Online Surveillance of IoT Agents in Smart Cities Using Deep Reinforcement Learning

Abstract

In the context of today's smart cities, the effective operation of online surveillance of IoT agents is crucial for maintaining public safety and security. To achieve this, collaboration and cooperation among these autonomous IoT agents are indispensable. While the existing research has focused on collaboration amongst the neighboring agents or implicit cooperation, real-world scenarios often necessitate broader forms of collaboration. In response to this need, we introduce a novel framework that leverages visual signals and observations to facilitate collaboration among online surveillance. Our proposed framework incorporates the Multi-Agent POsthumous Credit Assignment (MA-POCA) algorithm as a training mechanism. The empirical results demonstrate that our framework consistently outperforms the base model in various performance metrics. Specifically, it exhibits superior performance in group cumulative reward, cumulative reward, and episode length. Furthermore, our proposed model excels in policy loss performance measures when compared to base model.