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Uncertainty-aware RFID network planning for target detection and target location

机译:用于目标检测和目标定位的不确定性RFID网络规划

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The RFID reader-tag detection uncertainty comes from the inherent unreliability of the RFID technology due to the collisions between RFID devices and interference from the surrounding environment. The performance of an RFID network is largely affected by detection uncertainty, which should be considered in the network planning phase to minimize its negative impact. In this paper, we formulate a non-linear integer programming (NLIP) model to minimize the cost on a grid-based field while satisfying the given coverage requirement for the target detection and target location. Then, an exact p-order polynomial approximation (POPA) algorithm and heuristic algorithms are designed to solve the model. Through computational experiments, the efficiency of the proposed algorithms is demonstrated. We further apply the heuristic algorithms to an industrial case and illustrate how the proposed model and algorithms are applied to satisfy the demand of practical application in a mixed-model assembly line. The results indicate that the genetic algorithm with GRASP outperforms other algorithms in terms of solution quality and computational robustness. (C) 2016 Elsevier Ltd. All rights reserved.
机译:RFID读取器标签检测的不确定性来自RFID技术固有的不可靠性,这归因于RFID设备之间的碰撞以及周围环境的干扰。 RFID网络的性能在很大程度上受检测不确定性的影响,在网络规划阶段应考虑这一点,以最大程度地减少其负面影响。在本文中,我们制定了一个非线性整数规划(NLIP)模型,以最小化基于网格的字段的成本,同时满足目标检测和目标位置的给定覆盖范围要求。然后,设计了精确的p阶多项式逼近(POPA)算法和启发式算法来求解模型。通过计算实验,证明了所提算法的有效性。我们进一步将启发式算法应用于工业案例,并说明如何将所提出的模型和算法应用于混合模型装配线中的实际应用需求。结果表明,具有GRASP的遗传算法在解决方案质量和计算鲁棒性方面优于其他算法。 (C)2016 Elsevier Ltd.保留所有权利。

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