Open Access


Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

Mohamed Ali1, Ibrahim A. Abd El-Moghith2, Mohamed N. El-Derini3, Saad M. Darwish2,*
1 Higher Institute for Tourism, Hotels and Computer, Al-Seyouf, Alexandria, 21533, Egypt
2 Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 21526, Egypt
3 Department of Computer and Systems Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt
* Corresponding Author: Saad M. Darwish. Email:

Computers, Materials & Continua 2022, 70(3), 6127-6140.

Received 29 June 2021; Accepted 23 August 2021; Issue published 11 October 2021


Routing is a key function in Wireless Sensor Networks (WSNs) since it facilitates data transfer to base stations. Routing attacks have the potential to destroy and degrade the functionality of WSNs. A trustworthy routing system is essential for routing security and WSN efficiency. Numerous methods have been implemented to build trust between routing nodes, including the use of cryptographic methods and centralized routing. Nonetheless, the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities. At the moment, there is no effective way to avoid malicious node attacks. As a consequence of these concerns, this paper proposes a trusted routing technique that combines blockchain infrastructure, deep neural networks, and Markov Decision Processes (MDPs) to improve the security and efficiency of WSN routing. To authenticate the transmission process, the suggested methodology makes use of a Proof of Authority (PoA) mechanism inside the blockchain network. The validation group required for proofing is chosen using a deep learning approach that prioritizes each node's characteristics. MDPs are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting messages. According to testing data, our routing system outperforms current routing algorithms in a 50% malicious node routing scenario.


Wireless sensor networks; trusted routing; deep neural network; blockchain; markov decision

Cite This Article

M. Ali, I. A. Abd El-Moghith, M. N. El-Derini and S. M. Darwish, "Wireless sensor networks routing attacks prevention with blockchain and deep neural network," Computers, Materials & Continua, vol. 70, no.3, pp. 6127–6140, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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