Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization
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 (M...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
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| Online Access: | https://doi.org/10.1186/s13638-025-02449-w |
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| author | Medhat A. Tawfeek Ibrahim Alrashdi Madallah Alruwaili Leila Jamel Gamal Farouk Elhady Haitham Elwahsh |
| author_facet | Medhat A. Tawfeek Ibrahim Alrashdi Madallah Alruwaili Leila Jamel Gamal Farouk Elhady Haitham Elwahsh |
| author_sort | Medhat A. Tawfeek |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-72184cb9140e491e9972ae426a0fa27d |
| institution | OA Journals |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-72184cb9140e491e9972ae426a0fa27d2025-08-20T02:11:58ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-04-012025112710.1186/s13638-025-02449-wImproving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimizationMedhat A. Tawfeek0Ibrahim Alrashdi1Madallah Alruwaili2Leila Jamel3Gamal Farouk Elhady4Haitham Elwahsh5Department of Computer Science, College of Computer and Information Sciences, Jouf UniversityDepartment of Computer Science, College of Computer and Information Sciences, Jouf UniversityDepartment of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf UniversityDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment Computer Science, Faculty of Computers and Information, Menoufia UniversityFaculty of Information Technology, Applied Science Private UniversityAbstract 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.https://doi.org/10.1186/s13638-025-02449-wWireless sensor networksAnt colony optimizationEnergy efficiencyNetwork lifetimeRouting reliability |
| spellingShingle | Medhat A. Tawfeek Ibrahim Alrashdi Madallah Alruwaili Leila Jamel Gamal Farouk Elhady Haitham Elwahsh Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization EURASIP Journal on Wireless Communications and Networking Wireless sensor networks Ant colony optimization Energy efficiency Network lifetime Routing reliability |
| title | Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization |
| title_full | Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization |
| title_fullStr | Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization |
| title_full_unstemmed | Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization |
| title_short | Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization |
| title_sort | improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization |
| topic | Wireless sensor networks Ant colony optimization Energy efficiency Network lifetime Routing reliability |
| url | https://doi.org/10.1186/s13638-025-02449-w |
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