# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 2 Related Works
### Task Scheduling Based on Cost and Power Consumption  
[PERSON] et al. [21] proposed a genetic algorithm (GA) to solve the Fog task assignment and scheduling problem with the help of control nodes, which assign a service to the Fog control nodes or the Fog cells. The main motive of this optimization is to maximize the number of task assignments in the Fog network (instead of the cloud) to meet the requirements of user applications. In [22], the authors suggested a method for creating an environment that integrates scheduling, sequencing, and partitioning algorithms while ensuring a multi-objective optimization of the competing needs of users and providers. A dynamic-threshold-based task scheduling technique was presented to reduce the transmission and energy consumption of the IoT devices [23]. [PERSON] and [PERSON] [24] highlighted the need for efficient task scheduling in FC to address the challenges modelled by the distributed and resource-constrained environments. They provided various methods and metrics that can be employed to achieve optimal resource allocation and application performance. These earlier studies did not take fault tolerance into account, making them unsuitable for reliability applications. The Honey-Bee-Inspired Load-Balancing (HBI-LB) algorithm was proposed [25] to examine the load-balancing in the IoT-FoG network. [PERSON] et al. [26] proposed a shortest-path resource allocation algorithm for the optimal allocation of resources in D2D, which could handle multiple users simultaneously. A component-based throttled load-balancing technique was proposed in [27], involving VM readers, a free VM owner, and free VM management elements. The VM readers read all VMs that are available. The free VM element temporarily stores free VMs until they are relocated to the free VM management element. The delay energy-balanced task-scheduling (DEBTS) algorithm was suggested to reduce average service latency and delay jitter by minimizing overall energy consumption [28]. [PERSON] [29] proposed the framework to address the challenge of balancing cost, energy-efficiency, and QoS in cloud-Fog environments. They provided a method for scheduling tasks that minimizes the overall cost while ensuring service quality.  
In [30], the authors stated that the processing power is the most critical factor in the Fog network when attempting to accomplish particular tasks. In the Fog network hierarchy, the network communication and storage resources are a few constraints that may differ during the efficient distribution of resources. [PERSON] et al. [31] examined partial task-offloading by assessing the use of centralized/distributed network in edge computing while considering the trade-off between energy consumption and Fog node delays. In [32], the authors developed a simulation of the sound classification system by combining Fogcomputing and cloud computing for urban sound classification. To ensure the scalability of the proposed sensing system, they focused on the trade-off between power consumption, task runtime, and server latency. [PERSON] et al. (2018) examined mobile devices in Fog systems and started a task-offloading strategy that reduces the energy consumption of the mobile devices, as well as the end-to-end response time, for task-processing.