Vol.32, No.2, 2022, pp.963-978, doi:10.32604/iasc.2022.022126
OPEN ACCESS
ARTICLE
Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks
  • S. Premkumar Deepak*, M. B. Mukeshkrishnan
SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India
* Corresponding Author: S. Premkumar Deepak. Email:
(This article belongs to this Special Issue: AI powered Blockchain-Enabled privacy protected 5G Networks and Beyond)
Received 28 July 2021; Accepted 30 August 2021; Issue published 17 November 2021
Abstract
Underwater wireless sensor networks (UWSNs) are promising, emerging technologies for the applications in oceanic research. UWSN contains high number of sensor nodes and autonomous underwater vehicles that are deployed to perform the data transmission in the sea. In UWSN networks, the sensors are placed in the buoyant which are highly vulnerable to selfish behavioural attack. In this paper, the major challenges in finding secure and optimal route navigation in UWSN are identified and in order to address them, Entropy based ACO algorithm (E-ACO) is proposed for secure route selection. Moreover, the Selfish Node Recovery (SNR) using the Grasshopper Optimisation Algorithm (GOA) is used to minimize the packet loss in the UWSN. The performance of the proposed E-ACO method is compared with existing routing methods such as Secure Authentication with Protected Data Aggregation (SAPDA), Secure Energy Efficient and Cooperative Routing (SEECR), Fault Resilient Routing using Moth Flame Optimization (FRR-MFO) and Improved ACO (IACO) method. The packet delivery ratio of the proposed E-ACO with 500 nodes is 0.89 which is higher than other existing methods such as SAPDA, SEECR, FRR-MFO and IACO.
Keywords
Entropy based ant colony optimization; grasshopper optimisation algorithm; packet loss; secure routing; selfish node recovery; underwater wireless sensor network
Cite This Article
Deepak, S. P., Mukeshkrishnan, M. B. (2022). Secured Route Selection Using E-ACO in Underwater Wireless Sensor Networks. Intelligent Automation & Soft Computing, 32(2), 963–978.
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.