. For the motive to demonstrate the system's efficacy in enhancing statistics protection, optimizing energy usage, and making sure the fresh operation of IIoT programs in cloud environments, these simulations will evaluate its overall performance below numerous situations.

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Author Biographies

Ajit Kumar Singh Yadav, North Eastern Regional Institute of Science and Technology

Assistant professor Department of Computer Science & Engineering, North Eastern Regional Institute of Science and Technology, Itanagar, India.

Sreeraman Y., Apollo University

Associate Professor, Department of Computer Science and Engineering, School of Technology, The Apollo University, Chittoor, Andhra Pradesh, India.

Vivekanandan T., Apollo University

Associate Professor Department of Computer Science and Engineering, School of Technology, The Apollo University, Chittoor, Andhra Pradesh, India.

References

[1] Zhao Y, Akter F. Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks. Security and Communication Networks. 2022;2022(1):3872214.

[2] Li Q, Yue Y, Wang Z. Deep Robust Cramer Shoup delay optimised fully homomorphic for IIOT secured transmission in cloud computing. Computer Communications. 2020 Sep 1;161:10-8.

[3] Rami Reddy, M., Ravi Chandra, M. L., Venkatramana, P., & Dilli, R. (2023). Energy-efficient cluster head selection in wireless sensor networks using an improved grey wolf optimisation algorithm. Computers, 12(2), 35.

[4] Mao W, Zhao Z, Chang Z, Min G, Gao W. Energy-efficient industrial Internet of Things: Overview and open issues. IEEE transactions on industrial informatics. 2021 Mar 18;17(11):7225-37.

[5] Wang B, Liao X. A trusted routing mechanism for multi-attribute chain energy optimisation for Industrial Internet of Things. Neural Computing and Applications. 2023 Oct;35(29):21349-59.

[6] Qi S, Lu Y, Wei W, Chen X. Efficient data access control with fine-grained data protection in cloud-assisted IIoT. IEEE Internet of Things Journal. 2020 Sep 1;8(4):2886-99.

[7] Ahmed A, Abdullah S, Bukhsh M, Ahmad I, Mushtaq Z. An energy-efficient data aggregation mechanism for IoT secured by blockchain. IEEE Access. 2022 Jan 25;10:11404-19.

[8] Bhandari KS, Cho GH. An energy efficient routing approach for cloud-assisted green industrial IoT networks. Sustainability. 2020 Sep 8;12(18):7358.

[9] Humayun M, Jhanjhi NZ, Alruwaili M, Amalathas SS, Balasubramanian V, Selvaraj B. Privacy protection and energy optimisation for 5G-aided industrial Internet of Things. Ieee Access. 2020 Oct 6;8:183665-77.

[10] Zhu S, Ota K, Dong M. Green AI for IIoT: Energy efficient intelligent edge computing for industrial internet of things. IEEE Transactions on Green Communications and Networking. 2021 Aug 20;6(1):79-88.

[11] Hu N, Tian Z, Du X, Guizani N, Zhu Z. Deep-green: a dispersed energy-efficiency computing paradigm for green industrial IoT. IEEE Transactions on Green Communications and Networking. 2021 Mar 8;5(2):750-64.

[12] Liu M, Yu FR, Teng Y, Leung VC, Song M. Performance optimisation for blockchain-enabled industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics. 2019 Feb 6;15(6):3559-70.

[13] Vijayalakshmi V, Saravanan M. Reinforcement learning-based multi-objective energy-efficient task scheduling in fog-cloud industrial IoT-based systems. Soft Computing. 2023 Dec;27(23):17473-91

[14] Jian X, Wu L, Yu K, Aloqaily M, Ben-Othman J. Energy-efficient user association with load-balancing for cooperative IIoT network within B5G era. Journal of Network and Computer Applications. 2021 Sep 1;189:103110.

[15] Khan F, Jan MA, ur Rehman A, Mastorakis S, Alazab M, Watters P. A secured and intelligent communication scheme for IIoT-enabled pervasive edge computing. IEEE Transactions on Industrial Informatics. 2020 Nov 13;17(7):5128-37.

[16] Njah Y, Cheriet M. Parallel route optimisation and service assurance in energy-efficient software-defined industrial IoT networks. IEEE Access. 2021 Feb 3;9:24682-96.

[17] Hu C, Liu J, Xia H, Deng S, Yu J. A Lightweight Mutual Privacy Preserving $ k $-means Clustering in Industrial IoT. IEEE Transactions on Network Science and Engineering. 2023 Nov 30.

[18] Jiang D, Wang Y, Lv Z, Wang W, Wang H. An energy-efficient networking approach in cloud services for IIoT networks. IEEE Journal on Selected Areas in Communications. 2020 Mar 16;38(5):928-41.

[19] Mukherjee A, Goswami P, Yang L, Sah Tyagi SK, Samal UC, Mohapatra SK. Deep neural network-based clustering technique for secure IIoT. Neural Computing and Applications. 2020 Oct;32:16109-17.

[20] Mukherjee A, Goswami P, Yang L, Sah Tyagi SK, Samal UC, Mohapatra SK. Deep neural network-based clustering technique for secure IIoT. Neural Computing and Applications. 2020 Oct;32:16109-17.

[21] Sharma, S. and Saini, H., 2020. Fog assisted task allocation and secure deduplication using 2FBO2 and MoWo in cluster-based industrial IoT (IIoT). Computer Communications, 152, pp.187-199.

[22] Mansour RF. Blockchain assisted clustering with intrusion detection system for industrial internet of things environment. Expert Systems with Applications. 2022 Nov 30;207:117995.

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Published

01-08-2024

How to Cite

1.
T. P, Yadav AKS, Y. S, T. V. Optimized Secure Clustering and Energy Efficient System for IIoT Data in Cloud Environment. EAI Endorsed Trans Energy Web [Internet]. 2024 Aug. 1 [cited 2024 Sep. 1];11. Available from: https://publications.eai.eu/index.php/ew/article/view/6032