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( Shigeng Zhang ),( Shuping Yan ),( Weitao Hu ),( Jianxin Wang ),( Kehua Guo ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.3
Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.
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Outlier Detection Techniques for Localization in Wireless Sensor Networks : A Survey
Hala Abukhalaf,Jianxin Wang,Shigeng Zhang 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.6
In wireless sensor networks (WSNs), localization is one of the most important topics because the location information is typically useful for many applications. The primary data used in a localization process include the locations of anchor nodes and the distances between neighboring nodes. However, these data may contain outliers that deviate from their true values. The existence of the outliers might make the estimated positions not accurate. Thus, it is important to detect and handle outliers in order to achieve high localization accuracy. In this paper, we survey the existing outlier detection techniques for localization in wireless sensor networks. We provide taxonomy for classifying outlier detection techniques in WSNs localization based on different features. In addition, we present comparisons of these techniques. Finally, we discuss the future research directions in this area.
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Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks
Hala Abukhalaf,Jianxin Wang,Shigeng Zhang 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.7
Accurate location information is critical to many applications in wireless sensor networks (WSNs) such as target tracking, environmental monitoring and geographical routing. Localization aims to figure out the locations of unknown nodes based on global locations of anchors and inter-node distance measurements. However, the existence of outlier anchors and outlier distances degrade localization accuracy in many localization algorithms. Most existing outlier detection approaches focus on distance outlier detection; few efforts have been devoted to anchor outlier detection. In this paper, we propose a mobile-assisted approach to detect outlier anchors and mitigate their negative effects in localization to achieve high localization accuracy. The proposed approach, namely Mobile-Assisted Anchor Outlier detection (MAAO),employs a mobile element to traverse the wireless sensor network several times to collect position information from static anchors in the network. For every static anchor, the mobile element computes the average of the anchor’s positions acquired from all tours, and compare it with the position acquired from the last mobile tour to detect whether the anchor is an outlier or not. The evaluation results show that MAAO can effectively detect outlier anchors, which consequently results in remarkable improvement in localization accuracy by not using outlier anchors in the localization process.
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