Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization

(2025) 2025:22 Tawfeek et al. J Wireless Com Network https://doi.org/10.1186/s13638-025-02449-w RESEARCH EURASIP Journal on Wireless Communications and Networking Open Access Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization Medhat A. Tawfeek1,4* , Ibrahim Alrashdi1, Madallah Alruwaili2, Leila Jamel3, Gamal Farouk Elhady4 and Haitham Elwahsh5,6 *Correspondence: 1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, 72341 Sakaka, Aljouf, Saudi Arabia 2 Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, 72341 Sakaka, Aljouf, Saudi Arabia 3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671 Riyadh, Saudi Arabia 4 Department Computer Science, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt 5 Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan 6 Department of Computer Science, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33511, Egypt Abstract Wireless sensor networks (WSNs) are essential in a wide range of applications, but the challenges of energy efficiency, load balancing, and optimal routing remain critical for ensuring long-term network reliability. In this study, we introduce a Modified Ant Colony Optimization Algorithm (MACOA) to address these challenges. The proposed MACOA lies in several key innovations to address the limitations of existing ACO-based and bio-inspired routing protocols. First, MACOA applies a multi-objective heuristic function to simultaneously optimize power consumption while ensuring reliability, bandwidth, and short path distances to achieve an efficient routing solution. Second, it introduces an adaptive pheromone decay mechanism that dynamically adjusts based on network conditions, such as node energy levels and link reliability, to prioritize energy-efficient paths. Third, MACOA incorporates a load-balancing factor that prevents the overloading of certain nodes, thus extending the network lifetime. Finally, it regulates the exploration–exploitation trade-off dynamically by promoting early-stage exploratory behavior and later-stage exploitative behavior during optimization. Together, these innovations enable MACOA to be an efficient routing protocol that outperforms current state-of-the-art algorithms. We compare the performance of the proposed MACOA with existing state-of-the-art techniques, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony, Deep Reinforcement Learning, and Energy Reliable ACO Routing Protocol (E-RARP) in terms of network lifetime, network stabilization time, energy efficiency, load balancing, and throughput. Extensive results demonstrate that the proposed method outperforms the compared techniques. They state the adaptability of the proposed MACOA to dynamic network conditions and its robustness to node failures, which make the proposed MACOA a promising solution for WSNs and qualify it as a potential solution to large-scale and power-limited WSNs. Keywords: Wireless sensor networks, Ant colony optimization, Energy efficiency, Network lifetime, Routing reliability © The Author(s) 2025. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Tawfeek et al. J Wireless Com Network (2025) 2025:22 1 Introduction WSNs are widely used in different areas like the environment, health, smart cities, and the military. These networks include distributed sensor nodes that capture given information and relay this information to a base station or between the nodes [1]. The architecture of WSNs consists of sensor nodes distributed in a certain way to sense, process, and transmit data. They are often deployed in remote environments to collect and transmit data about their surroundings, and these environments are dangerous or difficult where human monitoring is impractical or very expensive [2]. WSNs achieved several challenges, such as energy constraints, a large number of nodes with varying structures, and fault tolerance. Since some types of these networks are developed for operation in rather inaccessible regions, it becomes important to use an optimal design for the communication and routing part in order to enhance the lifetime of the networks and ensure reliable data transfer [3]. One of the major challenges is energy efficiency, which is an exceptionally demanding task. Devices called sensor nodes typically have a limited power source in the form of batteries. The replacement or recharging of these batteries is costly, especially when they are used on a scale that is difficult to reach [4]. On the other hand, it is essential to set high routing reliability since the most sensitive sensor data should be delivered to the sink node [5]. Self-organizing WSN is another major issue and adheres to several other goals such as maximizing bandwidth, minimizing communication distance, reducing power consumption, and enhancing reliability. For this reason, working to increase the lifetime of each node and the network as a whole is the main direction. As a result, routing protocols act as critical determinants of energy efficiency in WSNs based on how data packets move in the network. The goal of an efficient routing scheme is to help identify good paths to minimize energy consumption in sending and receiving messages. This can also be done by balancing the load across reliable nodes and not focusing on the same paths continuously, which may lead to rapid consumption at certain nodes and then turning into dead nodes. However, some routing algorithms may have difficulty dealing with these objectives and cannot adequately meet these requirements, resulting in premature node failure and data loss due to the dynamic and constrained environment of WSNs. Also, modern approaches are usually limit (...truncated)