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<p>Nowadays, Wireless sensor Networks play a significant role in making the day-to-day services like healthcare, military, etc. easier. Still, it is exposed to different attacks, which lead to increases in network delay, consume very high energy and also reduce the throughput. Usually, routing can be done in WSNs using protocols such as Distributed Energy-Efficient Clustering (DEEC) and low-energy adaptive clustering hierarchy (LEACH). These kinds of protocols are not secure because, in some situations few nodes behave maliciously. To overcome this situation in the proposed system, four Deep Learning techniques and a Blockchain Security (BCS) Scheme are used to identify the activities of malicious nodes. Furthermore, dataset like WSN-DS 2016 is used to train the Deep Learning Models. The cluster node sends the data to the Base station, which the Sensor Node sent. In the proposed system, blockchain is installed in both the Cluster Head and the Base Station to identify the malicious behaviour of the nodes. Furthermore, BCS and Deep Learning Techniques are used to remove the malicious node. The registration of the Legal Nodes can be done in the Blockchain network. Moreover, further data transfer can be done between the Legitimate node using DEEC and LEACH protocols, and also with various protocols, and a comparison can be made. The security analysis for the smart contract can be done using Oyente, which indicates that the blockchain network is strong against susceptibility.</p>

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Blockchain-enabled deep learning techniques for providing security in wireless sensor networks

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • P. J. Beslin Pajila,
  • Y. Harold Robinson,
  • J. A. Jevin

摘要

Nowadays, Wireless sensor Networks play a significant role in making the day-to-day services like healthcare, military, etc. easier. Still, it is exposed to different attacks, which lead to increases in network delay, consume very high energy and also reduce the throughput. Usually, routing can be done in WSNs using protocols such as Distributed Energy-Efficient Clustering (DEEC) and low-energy adaptive clustering hierarchy (LEACH). These kinds of protocols are not secure because, in some situations few nodes behave maliciously. To overcome this situation in the proposed system, four Deep Learning techniques and a Blockchain Security (BCS) Scheme are used to identify the activities of malicious nodes. Furthermore, dataset like WSN-DS 2016 is used to train the Deep Learning Models. The cluster node sends the data to the Base station, which the Sensor Node sent. In the proposed system, blockchain is installed in both the Cluster Head and the Base Station to identify the malicious behaviour of the nodes. Furthermore, BCS and Deep Learning Techniques are used to remove the malicious node. The registration of the Legal Nodes can be done in the Blockchain network. Moreover, further data transfer can be done between the Legitimate node using DEEC and LEACH protocols, and also with various protocols, and a comparison can be made. The security analysis for the smart contract can be done using Oyente, which indicates that the blockchain network is strong against susceptibility.