The use of real prototyping systems allows implementing real-world deployments which permit evaluating new protocols, algorithms and network solutions. This study investigates the problem of 3D indoor redeployment of connected objects in IoT collection networks. The objective is to choose the right positions in which connected objects are added to an initial configuration, while optimizing a set of objectives. To solve this problem, a novel bird's dialect-based particle swarm optimization algorithm (named acMaPSO) is introduced. The new concept of bird's dialect is based on a set of birds which are separated into different dialect groups by their regional habitation and are classified into groups according to their common manner of singing. The obtained numerical results and the real experiments on our testbed prove the effectiveness of the two proposed variants compared with the standard PSO algorithm and a recent state of art of many-objective evolutionary algorithms: the NSGA-III.