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Reference node placement and selection algorithm based on trilateration for indoor sensor networks
Han, Guangjie,Choi, Deokjai,Lim, Wontaek John Wiley Sons, Ltd. 2009 WIRELESS COMMUNICATIONS AND MOBILE COMPUTING Vol.9 No.8
<P>The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two-phase positioning (TPP) algorithm, are too complicated to be implemented and they cannot provide the real-time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose reference node selection algorithm based on trilateration (RNST), which can provide real-time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real-time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real-time localization problem of the mobile nodes in indoor sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.</P>
( Guangjie Han ),( Huihui Xu ),( Jinfang Jiang ),( Lei Shu ),( Naveen Chilamkurti ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.11
Recently there has been an increasing interest in exploring the radio irregularity research problem in Wireless Sensor Networks (WSNs). Measurements on real test-beds provide insights and fundamental information for a radio irregularity model. In our previous work “LMAT”, we solved the path planning problem of the mobile anchor node without taking into account the radio irregularity model. This paper further studies how the localization performance is affected by radio irregularity. There is high probability that unknown nodes cannot receive sufficient location messages under the radio irregularity model. Therefore, we dynamically adjust the anchor node`s radio range to guarantee that all the unknown nodes can receive sufficient localization information. In order to improve localization accuracy, we propose a new 2-hop localization scheme. Furthermore, we point out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability. Finally, simulations show that, compared with 1-hop localization scheme, the 2-hop localization scheme with the radio irregularity model reduces the average localization error by about 20.51%.
OLAP4R: A Top-K Recommendation System for OLAP Sessions
( Youwei Yuan ),( Weixin Chen ),( Guangjie Han ),( Gangyong Jia ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.6
The Top-K query is currently played a key role in a wide range of road network, decision making and quantitative financial research. In this paper, a Top-K recommendation algorithm is proposed to solve the cold-start problem and a tag generating method is put forward to enhance the semantic understanding of the OLAP session. In addition, a recommendation system for OLAP sessions called “OLAP4R” is designed using collaborative filtering technique aiming at guiding the user to find the ultimate goals by interactive queries. OLAP4R utilizes a mixed system architecture consisting of multiple functional modules, which have a high extension capability to support additional functions. This system structure allows the user to configure multi-dimensional hierarchies and desirable measures to analyze the specific requirement and gives recommendations with forthright responses. Experimental results show that our method has raised 20% recall of the recommendations comparing the traditional collaborative filtering and a visualization tag of the recommended sessions will be provided with modified changes for the user to understand.