Why choose the Neural Network approach when there exists a whole slew of well known Machine Learning techniques that can be computed much more efficiently?
Its self-learning capability is easily the most convincing argument. Take, for example, the common linear regression. The burden is on the practitioner to correctly identify the predictor features before feeding them into the model. More often than not, the identification of relevant features is more than half of the battle, even with the help of domain experts. This becomes especially challenging in IoT applications, where so many sensor signals are collected very quickly. On top of identifying relevant signals, determining the meaning sequence and patterns out of these signals can be just as difficult. Neural Networks with their self-learning mechanisms are capable of automatically fishing out the right combination of input signals and identifying the composite event patterns of interest. See: Complex Event Processing.