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Volume 13 | Issue 7 | Year 2026 | Article Id. IJECE-V13I7P113 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I7P113

Energy-Aware Cluster-Optimized Intelligent Routing Protocol for WSN-based IoT Networks

Pravin Yallappa Kumbhar, Apurva Abhijit Naik

Received Revised Accepted Published
10 May 2026 11 Jun 2026 29 Jun 2026 29 Jul 2026

Citation :

Pravin Yallappa Kumbhar, Apurva Abhijit Naik, "Energy-Aware Cluster-Optimized Intelligent Routing Protocol for WSN-based IoT Networks," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 185-202, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P113

Abstract

Wireless Sensor Networks (WSNs) use resource-constrained sensor nodes to continuously monitor ambient conditions and serve as data sinks for various Internet of Things (IoT) applications. However, maximizing Energy Efficiency (EE) while maintaining reliable data delivery remains a significant challenge. Existing clustering and routing algorithms struggle with issues such as uneven energy consumption, premature node failures, and poor network performance. Additionally, many existing metaheuristic schemes are not adaptable to dynamic network environments, which leads to ineffective energy management. To address the above issues and enhance the energy efficiency of IoT-based WSN, this research introduced a novel Energy-Aware Cluster-Optimized Intelligent Routing (EACO-IR) protocol. The EACO-IR protocol performs in three stages: stable cluster formation, Cluster Head (CH) selection, and energy-aware route finding. At first, stable clusters are formed using the Hybrid Fuzzy–Density Adaptive Kronecker Clustering (HF-DAC) algorithm. Subsequently, CHs are optimally selected using the Mutation-Enhanced Armadillo–Devil Optimization (MEADO) based on a multi-objective function for the Base Station (BS). Finally, the Multi-level Energy-Aware Attention Transformer- Based Reinforcement Learning is introduced to create intra and inter-cluster data travel ways to minimize communication overhead from SNs to the BS. Experimental results show that the proposed protocol yields an average throughput of 4 Mbps, an average Packet Delivery Ratio (PDR) of 98.71%, and an end-to-end latency of 0.06 seconds, outperforming current state-of-the-art clustering and routing algorithms. Ultimately, this framework establishes a highly adaptable template for deploying self-optimizing, long-lasting IoT architectures capable of supporting real-time data streaming without premature network degradation.

Keywords

Energy-efficient routing, Kronecker clustering, Metaheuristic energy-optimization, Wireless sensor networks, Reinforcement learning.

References

  1. Mohammed Zaid Ghawy et al., “An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1-13, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  2. Muhammad Ilyas et al., “Trust-Based Energy-Efficient Routing Protocol for Internet of Things–based Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 16, no. 10, pp. 1-20, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. Maryam Shafiq et al., “Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN,” Computers, Materials & Continua, vol. 67, no. 3, pp. 3505-3521, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  4. Roopali Dogra et al., “Energy-Efficient Routing Protocol for Next-Generation Application in the Internet of Things and WirelessSensor Networks,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1-10, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. Sowmya Gali, and Venkatram Nidumolu, “An Intelligent Trust Sensing Scheme with Metaheuristic based Secure Routing Protocol for Internet of Things,” Cluster Computing, vol. 25, no. 3, pp. 1779-1789, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Ali Abu Odeh et al., “A Survey of Energy-Efficient Routing Protocols for WSNbased IoT Networks,” University of Bahrain, pp. 1-10, 2024.
    [Google Scholar] [Publisher Link]
  7. Quan Tang, and Fang Nie, “Clustering Routing Algorithm of Wireless Sensor Network based on Swarm Intelligence,” Wireless Networks, vol. 30, no. 9, pp. 7227-7238, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Ashutosh Kumar Rao, Bhupesh Kumar Singh, and Kapil Kumar Nagwansh, “An Optimized Routing Protocol for Energy-Efficient Data Transmission in Agricultural Environments using WSN-based IoT Networks,” International Journal of Advanced Technology and Engineering Exploration, vol. 11, no. 120, pp. 1562-1578, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Shikha Verma, and Vimal Dwivedi, “Energy-Aware Routing Protocols in IoT-Based WSN,” Energy Efficient Internet of Things‐Based Wireless Sensor Network, pp. 217-234, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Rahul Priyadarshi, “Exploring Machine Learning Solutions for Overcoming Challenges in IoT-based Wireless Sensor Network Routing: A Comprehensive Review,” Wireless Networks, vol. 30, no. 4, pp. 2647-2673, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  11. Shreeram Hudda, and K. Haribabu “A Review on WSN based Resource Constrained Smart IoT Systems,” Discover Internet of Things, vol. 5, no. 1, pp. 1-46, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  12. Ali Siddiq, and Yahya Jaber Ghazwani, “Hybrid Optimized Deep Neural Network-Based Intrusion Node Detection and Modified Energy Efficient Centralized Clustering Routing Protocol for Wireless Sensor Network,” IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 6303-6313, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  13. Nguyen Duy Tan, and Van-Hau Nguyen, “Machine Learning Meets IoT: Developing an Energy-Efficient WSN Routing Protocol for Enhanced Network Longevity,” Wireless Networks, vol. 31, no. 4, pp. 3127-3147, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  14. S. Jeevanantham, C. Venkatesan, and B. Rebekka, “Distributed Neuro-Fuzzy Routing for Energy-Efficient IoT Smart City Applications in WSN,” Telecommunication Systems, vol. 87, no. 2, pp. 497-516, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. S. Kishore Verma et al., “Energy Efficient Multi-Objective Cluster-Based Routing Protocol for WSN using Interval Type-2 Fuzzy Logic Modified Dingo Optimization,” Peer-to-Peer Networking and Applications, vol. 17, no. 4, pp. 1934-1962, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Noor Raad Saadallah, and Salah Abdulghani Alabady, “A Comprehensive Study on Energy-Efficient-based Routing Protocols in the Internet of Things Part I: Definition and Classification,” Iran Journal of Computer Science, vol. 7, no. 3, pp. 631-661, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. G.E. Raghavendra Patil, “A Methodology for Energy-Efficient Cooperative Routing Protocol in WSN-based IoT,” SGS-Engineering & Sciences: LGPR Batch 1 Conference 2, vol. 1, no. 2, pp. 1-6, 2025.
    [Google Scholar] [Publisher Link]
  18. Pranati Mishra et al., “Energy-Aware Intelligent Routing Framework for 6G-based Wireless Sensor Networks,” Procedia Computer Science, vol. 258, pp. 1285-1294, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. G.E. Raghavendra Patil, Vivekanandam, and Pawan Whig, “An Energy-Efficient Cooperative Routing Protocol for Wireless Sensor Networks (WSNs) based IoT Applications: A Review,” SGS-Engineering & Sciences: LGPR Batch 1 Conference 1, vol. 1, no. 1, pp. 1-8, 2025.
    [Google Scholar] [Publisher Link]
  20. M. Karthikeyan, D. Manimegalai, and Karthikeyan Rajagopal, “Optimizing Energy Efficiency in Wireless Sensor Networks: Dynamic Routing with Capuchin Search Algorithm,” Multimedia Tools and Applications, vol. 84, no. 15, pp. 15453-15477, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Regilan Soosai Raj, and Lakshmi Kuppusamy Hema, “Dynamic Clustering Optimization for Energy Efficient IoT Network: A Simple Constrastive Graph Approach,” Expert Systems with Applications, vol. 264, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. S. R. Menaka, and M. Prabu, “A Novel Approach for Fuzzy Logic–Based Clustering With Optimized Energy Efficient Routing in Internet of Things–Based Wireless Sensor Networks,” International Journal of Communication Systems, vol. 38, no. 16, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Muzammil Hussain et al., “Optimized Rank-based Key Management for Energy-Efficient Routing in Wireless Sensor Networks for IoT Applications,” Discover Internet of Things, vol. 5, no. 1, pp. 1-31, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Nguyen Duy Tan, Nguyen Minh Quy, and Van-Hau Nguyen, “EE-AIRP: An AI-Enhanced Energy-Efficient Routing Protocol for IoT-Enabled WSNs,” Computer Networks, vol. 273, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. V. Irine Shyja et al., “Optimal Cluster-based Energy Efficient Routing Scheme for QoS Aware IoT-Enabled Wireless Body Area Network,” Scientific Reports, vol. 16, pp. 1-31, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. Sridevi Tumula et al., “An Opportunistic Energy-Efficient Dynamic Self-Configuration Clustering Algorithm in WSN-based IoT Networks,” International Journal of Communication Systems, vol. 37, no. 1, pp. 1-21, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. M. Archana et al., “Energy-Efficient and Sustainable Cluster-Based Routing in IoT Based WSNs Using Metahueristic Optimization,” 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Goathgaun, Nepal, pp. 79-83, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Bishwajeet Kumar, and Ranjith Kumar Anandan, “AEZ-SEP: Advance EZ-SEP Heterogeneous Energy Efficient Routing Protocol with Hierarchical Clustering Approach for WSN-Based IoT,” Journal of Information Science & Engineering, vol. 42, no. 2, pp.  365-377, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  29. Piyush Rawat et al., “Energy-Efficient Cluster-based Routing Protocol for Heterogeneous Wireless Sensor Network,” Annals of Telecommunications, vol. 80, no. 1, pp. 109-122, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. K. K. Rajkumar et al., “Adaptive and Energy-Efficient Routing in WSNs from Fuzzy Clusters to Bee-Inspired Paths,” 2025 Fourth International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), Villupuram, India, pp. 1-5, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Amir Masoud Rahmani et al., “A Routing Approach based on Combination of Gray Wolf Clustering and Fuzzy Clustering and using Multi-Criteria Decision Making Approaches for WSN-IoT,” Computers and Electrical Engineering, vol. 122, pp. 1-17, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  32. Wenfen Zhang et al., “An Adaptive Clustering Routing Protocol for Wireless Sensor Networks Based on a Novel Memetic Algorithm,” IEEE Sensors Journal, vol. 25, no. 5, pp. 8929-8941, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  33. C. Vimalarani et al., “Improving Energy Efficiency in WSN through Adaptive Memetic-based Clustering and Routing for Resource Management,” Sustainable Computing: Informatics and Systems, vol. 45, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Ali Kooshari et al., “An Optimization Method in Wireless Sensor Network Routing and IoT with Water Strider Algorithm and Ant Colony Optimization Algorithm,” Evolutionary Intelligence, vol. 17, no. 3, pp. 1527-1545, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Vanita Verma, and Vijay Kumar Jha, “Secure and Energy-Aware Data Transmission for IoT-WSNs with the Help of Cluster-Based Secure Optimal Routing,” Wireless Personal Communications, vol. 134, no. 3, pp. 1665-1686, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  36. Yang Zhang, Ling Yang, and Yan Tan, “Energy-Efficient Adaptive Routing in Heterogeneous Wireless Sensor Networks via Hybrid PSO and Dynamic Clustering,” Journal of Cloud Computing, vol. 14, no. 1, pp. 1-15, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  37. Liu Guangjie, “QPSODRL: An Improved Quantum Particle Swarm Optimization and Deep Reinforcement Learning based Intelligent Clustering and Routing Protocol for Wireless Sensor Networks,” Scientific Reports, pp. 1-17, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  38. E. Silambarasan et al., “Energy Efficient Heterogeneous Clustering Scheme using Improved Golden Eagle Optimization Algorithm for WSN-based IoT,” International Journal of Information Technology, vol. 17, no. 3, pp. 1753-1760, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  39. Aruna Malik et al., “Greylag Goose-Based Optimized Cluster Routing for IoT-Based Heterogeneous Wireless Sensor Networks,” IEEE Transactions on Network and Service Management, vol. 23, pp. 340-349, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  40. Muhammad Akram et al., “EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks,” IEEE Access, vol. 13, pp. 70849-70871, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  41. Jun Yang et al., “An Energy-Efficient and Transmission-Efficient Adaptive Routing Algorithm Using Deep Reinforcement Learning for Wireless Sensor Networks,” IEEE Internet of Things Journal, vol. 12, no. 23, pp. 50414-50426, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  42. T. Kiruthika, and E. Karthikeyan, “Novel Energy-Centric Fossa Optimization-Based Energy-Aware Clustering and Routing in IoTenabled Wireless Sensor Networks,” 2026 4th International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Ottapalam, India, pp. 764-771, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  43. Imane Aly Saroit, and Dina Tarek, “Energy-Driven K-Means-Based LEACH Routing Protocol for Enhanced Lifetime in Wireless Sensor Networks,” Scientific Reports, vol. 16, pp. 1-35, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  44. Zhengbing Hu et al., “Fuzzy Clustering of Educational Data with Automated Selection of Processing Parameters in System Analysis of Quality Education,” International Journal of Modern Education and Computer Science (IJMECS), vol. 17, no. 4, pp. 101-111, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  45. Xiaojuan Ran et al., “A Novel Fuzzy System-Based Genetic Algorithm for Trajectory Segment Generation in Urban Global Positioning System,” Journal of Advanced Research, vol. 81, pp. 469-480, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  46. Abdel Rahman Mohammad Alkhazali et al., “Pink Fairy Armadillo Optimization (PFAO): A Novel Bio-inspired Metaheuristic for Solving Optimization Problems,” International Journal of Intelligent Engineering and Systems, vol. 19, no. 3, pp. 1099-1117, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  47. Himansu Das, Muskan, and Hrishikesh Kumar, “Feature Selection using Tasmanian Devil Optimisation Algorithm for Software Fault Prediction,” International Journal of Computational Science and Engineering, vol. 28, no. 1, pp. 32-55, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  48. Fatemeh Roshanzadeh, Hamid Barati, and Ali Barati, “DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN),” Concurrency and Computation: Practice and Experience, vol. 38, no. 1, pp. 1-28, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  49. Vidhya Sachithanandam et al., “Deep Reinforcement Learning and Enhanced Optimization for Real-time Energy Management in Wireless Sensor Networks,” Sustainable Computing: Informatics and Systems, vol. 45, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  50. H. N. Vishwas, and T. K. Ramesh, “Hybrid Energy-Aware Framework for WSNs: Clustering, Routing, and Optimization Using PSO, Fuzzy Logic, and SOM,” IEEE Open Journal of the Communications Society, vol. 7, pp. 1827-1872, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  51. Anita Rani, Rakesh Kumar, and Anant Ram, “A Dynamic Cluster-Based Approach for Enhanced Energy Efficiency in Wireless Body Area Networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 16, no. 11, pp. 1241-1251, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  52. Swati Goel et al., “An Improved Routing Technique for Energy Optimization and Delay Reduction for Wireless Body Area Networks,” Egyptian Informatics Journal, vol. 29, pp. 1-12, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  53. Nelofar Aslam et al., “Intelligent Wireless Charging Path Optimization for Critical Nodes in Internet of Things-Integrated Renewable Sensor Networks,” Sensors, vol. 24, no. 22, pp. 1-17, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  54. Mehdi Hosseinzadeh et al., “Enhancing DBSCAN Clustering with Fuzzy System to Improve IoT-based WBAN Performance,” Scientific Reports, vol. 15, no. 1, pp. 1-17, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  55. Sohail Saif, and Ramesh Saha, “M-EEMH: An Energy-Efficient Mobility Aware Multi-hop Protocol for QoS-driven WBAN Applications,” Cluster Computing, vol. 28, no. 10, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]