Fuzzy-ACO Hybrid Routing Framework for Resilient AODV in IoT-Enabled WSN with Link Interference
Keywords:
Shortest Path Routing, Dynamic Networks, MANET, IoT, VANET, Adaptive Algorithms, Packet Delivery Ratio, End-to-End Delay, Throughput, Routing Overhead, Opti- mization Techniques, Network Performance.Abstract
The rapid growth of the Internet of Things (IoT) has increased the demand for reliable and energy-efficient communication in Wireless Sensor Networks (WSNs). However, traditional Ad hoc On-Demand Distance Vector (AODV) routing faces challenges such as link instability, interference, packet loss, and high energy consumption in dynamic IoT environments. To address these issues, this research proposes a Fuzzy-ACO Hybrid Routing Framework for Resilient AODV in IoT-Enabled WSNs with Link Interference. The proposed framework integrates Fuzzy Logic and Ant Colony Optimization (ACO) with AODV routing to enhance route selection and network resilience. Fuzzy Logic evaluates multiple routing parameters, including residual energy, link quality, node distance, and interference level, to determine optimal routing decisions. ACO further refines route discovery by identifying efficient and reliable paths based on pheromone-guided optimization. The hybrid approach minimizes packet loss, reduces end-to-end delay, improves packet delivery ratio (PDR), and extends network lifetime under varying interference conditions. Simulation results demonstrate that the proposed framework outperforms conventional AODV and existing optimization-based routing schemes in terms of throughput, energy efficiency, reliability, and Quality of Service (QoS). The proposed solution provides a robust and intelligent routing mechanism suitable for next-generation IoT-enabled WSN applications.
References
Rahmani, A. M., Haider, A., Ali, S., Mohammadi, M., Mehranzadeh, A., Khoshvaght, P., & Hosseinzadeh, M. (2025). 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, 122, 109946.
Khedr, A. M., Alfawaz, O., PV, P. R., & Osamy, W. (2025). Advancing IoT-driven WSNs with context-aware routing: A comprehensive review. Computer Science Review, 58, 100803.
Sefati, S. S., Maiduc, S. O., Arasteh, B., Larkotey, W. O., Bouyer, A., & Khan, W. U. (2025). Optimizing energy-efficient routing in Mobile internet of things (MIoT) networks using Grey wolf optimization and recurrent neural networks. Ad Hoc Networks, 104047.
Karuppiah, A. B., Nanjappan, V., RajaRaja, R., & Priyan, S. V. (2025). Neuro inspired deep learning based secure and energy efficient routing with autonomous intrusion prevention in wireless sensor networks. Engineering Applications of Artificial Intelligence, 162, 112783.
Nivedita, V., Shieh, C. S., & Horng, M. F. (2025). An integrated trust-based secure routing with intrusion detection for mobile Ad Hoc network using adaptive snow geese optimization algorithm. Ain Shams Engineering Journal, 16(7), 103385.
Chandra, A., & Chakravarthy, A. S. N. (2025). EAURP: An Energy-Efficient and Trust-Aware Unobservable Routing Protocol for Secure Mobile Ad Hoc Networks. Sustainable Computing: Informatics and Systems, 101285.
Poornima, M. R., Vimala, H. S., & Shreyas, J. (2023). Holistic survey on energy aware routing techniques for IoT applications. Journal of Network and Computer Applications, 213, 103584.
Jain, J. K., & Chauhan, D. (2025). Optimized secure and energy-efficient approach for IoT-enabled wireless sensor networks. Pervasive and Mobile Computing, 110, 102049.
Khan, T., Singh, K., Hasan, M. H., Ahmad, K., Reddy, G. T., Mohan, S., & Ahmadian, A. (2021). ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs. Future Generation Computer Systems, 125, 921-943.
Anugraha, M., Ebenezer, S. S., & Maheswari, S. (2025). Hybrid Elk Herd Green Anaconda-Based Multipath Routing and Deep Learning-Based Intrusion Detection In MANET. Pervasive and Mobile Computing, 102079.
Mishra, R. (2024). Raspberry Pi Performance analysis across its Operating System in LED Control Operation. International Journal of Advanced Research and Multidisciplinary Trends (IJARMT), 1(2), 01-11.
Mishra, R. (2025). IOT and DSP (combination of hardcore Virtex-5 FPGA and soft core DSP processor) OFDM System PAPR Reduction Using Artificial Intelligence Algorithm. International Journal of Advanced Research and Multidisciplinary Trends (IJARMT), 2(1), 135-149.
Mishra, R., & Sharma, A. (2026). Enhanced Trajectory Tracking of a 6-DOF Robotic Manipulator Using GA–PID and ANN–PID Controllers. International Journal of Research & Technology, 14(2), 53-70.
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