Abstract

The rapid proliferation of IoT devices and multimedia applications has necessitated the development of advanced resource management techniques to ensure optimal Quality of Service (QoS) in IoT-chained multimedia processing networks. Existing static resource allocation methods and reactive maintenance strategies often fail to adapt to the dynamic nature of these networks, leading to suboptimal performance and increased service disruptions. To address these challenges, this study introduces a suite of novel approaches designed to enhance the QoS of IoT-chained multimedia processing networks. Firstly, we propose a Deep Reinforcement Learning framework for Dynamic Resource Allocation (DRL-DRA), which learns optimal resource allocation policies through continuous interaction with the network environment. Unlike traditional static methods, DRL-DRA dynamically allocates resources based on real-time data and QoS requirements, significantly improving metrics such as latency, throughput, and packet loss. Inputs to the model include environmental data, device status, network conditions, task characteristics, and historical performance metrics. Our simulations demonstrate a 30% reduction in latency, a 25% increase in throughput, and a 15% decrease in packet loss compared to baseline static allocation methods. Secondly, we develop a Predictive Maintenance model using Long ShortTerm Memory networks (PM-LSTM), tailored specifically for the unique demands of IoT-chained multimedia processing networks. By leveraging historical data on device performance, environmental conditions, and usage patterns, PM-LSTM accurately predicts maintenance needs, thereby minimizing service disruptions and enhancing network reliability. The model’s outputs include predictive maintenance schedules and recommended proactive actions.