# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 3 Classification of Articles
### Time, Latency, or Delay Constraint  
Of the 95 articles, 16 belong to the time, latency, or delay constraint category. In this category, the authors use the concept of real-time as an approach that systems must respond within an acceptable time or delay. The proposals and applications used by the authors are described below. [PERSON] et al. [100] present the problem of mapping and programming mixed-criticality applications on a fog computing platform. Therefore, a Simulated Annealing meta-heuristic strategy is proposed that uses a simulation of the EDF algorithm to generate the schedules. The purpose of the proposal is to determine the task mapping and scheduling table of their activations to maximize the quality of control of tasks control and to meet time requirements, including non-critical real-time tasks. [PERSON] et al. [101] propose a Hungarian-based topology-building algorithm to identify the ideal gateways locations and resources, which consider latency constraints of various applications in a smart city. Each application has its own associated latency constraint, which includes propagation, transmission, processing, and buffering. A candidate location identification algorithm based on computational geometry is also proposed to identify the possible places for the placement of gateways. A resource scheduling algorithm called Privacy-Aware Scheduling in a Heterogeneous Fog Environment (PASHE) in fog computing environments that support heterogeneous micro data centers (MDCs) and user mobility is proposed in the [PERSON] et al. [102] article. The proposed approach considers task requirements, such as security and completion constraints, to identify the most appropriate place for execution. Tasks were classified into three categories according to deadlines and privacy of information: private with a tight deadline, performed at local MDC; semi-private with a tight deadline, it runs on remote MDCs; public domain with a loose deadline executes in the cloud data center. The proposed algorithm also allows the reservation of bandwidth on remote MDCs, so that it is possible to meet the established deadlines. [PERSON] et al. [27] propose a task scheduling model for car systems based on the concept of fog computing that transfers device-level scheduling to the server level. The purpose of the proposed model is to reduce the amount of computation required to perform load balancing. Additionally, a load balancing optimization problem is formulated to minimize missed deadlines and total task execution time. [PERSON] et al. [103] propose a time-sensitive task scheduling mechanism based on fog computing on a layered IoT infrastructure. To this end, the task scheduling problem was formulated for a cloud-fog environment such as the multidimensional backpack, known to be NP-hard. Then, an algorithmic solution based on the ant colony optimization heuristic (ACO) is proposed. The goal is to maximize the total net profit received by service providers by scheduling and positioning tasks to meet application deadline requirements and resource capacity constraints in fog and cloud computing. [PERSON] and [PERSON] [104]'s article proposes a framework for sharing and symbiotic consolidation edge computing capabilities for creating a fog computing environment in real-time with high resource utilization. For this, two scheduling algorithms are proposed: Distributed EDF for Fog (DEF), to maximize resource utilization at the network edge, and Profit-aware Distributed EDF for Fog (PDEF) extends the DEF algorithm making task scheduling profitable as it ensures a minimum amount of profit for the service provider and meets task constraints and device computation rates. [PERSON] and [PERSON] [105] present the time-triggered virtual machine (TTVM) concept that provides a precisely specified virtual interface between a real-time software component and its underlying hardware infrastructure.