Efficient Task Allocation in Internet of Things Using Lévy Flight-Driven Walrus Optimization

The rapid growth of the Internet of Things (IoT) has presented a significant challenge in efficiently managing energy-aware task distribution over heterogeneous devices. Optimizing the efficient use of resources in terms of energy consumption is critical when considering IoT device resource-constrained environments. This study proposes a new IoT task distribution resolution mechanism using an Enhanced Walrus Optimization Algorithm (EWOA). EWOA incorporates sophisticated techniques, such as Lévy flight processes and augmented exploration-exploitation, and thus is best suited to complex and dynamic IoT environments. This study proposes an EWOA to assign effective tasks considering device capability compatibility and reduced energy consumption. Simulations over benchmark IoT scenarios validate that the EWOA outperforms current approaches in terms of efficiency in terms of energy consumption, convergence, and robustness. In conclusion, improvements in minimizing energy consumption, enhancing task execution performance, and efficient use of resources in IoT networks have been emphasized significantly. In this work, the EWOA was proven to be an effective tool for IoT NP-hard optimization problem resolution and opens doors for future work in utilizing sophisticated metaheuristic algorithms for use in energy-constrained environments.

@article{CHEN2025,
  title     = {Efficient Task Allocation in Internet of Things Using Lévy Flight-Driven Walrus Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yaozhi CHEN},
  doi       = {10.14569/IJACSA.2025.0160555},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160555}
}