The Cognitive Internet of Things and Big Data

8. Conclusion

In this paper, at first, we have studied and analyzed the existing technologies, tools, and techniques from the related works and several survey papers. This allowed us to select the appropriate technologies for our architecture. In addition, we have surveyed the most important aspects of the IoT and CIoT. Unlike other IoT papers, this paper focused on limitations of existing works. Hence, it could help future IoT researchers who would create an architecture based cognitive IoT concept that fits well with the scalability of the business requirements. Moreover, the proposed solution merges DL and DWH in order to solve the limitation due to velocity, variety, and volume. Thus, the quantity of collected data (structured, unstructured, semi-structured) from several device features. Moreover, the tool solve the speed of data flow. In the future, we intend to enhance the tool by considering new methods such as deep learning for extracting and recognizing the data from data sources that could be used to improve the data collection; in addition to real cases of the implementation of the approach.