Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Abstract

Internet of Things (IoT) based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation. While performing this entire process, there is a high possibility for data corruption in the mid of transmission. On the other hand, the network performance is also affected due to various attacks. To address these issues, an efficient algorithm that jointly offers improved data storage and reliable routing is proposed. Initially, after the deployment of sensor nodes, the election of the storage node is achieved based on a fuzzy expert system. Improved Random Linear Network Coding (IRLNC) is used to create an encoded packet. This encoded packet from the source and neighboring nodes is transmitted to the storage node. Finally, to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector (DSDV) algorithm. Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics. Average residual energy, packet delivery ratio, compression ratio and storage time achieved for the proposed work are 8.8%, 0.92%, 0.82%, and 69 s. Based on this analysis, it is revealed that better data storage system and system reliability is attained using this proposed work.

Keywords

• internet of things (iot)
• data storage management
• fuzzy system
• improved random linear network coding
• energy utilization
• system reliability