**8. Conclusions**

The main motivation of this work is to contrast low-consumption wireless technologies applied to IoT that are characterized by transmitting small amounts of information in a reliable and flexible way, consume little battery in devices, and have great scalability in the communications system.

Concerning data, we can control the information management of applications in our computers such as photos, videos, mails, etc. But for IoT, it does not work in the same way. It captures information at every moment it considers necessary, instead of capturing it when requested. People usually become subjects for data collecting, instead of users of IoT services, and most cases they are not aware of it. Because it is not easy for people to know when sensors are activated. The privacy stack framework bridge of today's Internet of Things between IoT and user, starts with awareness. It concerns on how IoT services might open communication channels to users and subjects. The IoT protocol work has not gone into privacy data standardization, in other words, the bridge between privacy and public status is minimal. Second, the inference part proposes users to be conscious of the constantly grow of inferences, because data and IoT learning techniques rise their capabilities every day. Inferences helps users to understand what IoT devices learn about them and helps the system to improve privacy with a natural language to understand which are our privacy preferences.

**Author Contributions:** The methodology was proposed by C.D.-V.-S., the investigation and the formal analysis work were done by L.J.V. and C.D.-V.-S., the validation work was done by R.V., the data analysis work was finished by J.-C.L.-P. and L.R.-D., and the original draft was finished by L.J.V., C.D.-V.-S. and R.V.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
