Dominating sets in hierarchical sensor networks
*Mrunmai Mandar RanadeCorresponding authormrunmai.ranade@vupune.ac.inDepartment of Engineering SciencesVishwakarma UniversityPune, Maharashtra, 411048, IndiaView full profile → , S. Gayathrigayathri@avit.ac.inDepartment of Humanities and Sciences (Mathematics Division)Aarupadai Veedu Institute of TechnologyVinayaka Mission’s Research Foundation (Deemed to be University)Paiyanoor, Chennai, Tamil Nadu, 603104, IndiaView full profile → , Gagan Tiwarigagan.tiwari@niu.edu.inDepartment of Computer SciencesNoida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , Vundela SwathivundelaSwathi2024@protonmail.comCentre for Multidisciplinary ResearchAnurag UniversityHyderabad, Telangana, 500088, IndiaView full profile → , Shitij Goyalshitij.qsb@quantumeducation.inQuantum University Research CenterQuantum UniversityRoorkee, Uttarakhand, 247667, IndiaView full profile → , Ravi Kumarravi.kumar.orp@chitkara.edu.inCentre for Research Impact and OutcomeChitkara UniversityRajpura, Punjab, 140417, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3025
- Pages:
- 3985–3996
Abstract
The sets that take over are important as well in resource use and the effectiveness of communication in a hierarchical sensor network. The current paper presents a multi-level dominating set model which is particular to clustered architectures of sensor nodes, cluster heads and gateways. The proposed methodology will be oriented to reduce the use of energy, network coverage, minimize the latency, and enhance fault tolerance. A top-down dominating set construction algorithm is an algorithm that is meant to select the most appropriate nodes in every step according to the cluster-based decision making. The simulation results suggest that the proposed solution can save the energy usage, enhance the coverage effectiveness, and the latency of communications by about 28.6 percent, 21.4 percent, and 18.2 percent, respectively, compared to the traditional flat-network solutions. Additionally, the model increases the lifetime and resilience of the network in the event of node failures. It can be scaled and configured and can be applied to large-scale IoT and environmental monitoring.
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References
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