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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Hybrid multi objective marine predators algorithm based clustering for lightweight resource scheduling and

R Baskar1, E Mohanraj2

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This study introduces a new method for efficiently allocating Internet of Things (IoT) applications to fog computing nodes. The Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) technique optimizes resource use and minimizes network latency for better Quality of Service (QoS).

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ClusteringFog environmentFog pickerHybrid Multi-Objective marine predators algorithmIoT application placementResource scheduling

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) necessitates fog computing for applications requiring low latency and user proximity, complementing traditional cloud infrastructure.
  • Efficient allocation and scheduling of IoT applications within fog environments are crucial for realistic deployment and optimal performance.
  • Existing scheduling methods face multi-objective challenges, including resource waste, network latency, and maximizing Quality of Service (QoS) on fog nodes.

Purpose of the Study:

  • To develop a novel combinatorial technique for fog node allocation and dynamic scheduling of IoT applications.
  • To address the multi-objective nature of fog scheduling by optimizing resource utilization, minimizing network latency, and enhancing QoS.
  • To introduce a lightweight technique for efficient IoT application deployment in fog environments.

Main Methods:

  • Development of the Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) technique.
  • Utilizing the Fog Picker component for allocating IoT components to fog nodes based on predefined QoS parameters.
  • Conducting simulation trials using iMetal and iFogSim, evaluating performance with Hypervolume (HV) and Generational Distance (IGD) metrics.

Main Results:

  • The proposed HMMPACFP scheme demonstrated superior performance compared to benchmarked methodologies.
  • The integration of Fog Picker with HMMPACFP achieved 32.18% faster convergence and 26.92% greater solution variety.
  • The combined approach showed an improved balance between exploration and exploitation rates in the optimization process.

Conclusions:

  • The HMMPACFP technique offers an effective solution for dynamic scheduling and resource allocation in fog computing environments.
  • The developed method successfully addresses the multi-objective challenges inherent in fog node allocation for IoT applications.
  • The findings highlight the potential of HMMPACFP for enhancing the efficiency and performance of IoT deployments in fog computing.