Smart City Gnosys
Smart city article details
| Title | Enhancing Energy Distribution Through Dynamic Multi-Hop For Heterogeneous Wsns Dedicated To Iot-Enabled Smart Grids |
|---|---|
| ID_Doc | 23793 |
| Authors | Elmonser M.; Alaerjan A.; Jabeur R.; Chikha H.B.; Attia R. |
| Year | 2024 |
| Published | Scientific Reports, 14, 1 |
| DOI | http://dx.doi.org/10.1038/s41598-024-76492-w |
| Abstract | This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs. Although hierarchical clustering methods are effective in WSNs, finding the ideal routing protocol remains challenging due to extensive deployment. To address energy efficiency and network durability, the study proposes the integration of the heterogeneous dynamic multi-hop (HDM) approach with the low-energy adaptive clustering hierarchy (LEACH) protocol, specifically for IoT smart city applications. The HDM model combines multi-hop communication with dynamic hierarchical clustering, distinguishing between normal and advanced nodes based on energy levels to optimize cluster head selection probabilities. The methodology involves a comparative analysis between the HDM protocol and LEACH in a heterogeneous environment (H-LEACH) in terms of energy conservation, and network lifespan. Results demonstrate that the HDM protocol outperforms H-LEACH. Notably, HDM consumes half of the total energy over 4600 rounds, compared to H-LEACH’s 3000 rounds. These findings have important implications for deploying WSNs in smart grid applications, supporting sustainable and resilient urban IoT ecosystems. © The Author(s) 2024. |
| Author Keywords | Energy efficiency; Heterogeneous WSNs; Hierarchical clustering; IoT applications; Routing protocols; Smart grids |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
31134  | 0.908 | Venkatesan V.K.; Izonin I.; Periyasamy J.; Indirajithu A.; Batyuk A.; Ramakrishna M.T. | Incorporation Of Energy Efficient Computational Strategies For Clustering And Routing In Heterogeneous Networks Of Smart City | Energies, 15, 20 (2022) |
| 8882 | 0.895 | Saleh S.S.; Alansari I.S.; Farouk M.; Hamiaz M.K.; Ead W.; Tarabishi R.A.; Khater H.A. | An Optimized Hierarchal Cluster Formation Approach For Management Of Smart Cities | Applied Sciences (Switzerland), 13, 24 (2023) |
| 18992 | 0.886 | Osamy W.; Khedr A.M.; Salim A. | Design Workload Aware Data Collection Technique For Iot-Enabled Wsns In Sustainable Smart Cities | IEEE Transactions on Sustainable Computing, 10, 2 (2025) |
| 35434 | 0.886 | Srivastava A.; Mishra P.K. | Load-Balanced Cluster Head Selection Enhancing Network Lifetime In Wsn Using Hybrid Approach For Iot Applications | Journal of Sensors, 2023 (2023) |
| 6265 | 0.885 | Swain A.S. | Adaptive Iot Routing Protocols In Wireless Sensor Networks | Big Data and Computing Visions, 5, 2 (2025) |
| 8370 | 0.884 | Darabkh K.A.; Amareen A.B.; Al-Akhras M.; Kassab W.K. | An Innovative Cluster-Based Power-Aware Protocol For Internet Of Things Sensors Utilizing Mobile Sink And Particle Swarm Optimization | Neural Computing and Applications, 35, 26 (2023) |
| 913 | 0.884 | Nedham W.B.; Al-Qurabat A.K.M. | A Comprehensive Review Of Clustering Approaches For Energy Efficiency In Wireless Sensor Networks | International Journal of Computer Applications in Technology, 72, 2 (2023) |
| 49667 | 0.881 | Darabkh K.A.; Al-Akhras M. | Smart Cities Optimization Using Computational Intelligence In Power-Constrained Iot Sensor Networks | Swarm and Evolutionary Computation, 94 (2025) |
| 8457 | 0.88 | Baskaran P.; Karuppasamy K. | An Integrated Model For Energy Conservation In Iot-Enabled Wsn Using Adaptive Regional Clustering And Monkey Inspired Optimization | Journal of Intelligent and Fuzzy Systems, 43, 4 (2022) |
| 20677 | 0.879 | Jeevanantham S.; Venkatesan C.; Rebekka B. | Distributed Neuro-Fuzzy Routing For Energy-Efficient Iot Smart City Applications In Wsn | Telecommunication Systems, 87, 2 (2024) |