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🔥 Content Introduction
Wireless Sensor Networks (WSN) consist of numerous sensor nodes deployed in monitoring areas, which collaborate through wireless communication to perform tasks such as data collection, transmission, and processing. They have wide applications in environmental monitoring, intelligent transportation, and industrial monitoring.
However, sensor nodes are typically powered by batteries, which are limited and difficult to replace. Energy efficiency becomes a key factor that restricts the performance and lifespan of WSNs. Operations such as sensing, communication, and data processing consume energy, with communication being particularly energy-intensive. Optimizing node behavior to reduce unnecessary energy consumption and extend network lifespan is one of the core issues in WSN research.
Reinforcement learning learns optimal strategies through the interaction of agents with their environment, making it suitable for the dynamically changing WSN environment. The SARSA algorithm, as an online temporal difference learning algorithm, can consider the next action during the learning process, focusing more on the safety and stability of the strategy, making it suitable for optimizing energy efficiency in WSNs. By training models with the SARSA algorithm, sensor nodes can autonomously adjust communication radius, sampling frequency, and data transmission strategies, significantly improving network energy efficiency and holding important theoretical and practical application value.
II. Principles of the SARSA Algorithm
III. Design of Energy Efficiency Optimization Model for WSN Based on SARSA
IV. Time-Dependent Model Training Process
V. Conclusion and Outlook
(1) Research Conclusion
This paper applies the SARSA algorithm to optimize energy efficiency in wireless sensor networks. By defining reasonable state space, action space, and reward function, sensor nodes can autonomously learn energy-saving strategies through interaction with the environment. Experimental results show that after time-dependent training, the model can effectively reduce node energy consumption and extend network lifespan, with strategy stability superior to Q-learning and other algorithms, validating the effectiveness of SARSA in optimizing energy efficiency in WSNs.
(2) Future Outlook
⛳️ Operating Results
🔗 References
[1] Wang Xiaokang, Ji Jie, Liu Yang, et al. Path Planning for Unmanned Logistics Delivery Vehicles Based on Improved Q-Learning Algorithm [J]. Journal of System Simulation, 2024, 36(5):1211-1221. DOI:10.16182/j.issn1004731x.joss.23-0051.
[2] Ouyang Daliang. Research on Antenna Selection Based on Reinforcement Learning in Wireless Communication [D]. Huaqiao University, 2020.
[3] Li Mingyu. Resource Allocation of Energy Harvesting MQAM Wireless Communication Systems Based on Reinforcement Learning [D]. Jilin University, 2019.
📣 Partial Code
🎈 Some theoretical references are from network literature; please contact the author for removal if there is any infringement.
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