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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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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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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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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach.

Zhuoer Hu1, Hui Gao1, Taotao Wang2

  • 1Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a mobile edge computing (MEC)-enabled blockchain system for secure Internet of Things (IoT) data sharing. The proposed adaptive scheme optimizes scalability and throughput, outperforming existing methods.

Keywords:
blockchaincomputation offloadingdeep deterministic policy gradient (DDPG)mobile edge computing

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Area of Science:

  • Computer Science
  • Network Engineering
  • Cybersecurity

Background:

  • Internet of Things (IoT) networks face challenges in secure data storage and sharing due to resource limitations and dynamic environments.
  • Mobile Edge Computing (MEC) offers potential for dynamic computation offloading but requires optimized resource allocation for blockchain integration.
  • Existing solutions often overlook user mobility and dynamic task requirements in MEC-enabled blockchain systems.

Purpose of the Study:

  • To propose a novel MEC-enabled blockchain system for secure IoT data storage and sharing.
  • To design an adaptive resource allocation and computation offloading scheme to enhance the scalability of MEC-enabled blockchain systems.
  • To jointly optimize computation offloading and block generation strategies for improved system efficiency and data security.

Main Methods:

  • Formulated the joint performance optimization as a Markov Decision Process (MDP) to address dynamic IoT scenarios.
  • Developed a Deep Deterministic Policy Gradient (DDPG)-based algorithm to solve the complex MDP problem.
  • Utilized variable consecutive time slots as decision epochs for efficient model training.

Main Results:

  • The proposed DDPG-based scheme significantly improves scalability, measured by MEC computational efficiency and blockchain throughput.
  • Demonstrated superior long-term transactional throughput compared to Deep Q Network (DQN) and greedy schemes.
  • Successfully addressed frequent user movement and dynamic task requirements in IoT networks.

Conclusions:

  • The developed MEC-enabled blockchain system with adaptive resource allocation enhances IoT data security and system efficiency.
  • The DDPG algorithm effectively manages the dynamics and complexity of MEC-enabled blockchain systems.
  • This approach offers a promising solution for scalable and secure data management in future IoT ecosystems.