面向室内定位的WIFI探针数据预处理研究
Research on WIFI Probe Data Preprocessing for Indoor Location
DOI: 10.12677/AIRR.2018.71004, PDF,    国家科技经费支持
作者: 张振亚*, 操华茜, 聂芹芹, 董梦杰, 王萍：安徽建筑大学智能建筑与建筑节能安徽省重点实验室，安徽 合肥；程红梅：安徽建筑大学经济与管理学院，安徽 合肥
关键词: WIFI探针；接收信号的强度指示；判别器；BP神经网络；WIFI Detector； Received Signal Strength Indication； Discriminator； BP Neural Network
摘要: 为验证依据多探针同时感知到的同一WIFI终端的RSSI值辨识WIFI终端是否在指定区域内的可行性，本文围绕多探针数据集的构造将将WIFI终端数据预处理流程划分为探针探测数据集解析、探针探测数据时间帧编号、面向室内人员定位的探针数据构造等三个阶段，并设计了相关预处理任务的流程并进行了实现。实验结果表明，以预处理后的数据为输入，基于BP神经网络的判别器可以以很高的准确率判别WIFI终端是否在指定的区域内，依据多探针同时感知到的同一WIFI终端的RSSI值辨识WIFI终端是否在指定区域内是可行的。
Abstract: To verify the feasibility of identifying whether a WIFI terminal is within a designated indoor area based on RSSI value collected by multi WIFI detector, data preprocessing task for the construc-tion of detection data set is divided into three phase such as WIFI probe data parsing, time frame Numbering for probe data and the construction of indoor occupant location oriented data set in this paper. Flow charts for those three phases are given. In this paper, a BP neural network based discriminator for the identification that whether a WIFI terminal is within a designated indoor area is implemented with multi RSSI vector as input. And experimental results show that the precision of the discriminator for indoor area location is high. It is feasibility of identifying whether a WIFI terminal is within a designated indoor area based on multi RSSI detected.
文章引用：张振亚, 操华茜, 聂芹芹, 董梦杰, 程红梅, 王萍. 面向室内定位的WIFI探针数据预处理研究[J]. 人工智能与机器人研究, 2018, 7(1): 34-42. https://doi.org/10.12677/AIRR.2018.71004

参考文献

[1] Kim, Y.S. and Srebric, J. (2017) Impact of Occupancy Rates on the Building Electricity Consumption in Commercial Buildings. Energy and Buildings, 38, 591-600. [Google Scholar] [CrossRef] 
[2] Rahman, K.A., Hariri, A., Leman, A.M. and Yusof, M.Z. (2017) Energy Consumption in Residential Building: The Effect of Appliances and Human Behavior. AIP Conference Proceeding.
[3] Oldewurtel, F., Sturznegger, D. and Morari, M. (2013) Importance of Occupancy Information for Building Climate Control. Applied Energy, 101, 521-532. [Google Scholar] [CrossRef] 
[4] Shen, W.M., Newsham, G. and Gunay, B. (2017) Leveraging Existing Occupancy-Related Data for Optimal Control of Commercial Office Buildings: A Review. Advanced Engineering Informatics. [Google Scholar] [CrossRef] 
[5] Bahl, P. and Pad-manabhan, V.N. (2000) RADAR: An In-Building RF-Based User Location and Tracking System. Proceeding of IEEE Infocom, 26-30 March 2000, 775-784. [Google Scholar] [CrossRef] 
[6] Jia, M., Srinivasan, R.S. and Raheem, A.A. (2017) From Occupancy to Occupant Behavior: An Analytical Survey of Data Acquisition Technologies, Modeling Methodologies and Simulation Coupling Mechanisms for Building Energy Efficiency. Renewable and Sustainable Energy Reviews, 68, 525-540. [Google Scholar] [CrossRef] 
[7] Candanedo, L.M., Feldheim, V. and Deramaix, D. (2017) A Methodology Based on Hidden Markov Models for Occupancy Detection and a Case Study in a Low Energy Residential Building. Energy and Buildings, 148, 327-341. [Google Scholar] [CrossRef] 
[8] Gu, Y., Lo, A. and Niemegeers, I. (2009) A Survey of Indoor Positioning Systems for Wireless Personal Networks. IEEE Communications Surveys and Tutorials, 11, 13-32. [Google Scholar] [CrossRef] 

为你推荐