Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation

• URL: http://arxiv.org/abs/2606.24340v1
• Date: Tue, 23 Jun 2026 09:27:59 GMT
• Title: Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation
• Abstract summary: Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.<n>Traditional energy-aware schedulers struggle with unpredictable workloads due to their reliance on static execution thresholds.<n>We propose two novel, hardware-agnostic dynamic scheduling strategies treating applications as a "black box"
• Score: 2.435798495038311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy. As edge applications grow in complexity, traditional energy-aware schedulers struggle with unpredictable workloads due to their reliance on static execution thresholds or pre-measured, hardware-specific task profiles. To overcome this, we propose two novel, hardware-agnostic dynamic scheduling strategies treating applications as a "black box," requiring no prior energy information: a model-free Reinforcement Learning (RL) agent and an on-the-fly Approximated Prediction (AP) method. We evaluate these methods against an adaptive task rate approach (AsTAR) and optimized static thresholds using a custom-built, physically accurate simulation framework driven by real-world solar data and dynamic LoRa transmission profiles. Rather than claiming universal superiority, our analysis exposes the distinct operational trade-offs of each method: the AP approach delivers lightweight, near-oracle task throughput; the RL agent provides tunable survival-execution balancing; and AsTAR excels at execution pacing across long energy gaps. Finally, we demonstrate that while these advanced strategies provide critical resilience for severely constrained systems with small capacitors, devices with larger energy buffers can efficiently rely on simpler, less computationally expensive static policies.

Related papers

• Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning [7.900374101465939]
  Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks.<n>Existing algorithms suffer from limited training data, which hampers their performance in highly dynamic environments.<n>We propose an attention-based Multi-Objective Reinforcement Learning architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments.
  arXiv  Detail & Related papers  (2026-01-20T15:55:11Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• 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.<n>VANETs enable vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing.<n>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)
• Secure Resource Allocation via Constrained Deep Reinforcement Learning [49.15061461220109]
  We present SARMTO, a framework that balances resource allocation, task offloading, security, and performance.<n>SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs.<n>These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments.
  arXiv  Detail & Related papers  (2025-01-20T15:52:43Z)
• Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes [0.0]
  This paper proposes a reinforcement learning (RL) approach to address the challenges of dynamic load scheduling. Our results show that the RL-based method provides a robust and scalable solution for real-time load scheduling.
  arXiv  Detail & Related papers  (2024-10-23T09:16:22Z)
• PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning [2.0249250133493195]
  We introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a textitPettingZoo-based interface for RL agent deployment in both solo and multi-agent setups. We demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings.
  arXiv  Detail & Related papers  (2024-03-26T12:12:44Z)
• Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning [68.16998247593209]
  offline reinforcement learning (RL) paradigm provides recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. In this paper, we propose an adaptive scheme for action quantization. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme.
  arXiv  Detail & Related papers  (2023-10-18T06:07:10Z)
• A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation [59.56144813928478]
  In current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback. We propose an engineer-friendly control strategy framework that embeds dynamic prior knowledge from building system characteristics simultaneously for system control. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%.
  arXiv  Detail & Related papers  (2023-01-23T09:07:07Z)
• Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent [79.58680275615752]
  We propose an energy-efficient federated meta-learning framework. We assume each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model.
  arXiv  Detail & Related papers  (2021-05-31T08:15:44Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

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.