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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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...
Distributed Loads01:19

Distributed Loads

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.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Maximum Power Transfer01:16

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...

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Related Experiment Videos

Joint optimization secure and energy-efficient computation offloading framework IoT-enabled edge networks.

Ayesha Shafique1, Mohammad Siraj2, Sadia Din3

  • 1School of IoT Engineering, Wuxi Taihu University, Jiangsu Key (Construction) Laboratory of Intelligent IoT Technology and Applications in Universities, Wuxi, China.

Scientific Reports
|June 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for smart cities, optimizing energy efficiency and security in Edge-Internet of Things (IoT) infrastructure. It enhances network stability and workload management for distributed IoT ecosystems.

Keywords:
Artificial intelligenceEdge computingEnergy efficiencyInternet of thingsTask offloading

Related Experiment Videos

Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Smart cities rely on real-time systems integrating Internet of Things (IoT), edge computing, and emerging technologies.
  • Existing systems face challenges in security, unpredictable communication, load balancing, and device heterogeneity.
  • Optimal offloading in edge-driven schemes is complicated by unreliable links and device diversity.

Purpose of the Study:

  • To introduce a joint optimization framework for energy-efficient, secured computation offloading and dynamic resource allocation in Edge-IoT infrastructure.
  • To address challenges of intelligence, security, load balancing, and optimal offloading in dynamic Edge-IoT environments.
  • To enhance network stability and improve workload management in distributed IoT ecosystems.

Main Methods:

  • Utilizes a Multi-Agent Reinforcement Learning approach for adaptive offloading decisions.
  • Establishes secure communication paths and effective resource allocation under dynamic network interactions.
  • Leverages trusted, decentralized processing points at the network edge for enhanced stability.

Main Results:

  • The proposed framework demonstrates substantial improvements in security and energy efficiency.
  • Achieves enhanced network stability by mitigating energy holes and improving workload management.
  • Outperforms recent state-of-the-art solutions in simulations for Edge-IoT infrastructure.

Conclusions:

  • The joint optimization framework effectively addresses key challenges in Edge-IoT systems.
  • The Multi-Agent Reinforcement Learning approach enables adaptive and secure computation offloading.
  • The research contributes to more stable, secure, and energy-efficient smart city infrastructures.