# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 4 Lessons Learned  
The concept of real-time has had several biases and has been used according to the approach proposed in the articles. The classical literature initially proposed that real-time systems had to perform their tasks within a deadline, regardless of the velocity of such processing. The actual Internet of Things scenarios present a continuous data sending and receiving, which creates the requirement for an enhanced processing and response. Fog computing, in turn, provides local processing and, consequently, low latency and fast response. Therefore, it is increasingly common to find articles that associate the speed of processing with real-time systems, which corroborates the concept of real-time presented by [PERSON] and [PERSON] [114]. The [PERSON] et al. [15] article presents a survey that shows the association of the data stream concept with systems that respond in real-time. In that survey, the articles were classified according to the way real-time was understood by the authors, based on big data and data stream. Big data tools propose real-time data execution by handling streaming data. With our research, we could see that the fog computing configuration has been widely used, as it provides benefits such as low latency and fast response, due to the processing, storage, and treatment of the data being close to the edge. Moreover, an IoT environment has a need to get a fast response due to the sensitivity of the information generated by the applications. In other words, there is a greater concern in processing the data immediately after its arrival than with the response time constraint and with system predictability, which directs this type of environment to offer near real-time responses. Our research performed the classification and categorization of the articles since it was possible to verify a pattern regarding the authors' understanding of the concept of systems with temporal requirements. We have divided the articles into five categories in which three of them present similar real-time concepts. In other words, the \"Faster Response\" and \"Low Latency\" categories are directly related to the \"Fog Computing Concept\" category, since the main characteristics of fog computing are to provide local processing and thus low latency and fast response. We decided to split these articles, as the authors accurately presented how their application should answer, as it was the real-time response, even using the fog computing approach. The category \"Data streaming\" included articles whose purpose was the use of video monitoring and data streams. The articles that belong to this category corroborate what was presented by [PERSON] [115], since ordering in the input and output of the data are necessary, mainly concerning the video frames. On the other hand, the articles belonging to the category \"Time, Delay or Latency Constraint\" propose mostly (68.75%) the implementation of algorithms or task scheduling methods. Only 31.25% propose the implementation of time constraints for industrial and smart things applications. However, there is concern about the need for guarantees and predictability in the processing and delivery of health and smart vehicular data [116, 117, 118]. These are time-sensitive applications in which delay or failure can cause catastrophic consequences. Table 4 shows the summary list of selected articles, the year of publication, the application used, and the category in which it was inserted.