# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 4 Lessons Learned
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Year** & **Application** & **Category** \\ \hline [PERSON] et al. [44] & 2019 & Industrial \\ [PERSON] et al. [49] & 2019 & Health \\ [PERSON] et al. [50] & 2019 & Health \\ [PERSON] et al. [30] & 2016 & Smart City \\ [PERSON] et al. [47] & 2018 & Face Recognition \\ [PERSON] et al. [32] & 2017 & Smart Vehicular \\ [PERSON] and [PERSON] [31] & 2018 & Smart Grid \\ [PERSON] and [PERSON] [46] & 2019 & Smart City \\ [PERSON] et al. [33] & 2018 & Health \\ [PERSON] and [PERSON] [34] & 2017 & Smart Vehicular \\ [PERSON] and [PERSON] [35] & 2016 & Health \\ [PERSON] et al. [51] & 2018 & Asset Tracking \\ [PERSON] and [PERSON] [52] & 2018 & Industrial \\ [PERSON] et al. [36] & 2017 & Smart City \\ [PERSON] et al. [37] & 2016 & Smart City \\ [PERSON] and [PERSON] [48] & 2017 & Health \\ [PERSON] et al. [38] & 2018 & Health \\ [PERSON] et al. [39] & 2018 & Industrial \\ [PERSON] [53] & 2018 & Industrial \\ [PERSON] and [PERSON] [40] & 2018 & Health \\ [PERSON] et al. [54] & 2019 & Health \\ [PERSON] et al. [43] & 2018 & Industrial \\ [PERSON] et al. [41] & 2018 & Security \\ [PERSON] and [PERSON] [42] & 2017 & Smart Home \\ [PERSON] et al. [55] & 2021 & Health \\ [PERSON] et al. [57] & 2021 & Smart City \\ [PERSON] et al. [59] & 2020 & Smart Things \\ [PERSON] et al. [56] & 2020 & Smart Vehicular \\ [PERSON] et al. [55] & 2021 & Industrial \\ [PERSON] et al. [61] & 2018 & Smart City \\ [PERSON] et al. [60] & 2016 & Smart Vehicular \\ [PERSON] et al. [69] & 2019 & Smart Home \\ [PERSON] et al. [62] & 2017 & Protocol \\ [PERSON] et al. [63] & 2018 & Health \\ [PERSON] and [PERSON] [64] & 2016 & Smart Vehicular \\ [PERSON] et al. [70] & 2018 & Smart City \\ [PERSON] et al. [65] & 2018 & Smart Vehicular \\ [PERSON] et al. [71] & 2019 & Algorithm \\ [PERSON] et al. [66] & 2016 & Device-to-Device \\ [PERSON] et al. [72] & 2018 & Face Recognition \\ [PERSON] et al. [67] & 2017 & Industrial \\ [PERSON] et al. [68] & 2018 & Health \\ \[PERSON] et al. [100] & 2019 & Task Scheduling \\ [PERSON] et al. [27] & 2017 & Scheduling \\ [PERSON] et al. [101] & 2018 & Algorithm \\ [PERSON] et al. [103] & 2017 & Task Scheduling \\ [PERSON] et al. [102] & 2018 & Scheduling Algorithm \\ [PERSON] and [PERSON] [104] & 2016 & Task Scheduling \\ [PERSON] and [PERSON] [105] & 2016 & Smart Vehicular \\ [PERSON] and [PERSON] [106] & 2018 & Scheduling Algorithm \\ [PERSON] et al. [107] & 2017 & Scheduling Algorithm \\ [PERSON] et al. [112] & 2017 & Scheduling Algorithm \\ [PERSON] et al. [110] & 2015 & Industrial \\ [PERSON] et al. [109] & 2018 & Scheduling Algorithm \\ [PERSON] and [PERSON] [111] & 2018 & Industrial \\ [PERSON] et al. [108] & 2017 & Smart Vehicular \\ [PERSON] et al. [91] & 2018 & Smart Things \\ [PERSON] et al. [113] & 2020 & Scheduling Algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)We perform a search, in SCOPUS, for keywords with the following search string: _(\"Fog Computing\" AND (\"Near Real Time\" OR \"Almost Real Time\") AND \"Real Time\")_, and filter to show information from the last six years and only from the engineering and computing areas. The goal of this search was to obtain the trend in the use of temporal concepts in fog computing environments. As a result, we obtained the word cloud shown in Figure 7.  
We can analyze that the near real-time concept is being used in approaches as Big data, Internet of Things, and Remote Sensing, which reinforces that these environments tend not to respond in hard real-time, due to the characteristics they present. In addition, it reinforces that, for these environments, there is a greater concern with the fast and continuous processing of data, thus generating a better effort in response time. Therefore, we intend, as future work, to show that a distributed IoT environment with fog computing configurations offers a better effort to perform its tasks in an acceptable time.