Abstract
Location prediction is critical to mobile service because various kinds of applications tightly combined with object’s location. However, location prediction is a challenging work because the location captured is always not continuous and object’s behavior is uncertain and irregular. The prediction accuracy of many models is less than 30%. But the prediction accuracy is important to location prediction. It will directly affect the mobile services. So this paper is to improve prediction accuracy to provide more efficient mobile service. This paper proposes a location prediction model based on k-means algorithm and time matching. For the mobile service always region oriented, we first cluster history location using k-means algorithm to define several regions. Then we divide every day time into several segments and calculate the maximum probability location in every time segment. A trajectory of an object in one day is formed with trajectory model and trajectory updating model which is proposed in this paper. We can predict object’s location with time-matching method. At last, we do experiments with real location data which captured by APs. The prediction result with k-means is compared to the result without model based on k-means algorithm. The experiment result shows that prediction accuracy of our model is higher than the prediction without new model. So more location services can be provided to objects with this new model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
YU Rui-Yun, XIA Xing-You, LI Jie, ZHOU Yan, WANG Xing-Wei. Social-Aware Mobile Object Location Prediction Algorithm in Participatory Sensing Systems. Chinese Journal of Computers, vol. 38, No. 2, Feb 2015.
LI Wen, XIA Shi-xiong, LIU Feng, ZHANG Lei, YUAN Guan. Location prediction algorithm based on movement tendency. Journal on Communicaitons, Vol. 35, No. 2, February 2014.
Matthew W. Robards, Peter Sunehag. Semi-Markov kMeans Clustering And Activity Recognition From Body-Worn Sensors. 2009 Ninth IEEE International Conference on Data Mining.
Yi Yang, Zhiliang Wang, Qiong Zhang, Yang Yang. A Time Based Markov Model for Automatic Position-Dependent Services in Smart Home. 2010 Chinese Control and Decision Conference.
Wen Li, Shi-xiong XIA, Feng LIU, Lei ZHANG. Hybrid Markov Location Prediction Algorithm Based on Dynamic Social Ties. IEICE TRANS. INF. & SYST, VOL. E98-D, NO. 8 AUGUST2015.
LIN Shimin, TIAN Fengzhan, Lu Yuchang. Construction and applications in data mining of bayesian networks. Journal of Tsinghua University, 2001, 41(1).
Yucheng Zhang, Jinglong Hu, Jiantao Dong, Yao Yuan, Jihua Zhou, Jinglin Shi. IEEE GLOBAL COMMUNICATIONS CONFERENCE (IEEE GLOBECOM 2009).
Fatima MOURCHID, Ahmed HABBANI, Mohamed EL KOUTBI. Mining object patterns for location prediction in mobile social networks.
Kiran K. Rachuri and C.Siva Ram Murthy. Level Biased Random Walk for information Discovery in Wireless Sensor Networks.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hu, Y., Zhu, X., Ma, G. (2018). Location Prediction Model Based on K-means Algorithm. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_66
Download citation
DOI: https://doi.org/10.1007/978-981-10-3773-3_66
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3772-6
Online ISBN: 978-981-10-3773-3
eBook Packages: EngineeringEngineering (R0)