Abstract

Military deployment of Wireless Sensor Networks (WSNs) demands high security, communication facilities, and energy efficiency. Failing to provide secure and dynamic data transmission is a major challenge in such networks because there are no centralized base stations, an open distributed architecture, and various security threats. The research introduces a deep learning based secure routing infrastructure to blockchain-enabled autonomous military WSN that focuses on decentralized trust and flexible intrusion detection (ID). The WSN-DS data was used to analyze the research. For ID, a Seven-Spot Ladybird Optimization Enhanced Bidirectional Long Short-Term Memory with Attention Mechanism (SSLO-Bi-LSTM-ATT) model is applied. The Blockchain Enabled Secured Routing Protocol (BESRP) ensures trustworthy routing transactions by combining decentralized blockchain authentication with Bi-LSTM-ATT-driven anomaly detection. To authenticate node-to-node transfers, the protocol creates dynamic lightweight blockchains, lowering energy overhead and ensuring data confidentiality and integrity. By integrating the proposed structure of the proposed structure, energy consumption reduces and optimizes routing performance by integrating the proposed structure, by integrating the BI-LSTM-Att for decentralized authentication and BI-LSTM-night. Simulation results show that the proposed system improves existing approaches when it comes to the use of low energy, reduces package loss and improvement in throwing. Overall, the integration of deep learning with blockchain in the autonomous military WSN provides a promising approach to ensure safe operations, and addresses important defense requirements.

Keywords

Wireless Sensor Networks, Military Sensor Networks, Blockchain, Secured Routing, Intrusion Dete, a Seven-Spot Ladybird Optimization Enhanced Bidirectional Long Short-Term Memory with Attention Mechanism (SSLO-Bi-LSTM-ATT),

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