## 3 Technologies Supporting Intelligent Acoustic Monitoring
### Integration with Other Technologies  
Combining IoT technology with 5G networks, cloud computing, and edge computing systems enhances the capabilities of systems that use acoustic sensors. Network infrastructure development and edge-device computational power enable real-time sound signal analysis and transmission, essential for environmental monitoring, security systems, and industrial fault detection applications [85; 86].  
The IoT connects with audio systems through distributed acoustic sensor networks, which gather data across multiple locations before sending them to central processing systems. The network comprises MEMS microphones in mobile devices and smart speakers, alongside advanced monitoring systems, including hydrophones for underwater use and ultrasonic sensors for industrial applications. Such solutions require sensor synchronisation and calibration as the main challenge to achieve data consistency for accurate sound analysis. Before sending this information to relevant authorities, security systems use distributed microphones to generate real-time acoustic maps that detect disturbing sounds, including gunshots, explosions, and streams. For example, a similar approach is used in environmental acoustic monitoring to track animal populations based on their vocalisations [87; 88].  
The transmission and processing of signals in real time are made possible by modern network technologies and computational solutions. A key role here is played by the low latency in data transmission provided by 5G networks. In traditional IoT systems, audio data were typically processed in the cloud, necessitating the transmission of large quantities of data to remote servers. However, contemporary solutions increasingly utilise edge computing, which involves processing data directly on edge devices or local network nodes. Thanks to this, it is possible to perform preliminary filtering, analysis, and classification of sounds before sending them to the central system, which reduces bandwidth requirements and speeds up the response of control systems [89; 90].  
Cloud and edge computing operate as complementary technologies that process data in different ways. Cloud computing provides extensive computational power, making it suitable for permanently analysing extensive datasets, including recording, archiving, and AI algorithm applications. The local data processing capability of edge computing makes it suitable for applications that need low latency, such as smart alarm systems and speech recognition in mobile devices. The organisation uses a combined system that performs elementary processing tasks at network edges and advanced operations within a cloud infrastructure [91, 92].  
Implementing 5G connectivity is crucial for IoT acoustic systems, as it enables fast data transfer and low latency. The immediate alarm and recording transmission capability of 5G security systems enhances threat detection effectiveness. Through 5G technology in smart cities, noise detection systems and real-time acoustic environment quality monitoring have become possible. The industrial application of 5G technology enables the more precise control of robot sound sensors for machine fault diagnosis through operational sound analysis [93, 94].  
The selection between cloud computing and edge AI for audio data processing depends on the application requirements. Edge AI outperforms cloud-based systems for speech recognition and alarm monitoring because it enables data analysis directly at the local level, allowing for faster and more efficient processing. On the other hand, in cases where long-term analysis and result accuracy are crucial, such as in advanced acoustic anomaly detection systems, cloud processing offers better computational capabilities and access to advanced machine learning algorithms. A hybrid approach, combining the cloud's computational power with the speed of edge AI, seems to be the optimal solution for most IoT systems related to sound analysis [95].