The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

中文翻译：

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深度强化学习技术在物联网跟踪应用中的能源节约

对于智能农场，智能工厂和智慧城市等应用，需要使用基于物联网（IoT）的目标跟踪系统，在该应用中，许多传感器设备共同连接以收集移动的目标位置。每个传感器设备连续依靠电池供电，消耗能量，同时在特定环境中感知目标信息。为了减少实时物联网跟踪应用中的传感器设备能耗，以前已利用许多传统方法（例如群集，信息驱动和其他方法）来选择最佳传感器。但是，由于传感器节点的电池寿命有限，因此应用机器学习方法（尤其是深度强化学习（Deep RL））来解决跟踪应用中的传感器选择问题非常需求。在这项研究中，我们提出了一种基于长期记忆深度Q网络（DQN）的深度RL目标跟踪模型，以克服IoT目标应用中的能耗问题。所提出的方法用于在跟踪目标的同时选择节能最佳的传感器。最佳传感器由最小距离函数（即作为状态导出）定义，这会降低能耗。仿真结果显示了在最佳传感器选择和能耗方面的有利功能。从而降低了能耗。仿真结果显示了在最佳传感器选择和能耗方面的有利功能。从而降低了能耗。仿真结果显示了在最佳传感器选择和能耗方面的有利功能。