Energy State Sensing for Robust MAC Protocol Identification in Underwater Acoustic Networks

In underwater acoustic networks (UANs), identifying Medium Access Control (MAC) protocols is essential for non-cooperative network discovery and heterogeneous communication. The complex ocean environment, characterized by low SNR, non-Gaussian noise, long delays, and spatiotemporal uncertainty, presents significant challenges for effective MAC protocol identification. To overcome these issues, we present a signal observation model for UANs that incorporates underwater acoustic channel characteristics and MAC protocol information. By defining the UAN energy state and analyzing its consistency during signal transmission, we propose an Energy State Sensing (ESS) approach for MAC protocol identification. ESS captures UAN events by converting observation signals into energy state time series, enabling statistical extraction of distinctive MAC protocol characteristics, including slot and collision features. To address the temporal irregularity of packet arrivals in equal time slot protocols, we derive and analyze quasi-periodic slot characteristics in UANs, proposing an ESS-based quasi-periodic slot feature estimation method. Finally, a comprehensive ESS-based UAN MAC protocol identification feature set is developed. Numerical simulations demonstrate the effectiveness and robustness of ESS in accurately identifying three typical MAC protocols, achieving an F1-score above 95% even at SNR=0 in non-Gaussian noise, highlighting its potential to efficiently address UAN challenges.