Research Article | Open Access | Download Full Text
Volume 4 | Issue 2 | Year 2025 | Article Id: DST-V4I2P104 DOI: https://doi.org/10.59232/DST-V4I2P104
Energy Capable Clustering Approach to Enhance the Duration of Mobile Smart Dust Network
Rajesh D, Giji Kiruba D, Ramesh D, Swapna J
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 04 Apr 2025 | 12 May 2025 | 07 Jun 2025 | 30 Jun 2025 |
Citation
Rajesh D, Giji Kiruba D, Ramesh D, Swapna J. “Energy Capable Clustering Approach to Enhance the Duration of Mobile Smart Dust Network .” DS Journal of Digital Science and Technology, vol. 4, no. 2, pp. 56-68, 2025.
Abstract
Smart dust nodes in Mobile Wireless Sensor Networks (MWSNs) are typically placed in isolated places for extended periods with little to no social interaction and restricted energy resources. As a result, energy consumption becomes a crucial problem for smart dust deployment and layouts. To reduce energy consumption, a novel Energy-Capable Clustering Approach for the Smart Dust EC2ASD environment is a revolutionary tactic that is presented in this research. It depends on a two-phase clustering representation and offers the most environment coverage while using the least amount of energy possible. For self-governing heterogeneous techniques of establishing the characteristics in compliance with the signalling sequences wherein roughly the same data rates are supplied for every smart dust, this methodology utilized an efficient aware resource scheduling strategy. This resource-competent clustering technique can create energy-stabilized clusters as well, extending the network's life span and enhancing environmental coverage. According to simulation studies, EC2ASD outperforms DBSCAN and FCEEC in terms of network lifetime while also obtaining greater network coverage. The suggested technique not only provides a novel and improved method for choosing Cluster Heads (CHs) to minimise energy utilisation of network participants, producing greater overall stability of smart dust networks, but also identifies an appropriate cluster size with minimal energy dissipation.
Keywords
Smart dust, Energy competent clustering, Cluster head selection, Network coverage, Network duration.
References
[1] D. Abdul Kareem, and D. Rajesh, “An Energy-Efficient Cluster-Based Routing Protocol for WBAN in Elk Herd Optimizer,” Fusion: Practice and Applications, vol. 17, no. 1, pp. 107-123, 2025.
[2] Amrita Ghosal, Subir Halder, and Sajal K. Das, “Distributed on-Demand Clustering Algorithm for Lifetime Optimization in Wireless Sensor Networks,” Journal of Parallel and Distributed Computing, vol. 141, pp. 129-142, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Singh Omkar, and Vinay Rishiwal, “QoS Aware Multi-hop Multi-path Routing Approach in Wireless Sensor Networks,” International Journal of Sensors Wireless Communications and Control, vol. 9, no. 1, pp. 43-52, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Indranil Ghosh, "Study on Hierarchical Cluster-Based Energy-Efficient Routing in Wireless Sensor Networks,” International Research Journal of Engineering and Technology (IRJET), vol. 5, no, 3, pp. 688-691, 2018.
[Google Scholar] [Publisher Link]
[5] Anurag Shukla, and Sarsij Tripathi, “An Effective Relay Node Selection Technique for Energy Efficient WSN-Assisted IoT,” Wireless Personal Communications, vol. 112, no, 4, pp. 2611-2641, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ushus Elizebeth Zachariah, and Lakshmanan Kuppusamy, “A Novel Approach on Energy-Efficient Clustering Protocol for Wireless Sensor Networks,” International Journal of Communication Systems, vol. 35, no. 9, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Walid Osamy et al., “IPDCA: Intelligent Proficient Data Collection Approach for IoT-Enabled Wireless Sensor Networks in Smart Environments,” Electronics, vol. 10, no. 9, pp. 1-28, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Piyush Rawat, and Siddhartha Chauhan, “Particle Swarm Optimization-Based Energy Efficient Clustering Protocol in Wireless Sensor Network,” Neural Computing and Applications, vol. 33, no. 21, pp. 14147-14165, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Hifzan Ahmad, and Narendra Kohli, “LBCM: Energy-Efficient Clustering Method in Wireless Sensor Networks,” Engineering and Applied Science Research, vol. 48, no. 5, pp. 529-536, 2021.
[Google Scholar] [Publisher Link]
[10] M. Revanesh, John M. Acken, and V. Sridhar, “DAG block: Trust Aware Load Balanced Routing and Lightweight Authentication Encryption in WSN,” Future Generation Computer Systems, vol. 140, pp. 402-421, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] D. Anuradha et al., “Energy Aware Seagull Optimization-Based Unequal Clustering Technique in WSN Communication,” Intelligent Automation and Soft Computing, vol. 32, no. 3, pp. 1325-1341, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] R. Muthukkumar et al., “A Genetic Algorithm-Based Energy-Aware Multi-Hop Clustering Scheme for Heterogeneous Wireless Sensor Networks,” Peer Journal Computer Science, vol. 8, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jingyu Wang et al., “Forestry Canopy Image Segmentation Based on Improved Tuna Swarm Optimization,” Forests, vol. 13, no. 11, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] P. Suman Prakash, D. Kavitha, and P. Chenna Reddy, “Delay-Aware Relay Node Selection for Cluster-Based Wireless Sensor Networks,” Measurement Sensors, vol. 24, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] R. Vinodhini, and C. Gomathy, “Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm Optimization in Wireless Sensor Networks Forest Fire Detection,” Wireless Personal Communications, vol. 118, no. 4, pp. 3501-3522, 2021.