scholarly journals A Novel Intrusion Detection System in WSN using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Sarah Salaheldin Lutfi ◽  
◽  
Mahmoud Lutfi Ahmed ◽  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Noise Removal ◽  
Base Station ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Fuzzy Filter ◽  
Neuro Fuzzy

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. A new method of intrusion detection using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm (HNF-ACA) is proposed in this study, which has been able to map the network status directly into the sensor monitoring data received by base station, accordingly that base station can sense the abnormal changes in network.The hybridized Sugeno-Mamdani based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. The Modified Mutation Based Ant Colony Algorithm technique improves the accuracy of determining the membership values of input trust values of each node in fuzzy filters. To end, the proposed method was tested on the WSN simulation and the results showed that the intrusion detection method in this work can effectively recognise whether the abnormal data came from a network attack or just a noise than the existing methods.

Detection of Faulty Measurements in WBANs using Gaussian Mixture Model and Ant Colony

2014 ◽  
Vol 5 (4) ◽  
pp. 20-38
Author(s):  
Osman Salem ◽  
Yaning Liu ◽  
Ahmed Mehaoua
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Algorithm ◽  
Gaussian Mixture ◽  
Base Station ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Physiological Data ◽  
Detection Accuracy ◽  
Clinical Emergency

Wireless sensor networks are subject to different types of faults and interferences after their deployment. Abnormal values reported by sensors should be separated from faulty or injected measurements to ensure reliable monitoring operation. The aim of this paper is to propose a lightweight approach for the detection and suppression of faulty measurements in medical wireless sensor networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. The authors begin by collecting physiological data and then they cluster the data collected during the first few minutes using the Gaussian mixture decomposition. They use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules in the central base station. Afterward, the derived rules are transmitted and installed in each associated sensor to detect abnormal values in distributed manner, and notify anomalies to the base station. Finally, the authors exploit the spatial and temporal correlations between monitored attributes to differentiate between faulty sensor readings and clinical emergency. They evaluate their approach with real and synthetic patient datasets. The experimental results demonstrate that their proposed approach achieves a high rate of detection accuracy for clinical emergency with reduced false alarm rate when compared to robust Mahalanobis distance.

scholarly journals A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongchun Qu ◽  
Libiao Lei ◽  
Xiaoming Tang ◽  
Ping Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Base Station ◽  
Simulation Software ◽  
Wireless Sensor ◽  
Sensor Monitoring

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.

scholarly journals A Self-organizing Wireless Sensor Networks Based on Quantum Ant Colony Evolutionary Algorithm

2017 ◽  
Vol 13 (07) ◽  
pp. 69 ◽  
Author(s):  
Lin-lin Wang ◽  
Chengliang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Dynamic Adjustment ◽  
Coverage Problem ◽  
Self Organizing

<p><span style="font-size: medium;"><span style="font-family: 宋体;">Aiming at the coverage problem of self-organizing wireless sensor networks, a target coverage method for wireless sensor networks based on Quantum Ant Colony Evolutionary Algorithm (QACEA) is put forward. This method introduces quantum state vector into the coding of ant colony algorithm, and realizes the dynamic adjustment of ant colony through quantum rotation port. The simulation results show that the quantum ant colony evolutionary algorithm proposed in this paper can effectively improve the target coverage of wireless sensor networks, and has obvious advantages compared with the other two methods in detecting the number of targets and the convergence speed. Based on the above findings, it is concluded that the algorithm proposed plays an essential role in the improvement of target coverage and it can be widely used in the similar fields, which has great and significant practical value.</span></span></p>

scholarly journals Path Optimization of Mobile Sink Node in Wireless Sensor Network Water Monitoring System

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Fan Chao ◽  
Zhiqin He ◽  
Aiping Pang ◽  
Hongbo Zhou ◽  
Junjie Ge
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Mobile Sink ◽  
Base Station ◽  
Water Monitoring ◽  
Wireless Sensor ◽  
Sink Node

In the water area monitoring of the traditional wireless sensor networks (WSNs), the monitoring data are mostly transmitted to the base station through multihop. However, there are many problems in multihop transmission in traditional wireless sensor networks, such as energy hole, uneven energy consumption, unreliable data transmission, and so on. Based on the high maneuverability of unmanned aerial vehicles (UAVs), a mobile data collection scheme is proposed, which uses UAV as a mobile sink node in WSN water monitoring and transmits data wirelessly to collect monitoring node data efficiently and flexibly. In order to further reduce the energy consumption of UAV, the terminal nodes are grouped according to the dynamic clustering algorithm and the nodes with high residual energy in the cluster are selected as cluster head nodes. Then, according to the characteristics of sensor nodes with a certain range of wireless signal coverage, the angular bisection method is introduced on the basis of the traditional ant colony algorithm to plan the path of UAV, which further shortens the length of the mobile path. Finally, the effectiveness and correctness of the method are proved by simulation and experimental tests.

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