Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks  
[PERSON]  
Department of Computer Science and Engineering, Chungnam National University,  
Daejeon 34134, Republic of Korea; [EMAIL_ADDRESS] (A.I. ); [EMAIL_ADDRESS] (B.S. );  
structure. These networks are widely employed in applications demanding high adaptability and resilience, including disaster response, military communications, and remote sensing. However, the dynamic and unpredictable nature of MANET environments presents critical challenges, particularly in designing effective and efficient broadcasting mechanisms. These use-cases impose strict demands on broadcasting protocols, including the need to maintain reliable coverage despite rapid node mobility, limited energy availability, and fluctuating communication priorities. Our proposed adaptive broadcasting methods directly address these challenges. Method A's reinforcement learning-based adjustment allows for the system to progressively improve decision-making in evolving network conditions typical of prolonged emergency operations. Meanwhile, Method B's profile switching mechanism supports swift adaptation to sudden shifts, such as transitions between energy-saving modes and delay-sensitive transmissions, which are common in battlefield or crisis scenarios. These capabilities ensure that broadcasting strategies remain effective without centralized control, making the approach suitable for decentralized, resource-constrained MANET deployments under practical operational constraints. A central limitation in existing broadcasting schemes is their reliance on static decision-making frameworks, such as fixed fuzzy logic tables and immutable membership functions. While fuzzy logic-based broadcasting schemes have been successful in addressing the uncertainties inherent in MANETs, their static nature restricts their ability to adapt to rapidly changing network conditions, such as variations in node density, mobility, or signal quality. Static configurations often fail to capture real-time network states accurately, leading to suboptimal decisions that either cause excessive redundant transmissions or insufficient coverage. To defeat these deficiencies, in our previous research [1], we proposed a dynamic fuzzy logic-based broadcasting scheme that combined reinforcement learning techniques, specifically Temporal Difference (TD) learning and Monte Carlo (MC) simulations, with adaptive membership function adjustments. This approach dynamically modified membership functions based on key network parameters such as topology density, Connection Time of nodes, and RSSI. In addition, reinforcement learning was employed to refine the broadcasting ratio and optimize the fuzzy rule base through a reward and punishment mechanism, enabling the system to learn and adapt over time. The method demonstrated significant improvements in reducing redundant broadcasts, minimizing collisions, and enhancing message delivery reliability compared to static fuzzy logic approaches. The main limitation of our previous work was network overhead, resulting in increased resource requirements on the network and a slight delay. However, the iterative learning process of the RL algorithms proved to be very efficient at increasingly adapting to the network. The selection of parameters for Model A, such as Connection Time (\(CT\)), Number of Neighbors (\(NN\)), and Signal Strength (\(SS\)), and combinations for Model B, e.g., \(\{NN,\ E_{\text{res}}\}\) for energy-based profiles or \(\{D_{\text{tol}},\ CT\}\) for delay-sensitive profiles, was based on their demonstrated impact on MANET performance in prior studies, including our previous works [1; 2] and those cited in the Related Work section. Unlike many fuzzy-based approaches that consider overly broad parameter sets, often resulting in redundant packets, we focus on a concise set of impactful parameters that reduce Redundancy and computational overhead.