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
- Raju Prasad Singh, R.K. Verma, Study of Josephson Effect Between Bose Condensate , The Scientific Temper: Vol. 11 No. 1&2 (2020): The Scientific Temper
- S Prabhakaran, Yugeshkrishnan M, Santhiya M, Danush Kumar S M, Smart Dustbin using IOT , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Rajashree Sunder Raj, Sayar Ahmad Sheikh, Health status of women in slums: A comprehensive study in Raichur District Karnataka, India , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Bhavya Sathenapalli, Kali Charan Sabat, Unleashing entrepreneurial spirit: Driving innovation and growth in a rapidly changing world , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- V. Manibabu, M. Gomathy, Data Quality Management and Risk Assessment of Dairy Farming with Feed Behaviour Analysis Using Big Data Analytics with YOLOv5 Algorithm , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Vinay Viratia, Sandeep Kumar, Shama Praveen, Tarang Shrivastava, Priyanka, Enhancing Trunk Control Balance in Children with Spastic Diplegic Cerebral Palsy: Comparative Effectiveness of the Vestibular Stimulation Technique and Standard Treatment , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Sangeeta Modi, P Usha, Fault analysis in hybrid microgrid for developing a suitable protection scheme , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Sangeeta ., Jitander S. Sikka, Meenal Malik, Static deformation of a two-phase medium consisting of a rigid boundary elastic layer and an isotropic elastic half-space induced by a very long tensile fault , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- AMITESH KUMAR, R.K. VERMA, AN EVALUATION OF SUPER-FLUID DENSITY s AS A FUNCTION OF c T T FOR BCS-BEC CROSSOVER REGIME , The Scientific Temper: Vol. 10 No. 1&2 (2019): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 23 24 25 26 27 28 29 30 31 32 > >>
You may also start an advanced similarity search for this article.

