An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches

Mittal, Mohit, Kobielnik, Martyna, Gupta, Swadha, Cheng, Xiaochun ORCID logoORCID: and Wozniak, Marcin (2022) An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches. Journal of Cloud Computing, 11 (1) , 70. pp. 1-21. ISSN 2192-113X [Article] (doi:10.1186/s13677-022-00344-z)

PDF - Published version (with publisher's formatting)
Available under License Creative Commons Attribution 4.0.

Download (3MB) | Preview


Wireless sensor network (WSN) is widely acceptable communication network where human-intervention is less. Another prominent factors are cheap in cost and covers huge area of field for communication. WSN as name suggests sensor nodes are present which communicate to the neighboring node to form a network. These nodes are communicate via radio signals and equipped with battery which is one of most challenge in these networks. The battery consumption is depend on weather where sensors are deployed, routing protocols etc. To reduce the battery at routing level various quality of services (QoS) parameters are available to measure the performance of the network. To overcome this problem, many routing protocol has been proposed. In this paper, we considered two energy efficient protocols i.e. LEACH and Sub-cluster LEACH protocols. For provision of better performance of network Levenberg-Marquardt neural network (LMNN) and Moth-Flame optimisation both are implemented one by one. QoS parameters considered to measure the performance are energy efficiency, end-to-end delay, Throughput and Packet delivery ratio (PDR). After implementation, simulation results show that Sub-cluster LEACH with MFO is outperforms among other algorithms.Along with this, second part of paper considered to anomaly detection based on machine learning algorithms such as SVM, KNN and LR. NSLKDD dataset is considered and than proposed the anomaly detection method.Simulation results shows that proposed method with SVM provide better results among others.

Item Type: Article
Keywords (uncontrolled): LEACH, Quality of services, Soft computing techniques, Moth-Flame optimisation, Anomaly detection
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 36670
Notes on copyright: © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 31 Oct 2022 15:08
Last Modified: 07 Dec 2022 18:06

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.