Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks

Premalatha Baskar and Prakasam Periasamy ()
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Premalatha Baskar: School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamilnadu, India
Prakasam Periasamy: School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamilnadu, India

Future Internet, 2024, vol. 16, issue 4, 1-23

Abstract: The exponential growth in data traffic in the real world has drawn attention to the emerging computing technique called Fog Computing (FC) for offloading tasks in fault-free environments. This is a promising computing standard that offers higher computing benefits with a reduced cost, higher flexibility, and increased availability. With the increased number of tasks, the occurrence of faults increases and affects the offloading of tasks. A suitable mechanism is essential to rectify the faults that occur in the Fog network. In this research, the fault-tolerance (FT) mechanism is proposed based on cost optimization and fault minimization. Initially, the faulty nodes are identified based on the remaining residual energy with the proposed Priority Task-based Fault-Tolerance (PTFT) mechanism. The Minimum-Cost Neighbour Candidate Node Discovery (MCNCND) algorithm is proposed to discover the neighbouring candidate Fog access node that can replace the faulty Fog node. The Replication and Pre-emptive Forwarding (RPF) algorithm is proposed to forward the task information to the new candidate Fog access node for reliable transmission. These proposed mechanisms are simulated, analysed, and compared with existing FT methods. It is observed that the proposed FT mechanism improves the utilization of an active number of Fog access nodes. It also saved a residual energy of 1.55 J without replicas, compared to the 0.85 J of energy that is used without the FT method.

Keywords: fault tolerance; fog computing; Internet of Things; neighbour node discovery; pre-emptive forwarding (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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