Identification of WSN Compromised Nodes and Performance Degradation Prevention using Cooperative Cache

Abstract

Wireless Sensor Networks (WSNs) are critical in a variety of applications such as environmental monitoring, surveillance, and healthcare. WSNs, on the other hand, are vulnerable to assaults due to their dispersed and resource-constrained nature, resulting in compromised nodes and performance deterioration. Using network characteristics analysis and cooperative cache management with data and process affinity, this study provides a unique way to identifying compromised WSN nodes and mitigating performance deterioration. The initial portion of our suggested technique focuses on finding compromised nodes using in-depth network characteristics research. This suggested system can precisely locate probable hacked nodes by monitoring metrics such as traffic patterns, node activity, and communication abnormalities. Machine learning algorithms improve the identification process by learning and evolving adaptively to identify new assault patterns. Once affected nodes have been discovered, the second component of this suggested solution leverages cooperative cache management to prevent performance deterioration. Data and process affinity is presented, in which nearby nodes share caches to effectively store and retrieve data relevant to their processing activities. This affinity-based cache collaboration increases data availability, decreases communication costs, and improves overall network performance.