# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 3 Classification of Articles
### Fog Computing Concepts  
Of the 95 selected articles, 38 belong to this classification, as their proposal is a fog computing approach that uses real-time. [PERSON] and [PERSON] [22] propose an approach to reducing IoT data privacy rich to protect sensitive data throughout its lifecycle. In other words, this research proposes software called Policy Enforcement Fog Module (PEFM), designed to run on a single fog node. PEFM enforces privacy policies in two different ways: directly on real-time applications executing on local fog nodes, and indirectly on non-real-time applications executing on remote fog or cloud. The research of [PERSON] et al. [23] proposes an architecture that uses pedestrian smartphone sensors to obtain data and detect its geographical location and fog nodes. The architecture, called PV-Alert (Pedestrian-Vehicle Alert), aims to use sensor data to predict collisions between vehicles and pedestrians and send alerts if necessary. [PERSON] et al. [24] propose a Cloud of Things (CoT) platform, called ICN-iSapiens, whose goal is to solve smart home challenges through the use of Information Centric Networking (ICN) and Fog Computing concepts. The proposed architecture is composed of three layers: the physical layer, fog layer, and remote cloud layer. The middle tier (fog) consists of smart homes' servers (HSs) to support real-time services and to abstract the heterogeneity of IoT devices. [PERSON] et al. [25]'s research presents a low-cost Wireless Sensor Networks (WSN)-based system for monitoring real-time mobility and outdoor positioning of older adults with Alzheimer's. The purpose of monitoring is to detect anomalous behaviors and decrease the risk of the elderly to wander. Real-time data analysis is performed on the wearable device itself using the Fog Computing approach. [PERSON] et al. [26] propose an algorithm and a mobile real-time fall detection system called UFall. The proposed system uses low-cost pervasive smartphones and employs the fog computing paradigm for real-time detection. It has been implemented to monitor patients with a history of stroke due to the high incidence of falls. [PERSON] et al. [27] propose an intelligent urban traffic surveillance architecture and a single destination multi-vehicle tracking algorithm to reduce complexity and achieve real-time performance. The fog computing-based prototype was designed with four fog nodes and two drones acting as monitoring cameras. The implementation of the concept of fog computing for an e-Health scenario is proposed in [PERSON] et al. [28]'s article. For this, we propose a real-time signal processing algorithm, implemented in fog nodes, near the detection environment. These nodes are responsible for all health-related real-time data processing to enable a set of personalized services. [PERSON] and [PERSON] [29] propose a big data analysis architecture for Real-Time Intelligent Transportation System (RITS-BDA). The architecture utilizes the facilities of the fog computing approach to enable real-time intelligent transport system in the context of the Internet of Vehicles. The RITS-BDA architecture project considers three perspectives: real-time big data analysis, Internet of Vehicles, and computer intelligence (fog and cloud computing). [PERSON] et al. [30] propose an architecture, called Rainbow, that allows the development of applications in smart cities. Rainbow provides the combination of multi-agent systems and fog computing to enable the development of intelligent distributed applications that can interact directly and in real-time with the physical world. [PERSON] and [PERSON] [31] propose a data storage and processing architecture called IF3C (Internet of Things Layer; Fog Data Collection Layer; Fog Edge Layer; Fog Intermediate Computation Layer; Cloud Computing). The purpose of the proposed architecture is to provide real-time services from local storage and Edge Data Centers using fog computing infrastructure. In addition, the Ford-Fulkerson and priority-based queue algorithm is used for load balancing and job scheduling.