# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 3 Classification of Articles
### Data Streaming Applications  
For some authors, systems that manipulate data streams are real-time due to the characteristics that these systems must present, which are: fast response, low latency, and continuous data processing. The eight articles belonging to this category are presented below:  
[PERSON] et al. [92] propose a mobile edge computing-based platform on which physical devices are deployed on buses, where a descriptive analysis is implemented to analyze real-time data streams.  
[PERSON] et al. [93] propose a context-aware real-time data analysis platform for fog computing called CARDAP. It is a flexible and extensible generic platform capable of efficiently deploying distributed Cyber-Physical Social Systems (CPSS) applications such as MCS (Mobile Crowdsensing) that execute on demand.  
In the research of [PERSON] et al. [94], a virtualized network computing architecture based on the container concept is proposed. The proposed architecture operates as middleware and exploits the native capacity of containers to enable dynamic real-time scaling of available computational and network virtualized resources. The main objective is to reduce the total energy consumed per slot.  
[PERSON] et al. [95] propose a fog framework for a smart and secure vehicular environment applied to video monitoring fog computing to improve crime detection cost-effectively. The proposed framework, called Fog-FISVER, aims to support real-time, autonomous crime detection for public transport services.  
[PERSON] et al. [96] propose a real-time surveillance system architecture based on the edge computing approach. The goal of the proposed architecture is to adjust computing power resiliently and dynamically route data to edge servers suitable for applications. In addition,resource reallocation and workload balancing scheme in emergencies are presented in the article.  
In [PERSON] et al. [97]'s research, an intelligent surveillance approach to vehicle tracking is proposed. The goal is to explore an intelligent surveillance system to track all vehicles in real-time. Since vehicle tracking is a continuous and geographically distributed task, fog computing infrastructure was used to store data on fog nodes near cameras.  
[PERSON] [98] propose a smart and real-time healthcare information processing system based on fog computing that processes and analyzes data using Hadoop and Apache Spark systems. Data information collected in the data pre-processing step is further processed and analyzed in real-time using the Hadoop and Apache Spark ecosystem.  
Finally, [PERSON] et al. [99] propose a multilayer (IoT-device, edge, fog, and cloud) streaming analytics platform with two-tier fog layer, which comprises streaming and analytics tiers. The goal is to be capable of dealing with a large number of streaming and big data analytics IoT-based Cyber-Physical Systems applications. The fog layer uses advanced streaming and analytics paradigms, such as Apache Kafka, Apache Spark, and Hadoop, to support the effective development of applications that use streaming, long-term data storage, and analytics.