Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks

Mohit Mittal, Rocío Pérez de Prado, Yukiko Kawai, Shinsuke Nakajima and José E. Muñoz-Expósito
Additional contact information
Mohit Mittal: Centre de Recherche en Informatique, Signal et Automatique de Lille, INRIA, 59655 Villeneuve-d’Ascq, France
Rocío Pérez de Prado: Telecommunication Engineering Department, University of Jaén, 23071 Jaén, Spain
Yukiko Kawai: Department of Information Science and Engineering, Kyoto Sangyo University, Kamingamo, Kita-ku, Kyoto 603-8555, Japan
Shinsuke Nakajima: Department of Information Science and Engineering, Kyoto Sangyo University, Kamingamo, Kita-ku, Kyoto 603-8555, Japan
José E. Muñoz-Expósito: Telecommunication Engineering Department, University of Jaén, 23071 Jaén, Spain

Energies, 2021, vol. 14, issue 11, 1-21

Abstract: Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy–Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg–Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub-cluster LEACH-derived protocol is also proposed. Simulation results show that the Sub-LEACH with LMNN outperformed its competitors in energy efficiency. In addition, the end-to-end delay was evaluated, and Sub-LEACH protocol proved to be the best among existing strategies. Moreover, an intrusion detection system (IDS) has been proposed for anomaly detection based on the support vector machine (SVM) approach for optimal feature selection. Results showed a 96.15% accuracy—again outperforming existing IDS models. Therefore, satisfactory results in terms of energy efficiency, end-to-end delay and anomaly detection analysis were attained.

Keywords: LEACH protocol; EESR protocol; neural networks; support vector machine; energy efficiency; end-to-end delay; intrusion detection system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/11/3125/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/11/3125/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:11:p:3125-:d:563231

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().