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Time synchronization plays an important role in the scheduling and position technologies of sensor nodes in underwater acoustic networks (UANs). The time synchronization (TS) algorithms face challenges such as high requirements of energy efficiency, the estimation accuracy of the time-varying clock skew and the suppression of the impulsive noise. To achieve accurate time synchronization for UANs, an energy-efficient TS method based on nonlinear clock skew tracking (NCST) is proposed. First, based on the sea trial temperature data and the crystal oscillators’ temperature–frequency characteristics, a nonlinear model is established to characterize the dynamic of clock skews. Second, a single-way communication scheme based on a receiver-only (RO) paradigm is used in the NCST-TS to save limited energy. Meanwhile, impulsive noises are considered during the communication process and the Gaussian mixture model (GMM) is employed to fit receiving timestamp errors caused by non-Gaussian noise. To combat the nonlinear and non-Gaussian problem, the particle filter (PF)-based algorithm is used to track the time-varying clock state and an accurate posterior probability density function under the GMM error model is also given in PF. The simulation results show that under the GMM error model, the accumulative Root Mean Square Errors (RMSE) of NCST-TS can be reduced from 10−4 s to 10−5 s compared with existing protocols. It also outperforms the other TS algorithms in the aspect of energy efficiency.

Multidrug-resistant Acinetobacter baumannii (MDR-AB) infections are becoming increasingly common. The Real-Time Nosocomial Infection (NI) Surveillance System (RT-NISS) was used to monitor MDR-AB NI in intensive care units (ICUs) to prevent NI outbreaks. Therefore, the RT-NISS was used in the current study to monitor MDR-AB infections in a neurosurgery ICU. Clinical interventions, including recommended antibiotics, bacterial distribution in the patient analysis, and bed adjustments, were carried out based on the monitoring results. The RT-NISS was also used to monitor clinical data, implement, and provide training on NI control. The RT-NISS detected a potential cluster of XDR-AB when five patients admitted to the neurosurgery ICU were tested positive for AB between 11 and 17 June 2019. Only two infected cases originated in the hospital, and there was no NI outbreak. The hospital Infection Control Department took appropriate measures to prevent cross-infection; specifically, an epidemiologic investigation and environmental assessment were conducted, and NI prevention and outbreak management training was provided. In summary, the RT-NISS enhanced the timeliness and efficacy of NI control and surveillance in a neurosurgery ICU. In order to prevent NI outbreaks, the Real-Time Nosocomial Infection (NI) Surveillance System (RT-NISS) was employed to monitor MDR-AB NI in critical care units (ICU). Based on the monitoring data, clinical actions such as required antibiotics, bacterial distribution in the patient analysis, and bed changes were carried out. In a neurosurgery ICU, the RT-NISS improved the timeliness and efficacy of NI control and surveillance.