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In This Issue
Scaling Effects on AI Fairness: An Empirical Analysis of Stereotypical Bias in State-of-the-Art Transformer-Based Models
Dr. Selvanayaki Kolandapalayam Shanmugam
Assessing Judicial Independence and Its Impact on Democratic Consolidation in Nigeria: The Case of The Fourth Republic
OMOREGIE Edoghogho
Optimized Xception Deep Learning Model for Automated Skin Disease Classification in Scalable Healthcare Systems
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A Comprehensive Survey on Deep Learning-based Techniques for Tomato Leaves Disease Detection and Classification
Jagisha
State-of-the-Art Survey on Intelligent Energy-Aware Routing Algorithms in Wireless Sensor Networks
by Prof. Bhavini Parmar , Prof. Sohilkumar Dabhi , Prof. Keyur Patel , Prof. Vasim Vohra , Kinjalben B. Dabhi
International Journal of Technology & Emerging Research 2025 , 1 (6) , 52–57
10.64823/ijter.2506005Abstract
Wireless Sensor Networks (WSNs) are vital for applications such as environmental monitoring and industrial automation, yet their limited energy resources and dynamic environments challenge network longevity and data reliability. Artificial Intelligence (AI) offers effective solutions through adaptive, energy-aware routing strategies. This paper investigates AI-based routing techniques—including deep reinforcement learning, fuzzy logic, swarm intelligence, and hybrid meta-heuristics—for dynamic path optimization in WSNs. These methods enable sensor nodes to make context-aware decisions based on factors like residual energy, link quality, node density, and traffic load. We review current state-of-the-art algorithms, conduct comparative performance analysis, and examine trade-offs in energy efficiency, latency, and computational cost. Simulation results demonstrate that AI-driven routing significantly enhances network lifetime and data throughput over traditional approaches. The findings highlight AI’s potential to drive intelligent, scalable, and energy-efficient routing for next-generation IoT-based WSNs.
Keywords: Wireless Sensor Networks (WSN), Energy-aware routing, Intelligent routing algorithms, Machine learning, Network lifetime, Optimization techniques,Artificial intelligence (AI),QoS (Quality of Service).
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