Energy-efficient medium access control (MAC) protocols remain critical in resource-constrained Wireless Sensor Networks (WSNs) and IoT deployments, especially under mixed traffic patterns that combine event-driven and continuous monitoring operations. Traditional Time Division Multiple 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. Simulation results obtained in the MATLAB environment 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.

翻译：在资源受限的无线传感器网络（WSN）和物联网部署中，尤其是在结合事件驱动与持续监测操作的混合流量模式下，高能效的介质访问控制（MAC）协议仍然至关重要。传统的基于时分多址（TDMA）和位图辅助（BMA）的MAC协议无法根据传感器活动的时空变化调整其工作周期，导致不必要的无线电唤醒和能耗增加。为应对这一局限，本文提出EEI-BMA——一种AI辅助、事件概率感知的MAC协议，其利用基于轻量级神经网络的事件预测动态调整传输调度。该框架融合了节点级概率估计、自适应时隙激活和选择性信道接入，在保持感知可靠性的同时降低收发器活动。在MATLAB环境中获得的仿真结果表明，在不同节点密度、数据包大小、事件生成概率和持续监测负载下，EEI-BMA（最佳预测）相比传统TDMA能耗降低35–45%，相比能量感知TDMA节省22–30%，相比传统BMA提升18–28%。即使在预测不完美的情况下，EEI-BMA也持续优于所有基线协议，展现出强大的鲁棒性。结果证实，预测引导的MAC调度是面向下一代低功耗WSN与物联网系统的一种高效策略。