Statistical Verification of Medium-Access Parameterization for Power-Grid Edge Ad Hoc Sensor Networks

2026-02-05

AI Summary

This work addresses the challenge of selfish node behavior in power-grid edge ad hoc networks, where autonomous tuning of CSMA/CA parameters undermines reliability and energy efficiency, failing to meet stringent grid requirements. The paper proposes the first formal verification framework that integrates stochastic timed hybrid automata with statistical model checking augmented by confidence bounds, enabling rigorous analysis of medium access strategies in asynchronous, event-driven, and resource-constrained settings. By combining temporal logic specifications with Nash equilibrium reasoning, the approach supports large-scale evaluation of protocol configurations and certifies equilibrium strategies robust against unilateral deviations. Evaluated in a substation scenario, the certified strategy improves utility from 0.862 to 0.914 and packet delivery ratio from 89.5% to 93.2%, while satisfying strict constraints on latency, reliability, and energy efficiency—achieving a robustness coefficient exceeding 0.97 and reducing per-cycle energy consumption.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Adversarial Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

The widespread deployment of power grid ad hoc sensor networks based on IEEE 802.15.4 raises reliability challenges when nodes selfishly adapt CSMA/CA parameters to maximize individual performance. Such behavior degrades reliability, energy efficiency, and compliance with strict grid constraints. Existing analytical and simulation approaches often fail to rigorously evaluate configurations under asynchronous, event-driven, and resource-limited conditions. We develop a verification framework that integrates stochastic timed hybrid automata with statistical model checking (SMC) with confidence bounds to formally assess CSMA/CA parameterizations under grid workloads. By encoding node- and system-level objectives in temporal logic and automating protocol screening via large-scale statistical evaluation, the method certifies Nash equilibrium strategies that remain robust to unilateral deviations. In a substation-scale scenario, the certified equilibrium improves utility from 0.862 to 0.914 and raises the delivery ratio from 89.5% to 93.2% when compared with an aggressive tuning baseline. Against a delivery-oriented baseline, it reduces mean per-cycle energy from 152.8 mJ to 149.2 mJ while maintaining comparable delivery performance. Certified configurations satisfy latency, reliability, and energy constraints with robustness coefficients above 0.97 and utility above 0.91.

Problem

• CSMA/CA parameterization
• power-grid sensor networks
• medium-access control
• reliability
• energy efficiency

Innovation

• statistical model checking
• stochastic timed hybrid automata
• CSMA/CA parameterization
• Nash equilibrium certification
• power-grid edge sensor networks