A Resilient Framework for Industry 5.0 WSNs: Enhancing Network Lifetime via a Lightweight Reputation Ledger and Hybrid AI

Wireless Sensor Networks (WSNs) play an increasingly important role in Industry 5.0 cyber–physical systems, where resilience, trust, and energy efficiency are essential under dynamic operating conditions. However, their limited resources, scattered deployment, and continuous operation make these networks highly susceptible to unusual behavior and cyberattacks. Such issues can compromise data quality, disrupt network reliability, and shorten the overall lifespan of the system. To address these challenges, this study examines WSN resilience as a combined problem of anomaly detection accuracy, fault isolation latency, and network lifetime under realistic fault and energy constraints. At the core of the framework is a Model Context Protocol (MCP), which combines a supervised LightGBM classifier with an unsupervised LSTM autoencoder to capture both event-driven and temporal anomalies in sensor data. Complementing this is a compact “Micro-Ledger” system that updates trust values for each node by monitoring behavior and using streamlined consensus rules. Together, they create a continuous feedback mechanism that isolates suspicious nodes while keeping energy consumption in check. The framework is evaluated using a set of resilience-oriented metrics, including fault detection latency, Mean Time To Failure (MTTF), reputation convergence behavior, and overall network lifetime. Experiments conducted in a Digital Twin simulation environment report an F1-score of 0.997, an 18.7% improvement in network lifetime, and a Micro-Ledger storage overhead of approximately 98 KB. While the current validation is simulation-based, the proposed design can be extended to physical deployments through adaptive trust weighting, cluster-head redundancy, and probation-based node reintegration.

@article{N2025,
  title     = {A Resilient Framework for Industry 5.0 WSNs: Enhancing Network Lifetime via a Lightweight Reputation Ledger and Hybrid AI},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Padma Sree N and Malini M Patil},
  doi       = {10.14569/IJACSA.2025.0161242},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161242}
}