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Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation

机译:带无人机地面车辆的反向散射数据收集:移动管理和功率分配

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Collecting data from the massive Internet of Things (IoT) devices is a challenging task since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in the loT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this paper proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem, unfortunately, involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. The simulation results show that if the noise power is small (e.g., <=-100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., -60 dB m), the UGV should spend more motion energy to get closer to the IoT users.
机译:从大规模互联网(物联网)设备收集数据是一个具有挑战性的任务,因为通信电路是功率要求的,而IoT设备的能量供应是有限的。为了克服这一挑战,反向散射通信作为有希望的解决方案,它消除了批次设备中的射频分量。不幸的是,反向散射通信的传输范围很短。为了促进反向散射的沟通,本文提出将无人面的地面车辆(UGV)与反向散射数据收集集成。利用这样的方案,UGV可以通过接近各种IOT设备来提高通信质量。但是,迁移也成本耗费能源消耗和基本问题是:支出能源在沟通方面的良好平衡是什么?为了回答这个问题,本文研究了联合图移动性和反向散射通信模型的能量最小化。通过联合模型,遗憾的是,移动管理和功率分配问题涉及通过移动性和连续传播所带来的离散变量之间的非线性耦合。尽管优化挑战,但是,从理论上实现了最小能耗的算法,并且它导致在UGV反散射系统中的通信方面的开支能量之间自动折衷。仿真结果表明,如果噪声功率很小(例如,<= - 100 dBm),则UGV应收集具有小运动的数据。但是,如果噪声功率增加到更大的值(例如,-60 dB m),则UGV应花费更多的运动能量来更接近IOT用户。

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