Data Perturbation for Pervasive Edge Computing Environments Using Iterative Randomised Parallel Variable Perturbation in Wireless Sensor Network

Abstract

Abstract The reduction in the size of Packet Forwarding Nodes (PFNs) is a direct consequence of technological advancements in wireless sensor networks (WSN). Each node has an extra characteristic that results in diminished energy levels. The dispersed PFNs are arranged in a squared cross-sectional area, where each node functions as a Sensing Point (SP) capable of performing diverse applications such as temperature, ambient humidity, sound, and pressure measurements. The packet is partitioned into many fragments, with each fragment being classified as either having an exact or variable length. Each individual packet is sent over numerous Physical Forwarding Nodes (PFNs) on route to the data centre, using the PFNs as the underlying mechanism for transmission. The choice of route forwarding nodes (PFNs) is determined by their respective trust levels. Within the network, designated Packet Forwarding Nodes (PFNs) are strategically positioned to facilitate the delivery of packets to the data centre, ensuring the integrity of the data is preserved throughout the process. The process of selecting certain PFNs involves the calculation of meeting probability, remaining energy, data weight, and security value. The comparative analysis involves evaluating the Iterative Randomised Parallel Variable Perturbation (IRPVP) method against conventional approaches based on various parameters, including delay, link count, resource energy, healthy PFNs, non-healthy PFNs, health ratio computation, remaining energy, control to data ratio, and balancing factor. The results of the simulation demonstrate that the efficacy of the proposed Iterative Randomised Parallel Variable Perturbation (IRPVP) method surpasses that of other traditional algorithms.