# AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2510.16414 · HTML: https://mlsanity.com/paper/2510.16414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 18 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [On the Role of Age of Information in the Internet of Things](https://mlsanity.com/paper/1812.08286/) (2018)
- [AoI Minimization in Status Update Control with Energy Harvesting Sensors](https://mlsanity.com/paper/2009.04224/) (2020)
- [Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses](https://mlsanity.com/paper/2306.10309/) (2023)
- [DMSA: A Decentralized Microservice Architecture for Edge Networks](https://mlsanity.com/paper/2501.00883/) (2025)
- [Energy-Efficient Blockchain-enabled User-Centric Mobile Edge Computing](https://mlsanity.com/paper/2302.10515/) (2023)
- [Performance Optimization in RSMA-assisted Uplink xURLLC IIoT Networks with Statistical QoS Provisioning](https://mlsanity.com/paper/2405.16471/) (2024)
- [AoI, Timely-Throughput, and Beyond: A Theory of Second-Order Wireless Network Optimization](https://mlsanity.com/paper/2407.15983/) (2024)
- [Statistical QoS Provision in Business-Centric Networks](https://mlsanity.com/paper/2408.15609/) (2024)
- [Topology-aware Microservice Architecture in Edge Networks: Deployment Optimization and Implementation](https://mlsanity.com/paper/2501.04956/) (2025)
- [When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective](https://mlsanity.com/paper/2501.06552/) (2025)
- [Physiological Signal-Driven QoE Optimization for Wireless Virtual Reality Transmission](https://mlsanity.com/paper/2508.09151/) (2025)

## Abstract (arXiv)

In the Industrial Internet of Things (IIoT), the frequent transmission of large amounts of data over wireless networks should meet the stringent timeliness requirements. Particularly, the freshness of packet status updates has a significant impact on the system performance. In this paper, we propose an age-of-information (AoI)-aware multi-base station (BS) real-time monitoring framework to support extensive IIoT deployments. To meet the freshness requirements of IIoT, we formulate a joint task offloading and resource allocation optimization problem with the goal of minimizing long-term average AoI. Tackling the core challenges of combinatorial explosion in multi-BS decision spaces and the stochastic dynamics of IIoT systems is crucial, as these factors render traditional optimization methods intractable. Firstly, an innovative branching-based Dueling Double Deep Q-Network (Branching-D3QN) algorithm is proposed to effectively implement task offloading, which optimizes the convergence performance by reducing the action space complexity from exponential to linear levels. Then, an efficient optimization solution to resource allocation is proposed by proving the semi-definite property of the Hessian matrix of bandwidth and computation resources. Finally, we propose an iterative optimization algorithm for efficient joint task offloading and resource allocation to achieve optimal average AoI performance. Extensive simulations demonstrate that our proposed Branching-D3QN algorithm outperforms both state-of-the-art DRL methods and classical heuristics, achieving up to a 75% enhanced convergence speed and at least a 22% reduction in the long-term average AoI.