Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we propose DRLTrack, a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. In DRLTrack, a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks. DRLTrack is said to be trustworthy as it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance of DRLTrack considering the objectives through simulations and it demonstrates superior performance compared with existing work.

中文翻译：

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EdgeAI 辅助物联网中通过协作深度强化学习进行值得信赖的目标跟踪


使用人工智能 (AI) 方法进行移动目标跟踪，例如边缘辅助物联网 (Edge-IoT) 平台中的深度强化学习 (DRL)，前景广阔。在本文中，我们提出了 DRLTrack，这是一种在 Edge-IoT 中使用名为 C-DRL 的协作 DRL 进行目标跟踪的框架，旨在实现两个主要目标：高质量的跟踪 (QoT) 和资源高效的网络性能。在 DRLTrack 中，使用大量物联网设备来收集有关感兴趣目标的数据。网络中的一个或两个边缘设备与一组物联网设备协调，使用C-DRL方法协同检测目标，并由一组物联网设备在目标周围形成一个区域。为了在跟踪期间保持这样一个区域，我们采用深度 Q 网络来跟踪目标从一组到另一组。位于边缘设备顶部的 EdgeAI 可以在跟踪过程中控制 C-DRL 方法，并可以识别轨道序列。 DRLTrack 被认为是值得信赖的，因为它在 QoT、动态环境、甚至在某些网络攻击下表现出值得信赖的性能。我们通过模拟考虑目标来验证 DRLTrack 的性能，并且与现有工作相比，它展示了优越的性能。