Study proposes EBGT for smarter IoT power control

Uplink power control in dense IoT networks is a brutal puzzle: devices lack full channel info, interfere with each other, and can't afford heavy feedback. Enter EBGT—an epistemology-aided Bayesian game-theoretic framework that decentralizes power minimization for stochastically distributed IoT tiers. It models interfering users as a Poisson point process and gives each device a two-layer belief hierarchy—inter-epistemic reasoning about opponents plus intra-epistemic self-assessment—so transmit-power equilibrium emerges without repeated inter-node feedback. The authors derive a closed-form coverage-probability payoff via stochastic geometry and use Jensen–Shannon divergence to track belief convergence. Monte-Carlo simulations confirm the analytical coverage expressions: EBGT holds target coverage while reducing transmit power relative to fixed power control and stochastic non-cooperative power control baselines—especially under stringent SINR and high-density regimes.