In this work, we considered the problem of anomaly detection in next-generation cyber-physical systems (NG-CPS). For this, we used a double deep Q-network-enabled framework, where an agent was trained to detect anomalies in the traffic that does not match the behavior of the legitimate traffic at the end side. Furthermore, the proposed paradigm recognizes known and unknown anomalies by directly engaging with a simulation environment. Given that, it progressively develops its interpretation of anomalies to encompass new, previously unrecognized classes of anomalies by proactively exploring probable anomalies in data that have not been labeled. The method achieves this by concurrently optimizing the use of a limited amount of labeled abnormality data for better understanding (exploitation) and the identification of infrequent, unlabeled anomalies (exploration). During analysis, we observed that the proposed model achieves significant results in the context of average and greedy catching of anomalies in the presence of comparative models.

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.