Location-specific trusted third-party authentication model for environment monitoring using internet of things and an enhancement of quality of service
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.4.51Keywords:
Trusted third party, Physical unclonable function, Wireless sensor network, Internet of Things, Cluster node, Device fingerprint, X-OR operation.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In the modern digital world, the Internet of Things (IoT) is a modern and advanced technology that interconnects many immeasurable devices. The collection of wireless sensors formed the wireless sensor network. WSN nodes are battery-powered nodes with limited power and computational capability. When using loT-based wireless sensor networks, the nodes are used to communicate with the internet, where there is a need for more secure protocols. In this technological era where time factor plays a key role in everyone’s personal busy life. The need for smart and sensor appliances that work without human intervention can be a solution to some extent for the time factor. IoT is a network where physical objects, vehicles, devices, buildings and many other smart devices are electronically embedded with hardware and software with huge network connectivity. But the communication and data exchange are not that much easy to carry out, it requires a high secured protocol for authentication as well as key encryptions. Besides focusing on secured key distribution importance for enhancing various parameters are also considered which includes, EC additions, multiplications, pairing, hash-to-point operations, security performances, and energy consumption are also considered. In this paper, focuses on “LSTTP” which authenticates the nodes based on the Device Finger Print (DFP) with a Trusted Third Party and proposes the algorithm for enhancing the quality of service parameters such as Throughput, Jitter, Latency and Security.Abstract
How to Cite
Downloads
Similar Articles
- Josephine Theresa S, Graph Neural Network Ensemble with Particle Swarm Optimization for Privacy-Preserving Thermal Comfort Prediction , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- V Babydeepa, K. Sindhu, A hybrid feature selection and generative adversarial network for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, A COVID Net-predictor: A multi-head CNN and LSTM-based deep learning framework for COVID-19 diagnosis , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Rajesh Kumar Singh, Abhishek Kumar Mishra, Ramapati Mishra, Hand Gesture Identification for Improving Accuracy Using Convolutional Neural Network(CNN) , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Bommaiah Boya, Premara Devaraju, Integrating clinical and ECG data for heart disease prediction: A hybrid deep learning approach based on two modalities with particle swarm optimization , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- P. J. Robinson, S. W. A. Prakash, Stochastic artificial neural network for magdm problem solving in intuitionistic fuzzy environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Modenisha U, W. Ritha, Sasitharan Nagapan, Analysing the cost structure of construction sectors considering carbon emission factors , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Sharanagouda N. Patil, Ramesh M. Kagalkar, Analysis of substrate materials for flexible and wearable MIMO antenna for wireless communication , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Vimala S, G. Arockia Sahaya Sheela, Label-Aware Imputation with Cluster Refinement for Smartphone Usage Analytics in Educational Institutions , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Santhanalakshmi M, Ms Lakshana K, Ms Shahitya G M, Enhanced AES-256 cipher round algorithm for IoT applications , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
<< < 6 7 8 9 10 11 12 13 14 15 > >>
You may also start an advanced similarity search for this article.

