AI-Enabled Bit-Mapping Medium Access Control Protocol for Intelligent and Energy-Efficient IoT Networks

Energy-efficient medium access control (MAC) protocol remains a critical challenge in resource-constrained Wireless Sensor Networks and IoT deployments, especially under mixed traffic patterns combining event-driven and continuous monitoring operations. The traditional Time Division Access (TDMA)- and Bit Map Assisted (BMA)-based MAC protocols fail to adapt their duty cycles to spatiotemporal variations in sensor activity, resulting in unnecessary radio wake-ups and increased energy expenditure. To address this limitation, this paper proposes EEI-BMA, an AI-assisted, event-probability-aware MAC protocol that dynamically adjusts transmission scheduling using lightweight neural-network-based event prediction. The proposed framework incorporates per-node probability estimation, adaptive slot activation, and selective channel access to reduce transceiver activity while preserving sensing reliability. MATLAB simulation environment is modeled for corresponding parameters show that EEI–BMA (Best Prediction) achieves 35–45% lower energy consumption than Traditional– TDMA, 22–30% savings compared with Energy-Aware TDMA, and 18–28% improvement over Traditional–BMA across varying node densities, packet sizes, event-generation probabilities, and continuous monitoring loads. Even with imperfect prediction, EEI–BMA consistently outperforms all baseline protocols, demonstrating strong robustness. The results confirm that prediction-guided MAC scheduling is a highly effective strategy for next-generation low-power WSNs and IoT systems.