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ssss1 Smart cities data analytics framework.

Challenges:

In addition to that, data have proliferated significantly and are produced from heterogeneous sources. Therefore, the types of data vary from video and images to digits or strings and need particular procedures to convert all of the data into single unit measurements. These measurements enable us to run machine learning algorithms and other DL algorithms on the data readily to make optimum decisions.

Solutions:

To handle the heterogeneous problem, data engineering [22] is responsible for managing and analyzing the input data and adding labels to data left unlabeled. This requires experts and time; thus it is not cost and time efficient. Therefore, leveraging big data algorithms helps to tune and analyze the data properly.

The smart cities promises lead us to an ample proliferation and generation in data from all aspects of the domains and branches. Therefore, such huge amounts of data are at the core of the services generated by the IoT technologies [29]. This section of the framework, data analysis, is imperative because its results lead us to make proper decisions. If this process is not accomplished, the decision made will not be efficient. Thus, a large number of research studies have enhanced the process and yielded better results. In the early era of smart cities, there were only limited data generated every day due to the lack of sensors. Therefore, typical machine learning algorithms were sufficient for data analysis to make a model that can handle the situation and provide enough information to make a decision. However, thanks to technologies, the number of sensors and IoT objects have proliferated, and thus we have huge amounts of data that require big data algorithms like and Hadoop to handle the data [30].