Nimisha Rajput1,#, Amit Kumar1, Raghavendra Pal1,#, Nishu Gupta2,*, Mikko Uitto2, Jukka Mäkelä2
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3839-3859, 2025, DOI:10.32604/cmes.2025.065903
- 30 June 2025
Abstract Wireless Sensor Networks (WSNs) play a critical role in automated border surveillance systems, where continuous monitoring is essential. However, limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time. To address this issue, this paper presents an innovative energy-efficient protocol based on deep Q-learning (DQN), specifically developed to prolong the operational lifespan of WSNs used in border surveillance. By harnessing the adaptive power of DQN, the proposed protocol dynamically adjusts node activity and communication patterns. This approach ensures optimal energy usage while maintaining high coverage, connectivity, and data accuracy. More >
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