What question did this study set out to answer?

The research aims to develop an energy-efficient scheduling method for wireless sensor networks using a hybrid approach integrating reinforcement learning and optimization algorithms.

March 14, 2026Open Access

Energy optimized scheduling in wireless sensor networks (WSNs) using hybrid bio-inspired reinforcement learning approach

Key Points

The research aims to develop an energy-efficient scheduling method for wireless sensor networks using a hybrid approach integrating reinforcement learning and optimization algorithms.
Developed a scheduling method known as RL-HAPSO combining Ant Colony Optimization and Particle Swarm Optimization.
Utilized Q-learning for real-time reinforcement learning to adapt scheduling based on network states.
Conducted multiple simulations to validate performance against existing algorithms and hybrid models without reinforcement learning.
The RL-HAPSO model demonstrates superior optimization costs in execution time and energy usage compared to other algorithms.
Achieved execution times in microseconds for each algorithm with enhanced fault tolerance and coverage.
Adaptively adjusts operations under changing conditions, ensuring robust performance against node failures.

Abstract

There has been huge positive changes in smart infrastructure management due to the creation of systems that perform real time environmental tracking, and process cyber-physical data. These changes are apparent due to the combination of Wireless Sensor Networks (WSNs) with Internet of Things (IoT). Large WSN deployments face obstacles in scheduling, due to restricted energy supplies and operational environments being too hostile or inaccessible. Genetic Algorithms like Simulated Annealing and Artificial Bee Colony tends to perform in suboptimal standards as these algorithms fail to adapt well to environments that have energy depletion along with changing topologies. Hence, a new method for WSN scheduling known as RL-HAPSO, that utilizes Ant Colony Optimization (ACO) and the Particle Swarm Optimization (PSO) algorithms along with the adaptive capability of Q-learning Reinforcement Learning has been addressed in this paper. An energy-efficient node selection by ACO operates during the first phase, followed by PSO optimization, which improves coverage and minimizes redundancy before execution of real-time reinforcement learning algorithm that selects activation schedules based on network states. The model runs multiple simulations, and does performance validation by assessing its execution time and convergence cost along with energy utilization, which is compared to each algorithm independently and the also the hybrid model without RL implementation. Results indicate execution in microseconds interval by each algorithm, yet RL-HAPSO stands out, as it achieves better optimization costs through enhanced fault tolerance, coverage and minimal energy usage. During performance alterations the system automatically adjusts its operations leading to consistent robust behaviour, even in case of node failure and environmental variations. The obtained results indicate that this proposed methodology functions as a viable approach for future-generation IoT applications that support resource-aware and smart WSN scheduling.

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Cite This Study

Sarobin et al. (Wed,) studied this question.

synapsesocial.com/papers/69b4fb1bb39f7826a300ba9b https://doi.org/https://doi.org/10.1038/s41598-026-35077-5

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