Trustworthy Target Tracking With Collaborative Deep Reinforcement Learning in EdgeAI-Aided IoT

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.

Automated summary

This article proposes a framework called DRLTrack for mobile target tracking in edge-assisted IoT platform using collaborative deep reinforcement learning. DRLTrack aims to achieve high quality tracking and resource-efficient network performance. By employing a large number of IoT devices, DRLTrack is able to maintain an area around the target and demonstrate reliable performance even under cyberattacks.