Recently, the efficient deployment of Wireless Sensor Networks (WSNs) has become a leading field of research in WSN design optimization. Practical scenarios related to WSN deployment are often considered as optimization models with multiple conflicting objectives that are simultaneously enhanced. In the related literature, it had been shown that moving from monoobjective to multi-objective resolution of WSN deployment is beneficial. However, since the deployment of real-world WSNs encompasses more than three objectives, a multi-objective optimization may harm other deployment criteria that are conflicting with the already considered ones. Thus, our aim is to go further, explore the modeling and the resolution of WSN deployment in a many-objective (i.e., optimization with more than three objectives) fashion and especially, exhibit its added value. In this context, we first propose a manyobjective deployment model involving seven conflicting objectives, and then we solve it using an adaptation of the Decomposition-based Evolutionary Algorithm "θ-DEA". The developed adaptation is named "WSN-θ-DEA" and is validated through a detailed experimental study.