Open Access
ARTICLE
A Fuzzy Multi-Objective Framework for Energy Optimization and Reliable Routing in Wireless Sensor Networks via Particle Swarm Optimization
Medhat A. Tawfeek1,*, Ibrahim Alrashdi1, Madallah Alruwaili2, Fatma M. Talaat3,4
1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Aljouf, Saudi Arabia
2 Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Aljouf, Saudi Arabia
3 Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt
4 Faculty of Computer Science & Engineering, New Mansoura University, Gamasa, 35712, Egypt
* Corresponding Author: Medhat A. Tawfeek. Email: 
(This article belongs to the Special Issue: AI-Assisted Energy Harvesting Techniques and its Applications in Wireless Sensor Networks)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.061773
Received 03 December 2024; Accepted 07 March 2025; Published online 24 March 2025
Abstract
Wireless Sensor Networks (WSNs) are one of the best technologies of the 21st century and have seen tremendous growth over the past decade. Much work has been put into its development in various aspects such as architectural attention, routing protocols, location exploration, time exploration, etc. This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments, such as balancing energy consumption, ensuring routing reliability, distributing network load, and selecting the shortest path. Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives. To address this gap, this paper proposes an innovative approach that integrates Particle Swarm Optimization (PSO) with a fuzzy multi-objective framework. The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives. The search efficiency is improved by particle swarm optimization (PSO) which overcomes the large computational requirements that serve as a major drawback of existing methods. The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness. The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions. These adjustments influence PSO’s velocity updates, ensuring continuous adaptation under varying network conditions. The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation. The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%, increasing the stabilization time by 18.7%–25.5%, and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques. The proposed model also achieves a 15%–24% reduction in load variance, demonstrating balanced routing and extended network lifetime. Furthermore, analysis using
p-values obtained from multiple performance measures (
p-values < 0.05) showed that the proposed approach outperforms with a high level of confidence. The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs. It allows stable performance in networks with 100 to 300 nodes, under varying node densities, and across different base station placements. Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.
Keywords
Wireless sensor networks; particle swarm optimization; fuzzy multi-objective framework; routing stability
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