Radio Frequency Identification (RFID) technology has been widely used in indoor location tracking, especially serving human beings, due to its advantage of low cost, non-contact communication, resistance to hostile environments and so forth. Over the years, many indoor location tracking methods have been proposed. However, tracking mobile RFID readers in real-time has been a daunting task, especially for achieving high localization accuracy. In this paper, we propose a new Mobile RFID (M-RFID)-based Localization approach for Indoor Human Tracking, named MRLIHT. Based on the M-RFID model where RFID readers are equipped on the moving objects (human beings) and RFID tags are fixed deployed in the monitoring area, MRLIHT implements the real-time indoor location tracking effectively and economically. First, based on the readings of multiple tags detected by an RFID reader simultaneously, MRLIHT generates the response regions of tags to the reader. Next, MRLIHT determines the potential location region of the reader where two algorithms are devised. Finally, MRLIHT estimates the location of the reader by dividing the potential location region of the reader into finer-grained grids. The experimental results demonstrate that the proposed MRLIHT performs well in both accuracy and scalability.
MapReduce is a popular parallel data-processing system, and task scheduling is one of the kernel techniques in MapReduce. In many applications, users have requirements that their MapReduce jobs should be completed before specific deadlines. Hence, in this paper, a novel scheduling algorithm based on the most effective sequence (SAMES) is proposed for deadline-constraint jobs in MapReduce. First, according to the characteristics of MapReduce, we propose a novel sequence-based execution strategy for MapReduce jobs and a new concept, the effective sequence (ES). Then, we design some efficient approaches for finding ESes and choose the most effective sequence (MES) for job execution. We also propose methods for MES-updates and exception handling. Finally, we verify the effectiveness of SAMES through experiments. The experimental results show that SAMES is an efficient scheduling algorithm for deadline-constraint jobs in MapReduce.
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