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A novel-Q DFSA algorithm for passive RFID system

机译:一种用于无源RFID系统的新型Q DFSA算法

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摘要

The tag detection ability of Passive Radio Frequency Identification (RFID) systems are critically challenged by the collision occurrence due to simultaneous responding tags during the identification process. The dynamic scheduling of the frame size governed by Dynamic Frame Size ALOHA(DFSA) process, by adjusting the frame lengths according to the size of tag population can avoid the collisions during the identification. However, the performance of DFSA majorly depends on the frame size selection policy which in previous studies was adopted to achieve the target of throughput maximization during a frame. This condition is obtained at the cost of equating the frame size up to the number of estimated tags responding during the time frame. This approximation enhances the throughput value but contributes to massive energy wastages as the frame lengths approach to a very large value in large tag population size. Therefore, it is essential to develop the new frame size estimation policy for DFSA achieving the aim of optimization between throughput and energy for improved time and energy performance. In this paper, we have proposed an EPC C1G2 standards based Novel-Q DFSA algorithm which optimizes the frame size accounting both the energy and the throughput. The combined throughput and energy trade-offs are measured through Energy-Time-Delay (ET) cost which is minimum for our proposed algorithm compared to the existing solutions. Furthermore, the Throughput-Time Delay product approves the stability in large population size making it suitable for numerous identification applications. (C) 2017 Elsevier B.V. All rights reserved.
机译:被动射频识别(RFID)系统的标签检测能力通过在识别过程中同时响应标签而受到碰撞发生的严重挑战。通过根据标签群体的尺寸调整帧长度,通过动态帧大小Aloha(DFSA)处理所控制的帧大小的动态调度可以避免在识别期间碰撞。然而,DFSA的性能主要取决于采用先前研究中的帧尺寸选择策略来实现帧中的吞吐量最大化的目标。该条件以等于在时间框架期间响应的估计标签的数量等于帧大小的成本获得。该近似增强了吞吐量值,但有助于大量能量浪费,因为帧长度在大标签群体大小中的非常大的值。因此,必须为DFSA开发新的帧大小估计策略,以实现吞吐量和能量之间的优化,以改善时间和能量性能。在本文中,我们提出了一种基于EPC C1G2标准的新型Q DFSA算法,其优化了能量和吞吐量的帧大小。通过与现有解决方案相比,通过节能 - 时间延迟(ET)成本来衡量组合的吞吐量和能量折衷。此外,吞吐量延迟产品批准了大量人口大小的稳定性,使其适用于许多识别应用。 (c)2017 Elsevier B.v.保留所有权利。

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