Energy-aware power and rate control in MANETs using adaptive game theory and grey wolf optimization

Inherent resource constraints within Mobile Ad Hoc Networks (MANETs) necessitate resource optimization, specifically power and rate control, as a critical focus for enhancing network performance in terms of energy, throughput, and delay. Although traditional power and rate control mechanisms have successfully improved throughput or energy efficiency, they fail to address the complex trade-offs between delay, energy consumption, and network stability, particularly in highly dynamic or unpredictable networks. Motivated by this, this study introduces a new Dynamic Power-Rate Optimization Grey Wolf Algorithm (DPRO-GWA) mechanism derived from a game-theoretic framework that balances outage probability and residual energy demands to achieve energy efficiency and quality of service (QoS) in mobile ad hoc networks (MANETs). The proposed approach formulates power and rate allocation as a super-modular game, which ensures both the existence and uniqueness of a Nash Equilibrium (NE) as the optimal solution for distributed non-cooperative nodes. We subsequently introduce an Adaptive Grey Wolf Optimizer (AGWO), which enhances the Grey Wolf Optimizer (GWO) by increasing convergence speed through adaptive tuning of the exploration-exploitation trade-off. Extensive simulation results demonstrate that DPRO-GWA significantly outperforms existing algorithms, including the Dynamic Rate and Power Allocation Algorithm (DRPAA), Energy Conserving Power and Rate Control (ECPRC), and Rate-Effective Network Utility Maximization (RENUM) in terms of energy consumption, throughput, and delay. Additionally, the proposed method maximizes the energy-delay trade-off, leading to considerable improvements in the network lifetime and performance, particularly in time-variant and fading channel environments. Thus, this study creates a promising avenue for refining power and rate control protocols for next-generation MANETs.