Open AccessReducing energy consumption of wireless sensor networks using rules and extreme learning machine algorithm
Author(s) -
Duraisamy Sathya,
Pugalendhi Ganesh Kumar,
Balaji Prasanalakshmi
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.5288
Subject(s) - wireless sensor network , computer science , extreme learning machine , network packet , base station , intrusion detection system , transmission (telecommunications) , real time computing , energy consumption , key distribution in wireless sensor networks , energy (signal processing) , computer network , wireless , wireless network , data mining , artificial intelligence , telecommunications , artificial neural network , engineering , statistics , mathematics , electrical engineering
Wireless sensor networks consist of a collection of sensors to monitor physical or environmental events. Nowadays, the sensor networks are used in important applications like military, health and civilian monitoring. Since it is a wireless medium, deployed in remote locations and resource‐constrained nature, the sensor networks are easily vulnerable to attacks. The attack creates significant damages to the sensor networks. To avoid these problems, intrusion detection system (IDS) is implemented at the base station to filter any abnormal packets. In the proposed system, a survey is made on the attacks and rules to detect the attacks. Filtering the attacks using rule‐based IDS at the sensor nodes would reduce the amount of packet transmission to the base station which, in turn, would reduce the energy consumption of the sensor network. Extreme learning machine (ELM) algorithm is implemented at the base station to detect the abnormal packets. The experimental result shows the performance of different classification techniques and cross‐layer rules over the NSL‐KDD and real‐time datasets. The detection rate of the ELM algorithm is higher compared to other systems.