# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 3 Classification of Articles
### Fog Computing Concepts
[PERSON] et al. [32] proposes an SDN VANET architecture in a 5G environment, to provide easy management and security based on the SDN security plan. In addition, an SDN VANET real-time attack detection and prevention technique with tracking capabilities is proposed. [PERSON] et al. [33] propose a framework for the hybrid privacy-preserving clinical decision support system called HPCS (Hybrid Privacy-preserving Clinical decision support System) in fog and cloud computing. HPCS uses the fog computing approach to monitor the health condition of patients in real-time reliably. [PERSON] and [PERSON] [34]'s article proposes to combine the capabilities of cognitive and context-sensitive fog computing and IoT to develop a cognitive management framework. The goal is, in a context-sensitive manner, to maximize the number of functions that can be performed on the fog, to determine which functions should be performed on the fog and which ones to perform on the cloud, and how the fog should interact with the cloud. The proposed framework must respond in real-time to support a large number of low latency devices, sensors, and applications. [PERSON] and [PERSON] [35] propose an IoT-based real-time u-healthcare monitoring architecture. The proposed architecture makes use of the advantages of fog computing to take advantage of the proximity between processing and the devices and thus provide real-time monitoring for smart homes and hospitals. [PERSON] et al. [36] propose a mobile computing framework that supports real-time, location aware custom services for crowds. The framework consists of cloud, fog, and edge computing infrastructure. Each fog node covers a geographic zone and offers a subset of a city's services and features based on a user's geographic location. [PERSON] et al. [37] propose a crowdsourcing-based disaster management model using the fog computing and IoT model, called Crowdsourcing-based Disaster Management using Fog Computing (CDMC). In addition, a data transfer mechanism is proposed for the model so that disaster-related IoT data are sent to the fog even if the direct link is not available. The objective of the CDMC is to detect disasters in real-time and to disseminate information in advance to ensure public safety. [PERSON] et al. [38]'s research proposes an assisted application system based on the fog computing approach for animal behavior analysis and health monitoring in a dairy production scenario. The goal of the proposal is to identify farm activities that require a response and decision support in real-time or near real-time. [PERSON] et al. [39] propose a real-time automatic scaling mechanism for industrial applications. The proposed mechanism is called Fuzzy-based Real-time Auto-Scaling (FRAS). FRAS aims to be able to be employed in industries with IoT applications to provide a lightweight, automatic fog-based engine for industrial applications. [PERSON] and [PERSON] [40] propose remote monitoring of the health of smart home patients through the use of the concept of fog computing. The model uses advanced techniques and services such as embedded data mining, distributed storage, and notification services for network devices. In addition, it uses event-based data transmission methodology to process patient data in real-time at the fog layer. In [PERSON] et al. [41]'s research work, a model is proposed to detect collusion attacks in an IoT environment. The proposed model is based on the fog computing approach for real-time monitoring of possible attacks. In addition, a software-defined system layer is used to add flexibility to the configuration of fog nodes, allowing them to detect various types of collusion attacks. [PERSON] and [PERSON] [42] propose a data acquisition mechanism based on the fog computing architecture capable of filtering abnormal data and meeting real-time requirements. The proposed mechanism uses a cooperation method through the use of architectural and algorithmic approaches. [PERSON] et al.