Hainan Chen

BP neural network based continuous objects distribution detection in WSNs

WSNs (Wireless Sensor Networks) are widely applied in environment monitoring. Especially, in large scale environment monitoring, its flexibility in deployment and self-organization are strong points. However for distribution detection of continuous objects in large scale environment monitoring, there are two primary constraints: energy consumption and the accuracy of the detection which relies on the density of the WSNs. Currently, almost all of the continuous object monitoring are based on the boundary detection, and all the energy efficiency solutions only focus on the WSNs itself. Unfortunately, with the boundary detection method, the accuracy of the continuous objects detection highly relies on the density of the sensor nodes. What is worse, it is even impossible to make sure of the density of the sensor nodes in real situation. In order to deal with these issues, we proposed the Optimal Fusion Set based Clustering algorithm based on the continuous characteristics of the targets to enhance the energy efficiency and Global Distribution Status Monitoring (GDSM) algorithm to implement the monitoring with finite sensor nodes. Firstly, a dynamic diffusion model based on the Gaussian Puff model is proposed, and then the characteristics of continuous objects are analyzed. According to the theoretic analysis and simulation results, the GDSM algorithm can achieve stable accuracy with limited sensor nodes.

A Novel Multi-Module Separated Linear UWSNs Sensor Node

In this paper, we propose a novel design named multi-module separated linear (MMSL) underwater sensor node. Traditionally, the design of the underwater sensor node is the all-in-one type. The process module, sensor module, and communication module are integrated into one module, and generally, one node carries only one communication module. Therefore, the coverage of the Underwater Wireless Sensor Network (UWSNs) mainly rely on the number of the underwater sensor nodes, and when the scale of the UWSNs increases, the communication delay rapidly increases. MMSL underwater sensor node is composed of multiple traditional sensor modules and common communication modules. With the deployment of this type of new nodes, the completed acoustic channel would be transformed into the hybrid channel which is a combination of the acoustic and cable channel. We compare the coverage rate, data transmission energy loss, and delivery delay of this MMSL sensor node composed network with traditional UWSNs as well. According to the theoretical analysis and the simulation experiments based on the MATLAB platform and the UWSNs, which consist of MMSL sensor nodes, have distinct advantage in coverage rate and perform better on communication energy consumption and delivery delay.

Optimal Fusion Set based Clustering in WSN for continuous objects monitoring

Based on the application of continuous objects monitoring (COM) for Wireless Sensor Networks (WSNs), the sampling data collected by the sensors have relatively higher correlation and continuity due to the reason that the characteristic parameters of the monitored objects are continuous both in time and in space. In this paper, an Optimal Fusion Set based Clustering (OFSC) algorithm is presented to address the network clustering problem when monitoring the continuous objects. Different from the traditional clustering algorithms which cluster the network only after the cluster heads have been determined, OFSC algorithm, based on the global routing protocol in which the entire network information can be acquired by each node, the cluster head selection is carried out individually by nodes after the clustering results are firstly determined according to the Optimal Fusion Set theory. The performance evaluation results show that our OFSC algorithm can remarkably reduce the data traffic. On the other hand, our OFSC algorithm is a distributed clustering algorithm, which eliminates the computation of the communication cost during the cluster head selection, hence, decreases the computational complexity. Moreover, compared with the traditional LEACH and LEACH-C, our results show that the energy consumption of the entire network can be better balanced when monitoring the continuous objects.

An intrusion detection method for wireless sensor network based on mathematical morphology

Security issue in Internet of Things IoTs has long been the topic of extensive research in the last decade. Data encryption and authentication are the most common two methods to address the security issues in IoTs. However, these efforts are ineffective in detecting the diverse malicious attacks, especially in intrusion detection. Comparatively, very few attentions have been paid for detecting intrusive nodes in IoTs research. Therefore, in this paper, we derive an innovative method called granulometric size distribution GSD method based on mathematical morphology for detecting malicious attack in IoTs, such as intrusion detection. We successfully generate GSD clusters to directly monitor the number of active nodes in a wireless sensor network because the GSD curves are similar when the number of active nodes in a wireless sensor network is fixed. Link Quality Indicator data of each node are utilized as the network parameters in this method. The results show the effectiveness in intrusion detection. Copyright

The Enhancement of GSD Algorithm with Data Preprocessing Technique for WSN

As the application scope of wireless sensor network (WSN) grows at an amazing rate, the security issue of WSN is becoming increasingly important. Considering a distinct way to address the security problem rather than using the two most common methods which are data encryption and authentication, in this paper, we enhance an off-the-shelf data clustering based method called GSD by focusing on preprocessing RSSI and LQI data of WSN with sliding window and statistic methods for intrusion detection. Our results show that by preprocessing the RSSI and LQI data with the proposed Moving Average Variance method, GSD algorithm performs better in intrusion detection for WSNs.

A Granulometric Size Distribution based intrusion detection method for WSN

Security issue in Wireless Sensor Networks (WSNs) has long been the topic of extensive research in the last decade. Data encryption and authentication are the most common methods to address the security issues in WSN. However, these efforts are ineffective in detecting the diverse malicious attacks, especially in intrusion detection. Comparatively, very few attentions have been paid for detecting intrusive nodes in a common WSN topology. Therefore, in this paper we derive an innovative method called GSD method for detecting malicious attack in WSNs, such as intrusion detection. Link Quality Indicator (LQI) data of each node are utilized as the network parameters in this method. The simulation results show their effectiveness in intrusion detection.

Horizontal hierarchy slicing based data compression for WSN

Data compression on sensing data in Wireless Sensor Networks (WSNs) has long been the topic of extensive research in the last decade. Especially, in video sensor networks, the video and image data that need to be transmitted are relatively larger than common data. However WSNs usually have limited power supply and constrained communication bandwidth, it is significant to reduce the video and image data without any distortion before transmission to lower the energy consumption and the transmission delay. Therefore, in this paper we propose Horizontal Hierarchy Slicing (HHS) method based on Mathematical Morphology technology for compressing the data in WSNs. The results show its effectiveness in data compression for video and image data.