Advanced Indoor Location Measurement Architecture for Emergency Situations

According to the increasing of modern people's life time in indoor places, the importance of indoor LBS has been continuously gotten higher attention. This phenomenon is one of the inevitable IT trends which caused by the evolved computing paradigm, mobile and wireless communication technologies, and Internet of Things. In addition, there are other hot topics in these trends, big data and cloud. The trends are like a blackhole which strongly pulls the whole technology. That is, it has same ripple effects in the indoor location service and technology. Although there are still several problems which related to the accuracy and precision in the indoor location service, it will be certainly overcome in the near future. However, the practical uses of indoor LBS are applied in some limited fields such as marketing, finance, and so on until now. This is one of the reasons why we propose the advanced indoor location measuring architecture, which is very suitable for emergency situations. Moreover, the proposed architecture can improve the accuracy and precision, when it measures an indoor location, because we need high accuracy and precision in emergency situations than common situations. In the proposed architecture, it utilizes various information and big data to measure an exact indoor position, and it operates with various IoT devices. Based on these schemes, the proposed architecture can provide high accuracy and precision than the existing methods. In this paper, we verified the superiority of architecture than others in various aspects.

[1]  Stuart A. Golden,et al.  Sensor Measurements for Wi-Fi Location with Emphasis on Time-of-Arrival Ranging , 2007, IEEE Transactions on Mobile Computing.

[2]  Song Guo,et al.  Cost Minimization for Big Data Processing in Geo-Distributed Data Centers , 2014, IEEE Transactions on Emerging Topics in Computing.

[3]  Y. Cho,et al.  WARP-P: Wireless Signal Acquisition with Reference Point by using Simplified PDR – System Concept and Performance Assessment , 2013 .

[4]  Jinjun Chen,et al.  A Time Efficient Approach for Detecting Errors in Big Sensor Data on Cloud , 2015, IEEE Transactions on Parallel and Distributed Systems.

[5]  Ryu Miura,et al.  Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks , 2014, IEEE Transactions on Emerging Topics in Computing.

[6]  Jianwu Wang,et al.  Big Data Applications Using Workflows for Data Parallel Computing , 2014, Computing in Science & Engineering.

[7]  Hua Lu,et al.  Distance-Aware Join for Indoor Moving Objects , 2015, IEEE Transactions on Knowledge and Data Engineering.

[8]  Yonggang Wen,et al.  Toward Scalable Systems for Big Data Analytics: A Technology Tutorial , 2014, IEEE Access.

[9]  Antonio F. Gómez-Skarmeta,et al.  Automatic Design of an Indoor User Location Infrastructure Using a Memetic Multiobjective Approach , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).