# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 3 Classification of Articles
### Time, Latency, or Delay Constraint
TTVM's goal is to make software component allocation more flexible to provide ways to implement fault tolerance, evolution, and online validation in a time triggered distributed architecture. [PERSON] and [PERSON] [106] propose the Reinforcement Learning Data Scheduling (RLDS) algorithm based on reinforcement learning in order to find the optimal scheduling in case the user requests real-time service. The goal of the algorithm is to minimize the number of services that are not transmitted on time for vehicle applications connected to an SDN network and using the fog computing approach. [PERSON] et al. [107] propose a heuristic scheduling algorithm based on TSN (Time-Sensitive Networking) and the fog computing platform. For traffic with a critical time, TSN uses the type of traffic scheduling depending on GCL (Gate-Control Lists) on each output port of a network switch to decide on the transfer of staggered boards. The purpose of the proposed algorithm is to determine GCLs at runtime so that deadlines are met and queue usage is minimized to accommodate non-critical traffic. In the [PERSON] et al. [91] article, a mechanism is proposed for continuous and real-time detection of the boundary region of objects in a fog computing environment. For this, interpolation algorithms were applied to estimate the sensory data in specific geographical locations in order to obtain a more precise boundary line. In addition, the heuristic algorithm was applied to generate ideal paths for mobile sensors. The objective of the proposed mechanism is to obtain a precise region of the object boundaries and to balance the energy consumption and time of the mobile sensors. [PERSON] et al. [108] propose a VANET architecture that integrates SDN and fog computing to realize flexible network management and low delay services. A fault-tolerant reliability optimization algorithm is also proposed, which considers each case of failures in the processing nodes and optimizes the reliability of the entire system with delay constraints. [PERSON] et al. [109] propose a time-constrained multitasking scheduling algorithm for a hybrid Mobile Cloud Computing architecture based on the ant colony optimization algorithm (ACO) in order to find the optimal scheduling scheme. In addition, the design of the proposed algorithm considers load balancing and power consumption. The goal is to maximize total profit and constraints, which primarily include completion time, resource consumption, and execution order. The [PERSON] et al. [110] article proposes an energy-efficient, time-sensitive wireless computing system for IIoT applications. The system aims to minimize system power consumption by respecting an acceptable delay limit for data collection. For this, the proposed system controls server downtime and the number of network connections. [PERSON] and [PERSON] [111] propose an SDI architecture based on the IIoT platform and fog computing. The purpose of the proposed architecture is to enable real-time task processing with high reliability. To this end, a Computing Mode Selection (CMS) module is designed so that it can be select an optimal computing mode for each task, and a method for determining the execution sequence based on the priority of the tasks. [PERSON] et al. [112] proposes a security-aware real-time scheduling algorithm called RTSANE. The objective of the proposed algorithm is to support the integration between a microdata center (MDC) and a cloud data center (CDC) in order to balance performance and data security and privacy constraints. The algorithm distributes tasks to local MDCs and CDCs or remote MDCs and CDCs according to the level of privacy and time constraints. Finally, [PERSON] et al. [113] propose a real-time dynamic task scheduler which considers deadline and frequency constraints as an emergency degree, at the fog level, in order to process the incoming tasks correctly. A fog network is based on auctions in which tasks rejected by a fog can be accepted at another fog. Collectively, these studies outline a critical role of fog computing.