A Practical AoI Scheduler in IoT Networks with Relays
This work addresses the practical deployment of AoI schedulers in IoT networks, which is crucial for real-world applications with dynamic conditions and unknown traffic, though it is incremental as it builds on existing DRL methods with a novel mechanism for scalability.
The paper tackles the problem of designing a practical Age of Information (AoI) scheduler for two-hop IoT networks with relays, addressing limitations of existing methods that assume constant conditions and known traffic patterns, and proposes a novel voting mechanism based proximal policy optimization (v-PPO) algorithm that maintains linear action space for scalability. Simulation results show it outperforms both ML and traditional schedulers like DQN, MAF-MAD, MAF, and round-robin in all considered scenarios.
Internet of Things (IoT) networks have become ubiquitous as autonomous computing, communication and collaboration among devices become popular for accomplishing various tasks. The use of relays in IoT networks further makes it convenient to deploy IoT networks as relays provide a host of benefits, like increasing the communication range and minimizing power consumption. Existing literature on traditional AoI schedulers for such two-hop relayed IoT networks are limited because they are designed assuming constant/non-changing channel conditions and known (usually, generate-at-will) packet generation patterns. Deep reinforcement learning (DRL) algorithms have been investigated for AoI scheduling in two-hop IoT networks with relays, however, they are only applicable for small-scale IoT networks due to exponential rise in action space as the networks become large. These limitations discourage the practical utilization of AoI schedulers for IoT network deployments. This paper presents a practical AoI scheduler for two-hop IoT networks with relays that addresses the above limitations. The proposed scheduler utilizes a novel voting mechanism based proximal policy optimization (v-PPO) algorithm that maintains a linear action space, enabling it be scale well with larger IoT networks. The proposed v-PPO based AoI scheduler adapts well to changing network conditions and accounts for unknown traffic generation patterns, making it practical for real-world IoT deployments. Simulation results show that the proposed v-PPO based AoI scheduler outperforms both ML and traditional (non-ML) AoI schedulers, such as, Deep Q Network (DQN)-based AoI Scheduler, Maximal Age First-Maximal Age Difference (MAF-MAD), MAF (Maximal Age First) , and round-robin in all considered practical scenarios.