# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 1 Introduction
There are two approaches for adding efficient active FNs: the extraction of awaking nodes and collaborative neighbouring discovery nodes. The proposed method uses both approaches, resulting in conflict with other discovery methods. This indicates that two or more types of technologies can be combined. The inefficient FNs are replaced with efficient active FNs, which have a higher residual energy. Here, the FNs are subdivided into a fog control node for monitoring and fog access node for task-processing. The main motive of this work is to deploy the FoG-IoT network in a fault-free environment for task-processing using adequate fog access nodes. This encourages us to suggest a minimum-cost-based neighbouring candidate node discovery method for replacing the faulty nodes with alternate fog access nodes, where the faulty fog nodes are identified based on residual energy. To track the information request received from IoT by the newly assigned fog node, the replication and pre-emptive forwarding method is also suggested for task-processing, either in the same fog server or a neighbouring fog server. In the proposed method, the aforementioned replacement and removal of inefficient Fog access nodes with a threshold (Er < 0.1) are monitored by the Fog control nodes present in the network. Fog control nodes identify active fog access nodes, which have a minimum cost, and act as an intermediary to offload the tasks received from IoT devices among other fog access nodes with appropriate residual energy. The proposed architecture aims to enhance the task-offloading process in a fault-free fog network by optimizing the energy level and cost. The main contributions of this research article are as follows:  
* Propose a Priority Task-based Fault-Tolerance (PTFT) mechanism in Fog networks, which identifies the faulty Fog nodes with minimal residual energy. * Update the energy level of Fog access nodes automatically after a task is executed by the Fog control nodes or the Fog server. * The Fog control nodes also help to find the cost function for each Fog node with a minimum number of hops connected between them. Identify and remove the faulty Fog nodes if the residual energy (Er) of the Fog node is less than 0.1. * Update the cost function of the Fog network. * Propose a Minimum-Cost-based Neighbour Candidate Node Discovery (MCNCND) approach, which identifies the linked neighbouring candidate Fog access nodes with minimal costs for task-offloading. * Initially, the source Fog access node will broadcast its own cost to the linked neighbouring candidate Fog access nodes, and will receive the cost of all linked Fog access nodes. * Then, it will offload the task to the linked neighbouring Fog access node with the minimum cost. * This will be repeated until it finds the candidate Fog access nodes with the minimum cost in the Fog network. * Develop and deploy the Replication and Pre-emptive Forwarding (RPF) process, which tracks the source information of the Fog access node. * Pre-empt and forward the source Fog access node information to the linked neighbouring candidate Fog access node during the task-offloading process within the source Fog server. * Replicate the source Fog node information in the neighbour(s) Fog server with minimum cost when all Fog access nodes are busy/faulty in the source Fog server. This research work is organized as follows: Section 2 contains a detailed literature review of various existing node discover, replication, and forwarding approaches for reliable fault-free transmission in Fog computing. The proposed system model is discussed in Section 3, and Section 4 elaborates on the proposed algorithms. The experimental analysis and discussion are presented in Section 5, and Section 6 concludes the proposed research and provides directions for future research.