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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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Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm.

Nuruzzaman Faruqui1, Sandesh Achar2, Sandeepkumar Racherla3

  • 1Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka 1216, Bangladesh.

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Summary
This summary is machine-generated.

This study introduces Machine Vision at the IoT Edge (Mez) and a Grid Sensing (GRS) algorithm to reduce cloud costs for security camera networks. The combined approach significantly cuts bandwidth and storage needs for organizations.

Keywords:
EdgeIaaSIoT cameraMezbandwidthcloudmachine visionoptimizationsecurity gridstorage

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Area of Science:

  • Computer Science
  • Network Engineering
  • Cloud Computing

Background:

  • High-Definition (HD) Internet of Things (IoT) cameras are increasingly used for security, but transmitting HD video data strains cloud infrastructure.
  • Large-scale security grids face challenges in minimizing cloud network bandwidth and storage costs.
  • Optimizing cloud resource allocation is crucial for cost-effective surveillance systems.

Purpose of the Study:

  • To present an application of Machine Vision at the IoT Edge (Mez) technology combined with a novel Grid Sensing (GRS) algorithm.
  • To optimize cloud Infrastructure as a Service (IaaS) resource allocation for large-scale security grids.
  • To achieve significant cost minimization in cloud bandwidth and storage.

Main Methods:

  • Application of Machine Vision at the IoT Edge (Mez) technology.
  • Integration of a novel Grid Sensing (GRS) algorithm for IoT camera networks.
  • Utilizing a network latency feedback module within Mez for video frame transformation.
  • Automatic ranking of bandwidth requirements by different IoT nodes via the GRS algorithm.

Main Results:

  • Achieved a 31.29% reduction in bandwidth requirements.
  • Demonstrated a 22.43% reduction in storage requirements.
  • Minimized the entire grid's throughput through optimized resource allocation.

Conclusions:

  • The proposed system effectively optimizes cloud Infrastructure as a Service (IaaS) resource allocation for IoT camera security grids.
  • Mez technology and the GRS algorithm offer a viable solution for reducing bandwidth and storage costs.
  • The integration leads to significant cloud resource optimization and cost savings for organizations.