State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination

• URL: http://arxiv.org/abs/2504.15577v1
• Date: Tue, 22 Apr 2025 04:24:16 GMT
• Title: State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination
• Authors: Qingyuan He, Chang Liu, Juecen Zhan, Weiqiang Huang, Ran Hao,
• Abstract summary: This paper addresses the challenge of energy efficiency management faced by intelligent IoT devices in complex application environments.<n>A novel optimization method is proposed, combining Deep Q-Network (DQN) with an edge collaboration mechanism.<n> Experiments are conducted using real-world IoT data collected from the FastBee platform.
• Score: 3.4260861366674105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the challenge of energy efficiency management faced by intelligent IoT devices in complex application environments. A novel optimization method is proposed, combining Deep Q-Network (DQN) with an edge collaboration mechanism. The method builds a state-action-reward interaction model and introduces edge nodes as intermediaries for state aggregation and policy scheduling. This enables dynamic resource coordination and task allocation among multiple devices. During the modeling process, device status, task load, and network resources are jointly incorporated into the state space. The DQN is used to approximate and learn the optimal scheduling strategy. To enhance the model's ability to perceive inter-device relationships, a collaborative graph structure is introduced to model the multi-device environment and assist in decision optimization. Experiments are conducted using real-world IoT data collected from the FastBee platform. Several comparative and validation tests are performed, including energy efficiency comparisons across different scheduling strategies, robustness analysis under varying task loads, and evaluation of state dimension impacts on policy convergence speed. The results show that the proposed method outperforms existing baseline approaches in terms of average energy consumption, processing latency, and resource utilization. This confirms its effectiveness and practicality in intelligent IoT scenarios.

Related papers

• Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning [4.422684054800804]
  This paper proposes an adaptive resource scheduling method based on the A3C reinforcement learning algorithm. The method incorporates an asynchronous multi-threaded learning mechanism, allowing multiple agents to perform parallel sampling and synchronize updates to the global network parameters. The results show that the proposed method delivers high scheduling performance and system stability in multi-task concurrent environments.
  arXiv  Detail & Related papers  (2025-05-01T04:42:48Z)
• Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency [2.1877558143992184]
  Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems. VANETs enable vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing. This research proposes a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation.
  arXiv  Detail & Related papers  (2025-04-29T13:20:02Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network [29.895766751146155]
  Digital twin (DT) can provide real-time status and dynamic topology mapping for Internet of Things (IoT) devices. We develop a dynamic resource scheduling algorithm tailored for the asynchronous federated learning (FL)-based lightweight DT empowered IoT network. Specifically, our approach aims to minimize a multi-objective function that encompasses both energy consumption and latency.
  arXiv  Detail & Related papers  (2024-08-26T14:28:51Z)
• Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach [34.00679567444125]
  We develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes.
  arXiv  Detail & Related papers  (2024-05-20T14:13:22Z)
• Switchable Decision: Dynamic Neural Generation Networks [98.61113699324429]
  We propose a switchable decision to accelerate inference by dynamically assigning resources for each data instance. Our method benefits from less cost during inference while keeping the same accuracy.
  arXiv  Detail & Related papers  (2024-05-07T17:44:54Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning [53.18060442931179]
  We propose the age of semantics (AoS) for measuring semantics freshness of status updates in a cooperative relay communication system. We derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset.
  arXiv  Detail & Related papers  (2022-09-19T11:55:28Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks [22.06811314358283]
  Wireless networks used for Internet of Things (IoT) are expected to largely involve cloud-based computing and processing. In a cloud environment, dynamic computational resource allocation is essential to save energy while maintaining the performance of the processes. This paper models this dynamic computational resource allocation problem into a Markov Decision Process (MDP) and designs a model-based reinforcement-learning agent to optimise the dynamic resource allocation of the CPU usage. The results show that our agent rapidly converges to the optimal policy, stably performs in different settings, outperforms or at least equally performs compared to a baseline algorithm in energy savings for different scenarios.
  arXiv  Detail & Related papers  (2020-10-10T15:16:26Z)
• Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks [10.248295944860963]
  The proposed approach simultaneously selects the best connectivity and processing unit along with the percentage of data to be offloaded by jointly optimizing energy consumption, response-time, security, and monetary cost. The requirements of IoT devices in terms of response-time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy.
  arXiv  Detail & Related papers  (2020-04-30T02:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.