ANALYSIS OF STRUCTURAL SURVIVABILITY INDICATORS FOR MODULAR ACOUSTIC AIRSPACE MONITORING SYSTEM

In this study, the structural survivability indicator—pairwise connectivity probability—was analyzed. The research object is a modular acoustic airspace monitoring system. As the modeling method, an improved logic-probabilistic trajectory modeling approach was applied. Quantitative survivability values were obtained at the system-engineering design stage, when the system does not yet exist in hardware, by means of modeling. Modern airspace monitoring systems are critical components of information and communication systems, parts of air defense complexes and critical infrastructure. The effectiveness of such complexes largely depends on their structural survivability, i.e., the ability to maintain operability in the event of partial damage to elements or communication channels. Ensuring high survivability indicators is a relevant scientific and applied challenge, since even temporary loss of informational resilience may significantly reduce the overall effectiveness of the complex. A distinctive feature of the studied system is the use of a modular structure that combines centralized stationary GSM nodes with a decentralized mesh network based on smart acoustic sensors communicating via LoRa technology. Such a system design provides scalability and flexibility in data collection and transmission, while at the same time imposing additional requirements for assessing network reliability and identifying optimal solutions at the system-engineering design stage. For quantitative analysis of the structural survivability of the modular acoustic airspace monitoring system, modeling methods are applied to evaluate the probability of preserving connectivity between critical nodes under different conditions. In this work, an improved logic-probabilistic trajectory modeling method was employed, enabling the determination of survivability indicators already at the early stages of design, when the system is not yet implemented in hardware. This makes it possible to identify vulnerabilities in the topology in a timely manner, compare alternative architecture options, and substantiate decisions aimed at improving the reliability and resilience of the system.