Open Access

ARTICLE

Deep Q-Learning Driven Protocol for Enhanced Border Surveillance with Extended Wireless Sensor Network Lifespan

Nimisha Rajput^1,#, Amit Kumar¹, Raghavendra Pal^1,#, Nishu Gupta^2,*, Mikko Uitto², Jukka Mäkelä²

1 Department of Electronics Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, 395007, Gujrat, India
2 Future Communication Networks, VTT Technical Research Centre of Finland Ltd., Oulu, 90590, Finland

* Corresponding Author: Nishu Gupta. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)

Computer Modeling in Engineering & Sciences 2025, 143(3), 3839-3859. https://doi.org/10.32604/cmes.2025.065903

Received 24 March 2025; Accepted 23 May 2025; Issue published 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. The proposed system is modeled with 100 sensor nodes deployed over a 1000 m 1000 m area, featuring a strategically positioned sink node. Our method outperforms traditional approaches, achieving significant enhancements in network lifetime and energy utilization. Through extensive simulations, it is observed that the network lifetime increases by 9.75%, throughput increases by 8.85% and average delay decreases by 9.45% in comparison to the similar recent protocols. It demonstrates the robustness and efficiency of our protocol in real-world scenarios, highlighting its potential to revolutionize border surveillance operations.

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Keywords

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