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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Multi-Agent Deep Reinforcement Learning for Joint Task Offloading and Resource Allocation in IIoT with Dynamic

Yongze Ma1,2, Yanqing Zhao1,2, Yi Hu1,2

  • 1Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study introduces a dynamic priority-aware method for task scheduling in edge computing. It optimizes task offloading and resource allocation, improving efficiency for Industrial Internet of Things (IIoT) systems.

Keywords:
Industrial Internet of Thingscloud–edge–end collaborationmulti-agent deep reinforcement learningresource allocationtask offloading

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • The proliferation of Industrial Internet of Things (IIoT) terminals increases task concurrency and resource demands in edge computing.
  • Existing task scheduling methods struggle to balance offloading, resource allocation, and priority adaptation in competitive edge environments.
  • Cloud-edge-end collaborative computing offers potential but requires joint optimization for efficiency.

Purpose of the Study:

  • To propose a novel two-stage dynamic-priority-aware joint task offloading and resource allocation method (DPTORA) for edge computing.
  • To address the limitations of existing methods in balancing task execution and resource utilization under dynamic priorities and resource constraints.
  • To enhance the efficiency and performance of task scheduling in complex IIoT environments.

Main Methods:

  • Developed a two-stage approach: Stage 1 uses an improved Multi-Agent Proximal Policy Optimization (MAPPO) with a Priority-Gated Attention Module (PGAM) for dynamic priority-aware offloading.
  • Stage 2 formulates resource allocation as a single-objective convex optimization problem, solved via the Lagrangian dual method.
  • Integrated cloud-edge-end collaborative computing principles for cross-layer task offloading.

Main Results:

  • The proposed DPTORA method significantly reduces task latency and energy consumption.
  • DPTORA demonstrates a higher task completion rate compared to existing multi-agent reinforcement learning baselines.
  • The MAPPO-PGAM integration improved the robustness and accuracy of offloading strategies under dynamic priorities.

Conclusions:

  • DPTORA effectively balances task execution and resource utilization in resource-constrained edge computing scenarios.
  • The joint optimization of task offloading, resource allocation, and priority adaptation is crucial for efficient IIoT task scheduling.
  • The proposed method offers a significant advancement for intelligent task scheduling in edge computing environments.