User Traffic Prediction Based on K Neighbors Collaborative Filtering for CASoRT System

Repeat transmission of hotspot traffics results in great waste of energy and bandwidth in wireless network, for the communication of current network is content careless. To address this issue, a novel content aware transmission schema named CASoRT System was put forward in our previous research to reduce wireless resource waste by the benefit of broadcast. In this paper, we propose a K neighbors collaborative filtering prediction method to forecast the request of hotspot traffics, which will be broadcasted by CASoRT. Firstly, we introduce the traditional prediction methods and describe the framework of our job. Then, we deliver the expression of our method in formulation, and discuss the time complexity in quantitative analysis. At last, our method is validated by simulation, and the results demonstrate that the new schema can potentially lead to 20% deduction compared with the unicast schema in hotspot traffic transmission. And the results also show that our method reduces 25% prediction time than traditional methods, but with the same performance in saving wireless resources.

[1]  Xiaofeng Zhong,et al.  Optimization of Broadcasting Scheme for the CASoRT System , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[2]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[3]  Partha Sarathi Chakraborty,et al.  A Scalable Collaborative Filtering Based Recommender System Using Incremental Clustering , 2009, 2009 IEEE International Advance Computing Conference.

[4]  Jing Wang,et al.  Location based content recommendation for CASoRT system , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[5]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[6]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[7]  Sanjeev R. Kulkarni,et al.  Iterative collaborative filtering for recommender systems with sparse data , 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

[8]  Sutheera Puntheeranurak,et al.  An Item-based collaborative filtering method using Item-based hybrid similarity , 2011, 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

[9]  Hyun-Tae Kim,et al.  A recommender system based on genetic algorithm for music data , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[10]  Weimin Zheng,et al.  Improving the Effective IO Throughput by Adaptive Read-Ahead Strategy for Private Cloud Storage Service , 2012, 2012 Seventh ChinaGrid Annual Conference.

[11]  Dong Aihua,et al.  Hybrid Product Recommender System for Apparel Retailing Customers , 2010, 2010 WASE International Conference on Information Engineering.

[12]  Yu Huang,et al.  An energy-efficient opportunistic multicast scheduling based on superposition coding for mixed traffics in wireless networks , 2012, EURASIP J. Wirel. Commun. Netw..

[13]  Jing Wang,et al.  Content Aware Soft Real Time Media Broadcast (CASoRT) , 2008, 2008 Third International Conference on Communications and Networking in China.