http://www.sciepub.com/journal/JCN Journal of Computer Networks is a peer-reviewed, open access journal that provides rapid publication of articles in all areas of computer networks. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of computer networks. Science and Education Publishing en 2013 Science and Education Publishing Co. Ltd All rights reserved. Journal of Computer Networks 13 1 January 2025 2013 Science and Education Publishing Co. Ltd All rights reserved. http://pubs.sciepub.com/jcn/13/1/1 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.]]> Iven Aabaah, Valentine Aveyom, Solomon Anab, Seth Gyamerah 2026-06-03 Science and Education Publishing 2026-06-03 1 13 1 15 10.12691/jcn-13-1-1