Surrogate Models for IoT Task Allocation Optimization
The Internet of Things (IoT) is a rapidly growing field with various applications. Efficient task allocation in IoT networks is crucial for network performance. Existing optimization algorithms rely on simulation, which can be computationally expensive for low-power devices. Our published paper proposes two surrogate models, an analytical model and a Graph Neural Network (GNN) model, to evaluate task allocation in IoT networks. These models replace costly simulations and improve reactivity to network changes. The models are evaluated using different network and task topologies, demonstrating promising results. Future improvements will include augmenting the training data to better deal with node outages and applying the surrogate models to simulations including network dynamics.
Task (blue) and network (orange) Graphs used for the evaluation of the surrogate models.
- Dominik Weikert, Christoph Steup and Sanaz Mostaghim
- Surrogate Models for IoT Task Allocation Optimization
- GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion,July 2022, https://doi.org/10.1145/3520304.3528943