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

Jesmine Damilola Omonori, Iyanu Tomiwa Durotola, Godspower Paul Osilama

Abstract

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.

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

Abstract

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.
Paper Structure (31 sections, 31 equations, 10 figures, 2 tables)

This paper contains 31 sections, 31 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work and Comparative Analysis
  3. Methods
  4. Stochastic event generation
  5. Training dataset and sample construction
  6. Neural network predictor
  7. Loss function and gradient-based training
  8. Performance metrics (per node and aggregate)
  9. Event Generation Probability Model
  10. Energy Consumption Model
  11. TDMA Energy Model
  12. EA-TDMA Energy Model
  13. BMA Energy Model
  14. EEI-BMA Model (AI-Assisted Variants)
  15. 1) Predicted Event Probability Mapping
  16. ...and 16 more sections

Figures (10)

  • Figure 1: Per-node probability estimation results: (left) true vs. predicted event-generation probability; (right) node-wise estimation error.
  • Figure 1: Per-node probability estimation results: (left) true vs. predicted event-generation probability; (right) node-wise estimation error.
  • Figure 2: Energy consumption comparison of MAC protocols under varying event-generation probabilities
  • Figure 2: Energy consumption comparison of MAC protocols under varying event-generation probabilities
  • Figure 3: Energy consumption of MAC protocols for varying number of nodes
  • ...and 5 more figures