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Study of Traveling Partners’ Discovery Algorithm Based Closed Clustering and Intersecting
Kongfa Hu,Jiadong Xie,Chengjun Hu,Tao Yang,Yuqing Mao,Yun Hu,Long Li 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.6
As the rapid development of IOT (the Internet of Things), RFID technology has been widely applied, and it generates a large of RFID trajectory stream data with the spatial-temporal characteristic. Because RFID has many characteristics, it leads to become very difficult that extracting moving objects groups that together moving (ie. traveling partners) in a period of time from RFID trajectory stream data. Existing methods are difficult to efficiently find this model. This paper presents a closed clustering and intersecting algorithm (CCI) for RFID data to detect movement along traveling partners, which is mainly constituted by two steps: first step is clustering sub-trajectory, it generates sub-trajectory clusters; second step is intersecting sub-trajectories with the traveling partners’ candidate set to improve the candidate set, and find out traveling partners. In this process, we use the principle of Closure to accelerate our processing. Through experiments on the RFID synthetic dataset, we demonstrate the effectiveness and efficiency of our algorithm, thus show that our algorithm is suitable for discovering traveling partners in RFID applications.
Cost-Aware Scheduling of Computation-Intensive Tasks on Multi-Core Server
( Youwei Ding ),( Liang Liu ),( Kongfa Hu ),( Caiyan Dai ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.11
Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments’ results show the cost of ICAS is much lower than the existing method.