3D indoor redeployment in IoT collection networks: a real prototyping using a hybrid PI-NSGA-III-VF

The 3D indoor redeployment of connected objects in IoT collection networks is a complex problem that influences the overall performance of the network. In this paper, we aim to resolve this problem using a real prototyping system based on a real-world deployment. The aim is to choose the best positions to add a set of connected objects while optimizing a set of objectives. The used approach is based on a new hybrid optimization algorithm that combines a strategy of incorporation of user preferences (PI-EMO-VF) with a many-objective recent variant of the genetic algorithms (NSGA-III). The obtained numerical results and the real experiments on our testbeds prove the effectiveness of the proposed approach compared with another recent optimization algorithm (MOEA/DD).