DAG Block: Trust Aware Load Balanced Routing and Lightweight Authentication Encryption in WSN

Published In

Future Generation Computer Systems-The International Journal of Escience

Document Type

Citation

Publication Date

3-1-2023

Abstract

A wireless sensor network (WSN) is a staple network architecture that can be widely used to support diverse of applications as smart cities, smart homes, etc. However, energy consumption and security was still a major concern. The blockchain is a potential solution for security provisioning in WSN. However, due to resource constraint nature of the WSN nodes the adoption of classic blockchain leads to high energy consumption, slow transaction processing and poor scalability. To overcome the above issues, we propose Directed Acyclic Graph-based Trust aware Load Balanced Routing (DAG-BTLBR). Initially, the sensor nodes are unequally clustered in an energy-efficient manner to reduce the hotspot problem using Emperor Penguin Colony (EPC) algorithm. After clustering, packets are transmitted through load-balanced routes in secure manner using Adaptive Neuro-based Dual Fuzzy (ANDual Fuzzy) system which reduces the energy consumption by selecting optimal secure routes. The sensor nodes are subjected to meet the security requirements (data integrity, and confidentiality) due to its resource constraint nature. For that, lightweight authentication encryption scheme is designed in which the lightweight encryption is implemented by XTEA with Chaotic Map algorithm, whereas message authentication is implemented by Blake-256 algorithm. Finally, DAG based blockchain is utilized to improve the scalability and resiliency. The proposed DAG-BTLBR model is simulated and implemented using NS3 (version 3.26), and compared with existing works in terms of validation metrics such as end-to-end delay, time consumption, packet loss rate, and energy consumption, maximizing the throughput. The experimental results shows that the proposed work outperforms better than the existing works.

Rights

Copyright 2022 Elsevier

DOI

10.1016/j.future.2022.10.011

Persistent Identifier

https://archives.pdx.edu/ds/psu/39608

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