MoTiCPS: Energy Optimization on Multi-Objective Task Scheduling in IoT Integrated Cyber Physical Systems

Osprey Optimization Algorithm was implemented in Java and used as the task scheduler for iFogsim.

Metaheuristics

1. Osprey Optimization Algorithm (OOA): A new bio-inspired metaheuristic algorithm for solving engineering optimization problems
   Mohammad Dehghani and Pavel Trojovský https://doi.org/10.3389/fmech.2022.1126450
   Matlab implementation: https://www.mathworks.com/matlabcentral/fileexchange/124555-osprey-optimization-algorithm
   
2. African Vultures Optimization Algorithm (AVOA): A new nature-inspired metaheuristic algorithm for global optimization problems.
   Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili https://doi.org/10.1016/j.cie.2021.107408
   Matlab implementation: https://www.mathworks.com/matlabcentral/fileexchange/94820-african-vultures-optimization-algorithm
3. Golden Eagle Optimizer (GEO): A nature-inspired metaheuristic algorithm.
   Abdolkarim Mohammadi-Balani, Mahmoud Dehghan Nayeri, Adel Azar, Mohammadreza Taghizadeh-Yazdi https://doi.org/10.1016/j.cie.2020.107050
   Matlab implementation: https://www.mathworks.com/matlabcentral/fileexchange/84430-golden-eagle-optimizer-toolbox

Overview

MoTiCPS introduces a novel approach for task scheduling and resource allocation in fog computing environments, designed explicitly for IoT-integrated cyber-physical systems (CPS). The method leverages the Osprey Optimization Algorithm (OOA) to improve task reliability, balance resource use across edge devices, and optimize the performance of fog nodes under real-time constraints.

Key Features

• Multi-Objective Optimization: MoTiCPS optimizes for multiple objectives, including energy consumption, task makespan, and reliability.
• Task Scheduling Algorithm: A novel scheduling algorithm based on the Osprey Optimization Algorithm (OOA) ensures efficient task execution and resource management.
• Reliability Enhancement: The method employs a primary/backup fault-tolerance technique to enhance task reliability and system robustness.
• Energy Efficiency: The approach significantly reduces energy consumption in IoT-integrated CPS, making it suitable for energy-constrained environments.

Requirements

• iFogSim: simulations may require the iFogSim simulator for fog computing, a Java-based tool.

Installation

Clone the repository:

git clone https://github.com/yourusername/MoTiCPS.git

You can adjust the configurations in the JSON file to change the parameters such as the number of tasks, fog nodes, energy settings, etc.

Contact

If you have any inquiries, don't hesitate to contact Mohsen Ansari at ansari@sharif.edu or Abolfazl Younesi at abolfazl.yunesi@sharif.edu.