Compromise Between Topology Connection, Load Balance and Wireless Sensor Networks’Anomalies (WSN)

The article addresses the challenge of ensuring a compromise between topological connectivity, load balancing, and anomaly detection in Wireless Sensor Networks (WSN). It analyzes current methods and approaches for optimizing the performance of sensor networks under dynamic conditions. The goal of this work is to analyze the interrelationship between these compromise aspects and propose an optimal approach for their integration. The article proposes a combined approach, which enables an optimal trade-off between these characteristics and enhances network interaction efficiency. To ensure the efficient operation of wireless sensor networks (WSN), it is essential to balance three key factors: topological connectivity and stable connectivity, even load distribution and security. Anomaly detection in wireless sensor networks (WSN) is critically important for ensuring their security and reliability. Modern anomaly detection methods include statistical analysis and machine learning techniques. Statistical traffic analysis enables the identification of deviations from normal network behavior, which may indicate anomalies existence. This approach is based on collecting and analyzing network traffic data, such as transmitted data volume, packet frequency, and other parameters. Deviations from established norms can signal potential issues or attacks. Further research in this domain should focus on the development of intelligent algorithms capable of adapting to real-time network changes. The integration of artificial intelligence and machine learning in WSN systems opens new opportunities for improving their efficiency and resilience to environmental changes.