AI-Driven Adaptive Modulation for Enhancing Efficiency in Wireless Sensor Networks (WSNs)

Iven Aabaah^1, , Valentine Aveyom², Solomon Anab³ and Seth Gyamerah²

¹Department of Information Systems and Technology, University of Technology and Applied Sciences, Navrongo, Ghana

²Department of Computer Science, University of Technology and Applied Sciences, Navrongo, Ghana

³Department of Computer Science, Koforidua Technical University, Koforidua, Ghana

Cite this paper:
Iven Aabaah, Valentine Aveyom, Solomon Anab and Seth Gyamerah. AI-Driven Adaptive Modulation for Enhancing Efficiency in Wireless Sensor Networks (WSNs). Journal of Computer Networks. 2025; 13(1):1-15. doi: 10.12691/jcn-13-1-1

Abstract

Wireless Sensor Networks (WSNs) are a major development in communication and distributed sensing systems. A wireless sensor network (WSN) consists of numerous tiny, inexpensive, low-power devices called sensor nodes that monitor environmental or physical parameters, including temperature, pressure, humidity, motion, and pollution. Together, these sensor nodes gather, process, and wirelessly transmit data to a central location, often called a base station or sink node, for further analysis. Wireless sensor networks (WSNs) are increasingly used in dynamic, resource-constrained settings, where it remains very difficult to use energy and communication resources effectively. This paper presents an AI-driven adaptive modulation system that improves network performance, reliability, and energy efficiency. The proposed approach combines residual energy monitoring, channel-aware adaptation, and reinforcement learning-based decision-making to dynamically select the optimal modulation schemes under changing channel conditions. Modulation levels can be intelligently and independently selected based on node energy status, link quality, and signal-to-noise ratio (SNR) by defining the adaptive modulation problem as a Markov Decision Process (MDP). A thorough mathematical model that accounts for bit-error rate (BER) limitations, energy consumption, and wireless channel characteristics is developed to ensure reliable and efficient communication. The framework achieves the best possible balance between energy efficiency and transmission efficiency by optimizing energy use and normalizing throughput. Compared to the conventional static modulation procedure, the proposed method performs significantly better. Comprehensive simulations using MATLAB and NS-3 reveal notable increases in network longevity along with gains of up to 20–35% in energy efficiency, 15–25% in packet delivery ratio, 20–30% in latency reduction, and 25–35% in throughput. These findings show that AI-driven adaptive modulation provides a scalable and dependable solution for next-generation WSNs, enabling intelligent resource management and sustainable network operation in dynamic communication scenarios.

Keywords:
wireless sensor networks energy-efficiency artificial intelligence adaptive modulation

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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