Enhancing IoT blockchain scalability through the eepos consensus algorithm
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.2.02Keywords:
Blockchain, Consensus Algorithm, EePoS, Energy Efficiency, IoT, Proof of Stake.Dimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The integration of blockchain technology with the Internet of Things (IoT) introduces significant scalability, energy efficiency, and security challenges, particularly when using traditional consensus mechanisms like Proof of Work (PoW). IoT networks generate vast amounts of data while operating under resource constraints, necessitating the development of consensus algorithms that balance energy efficiency, transaction throughput, and security. Addressing these challenges is critical for the sustainable adoption of blockchain in IoT ecosystems. This research aims to enhance blockchain scalability and performance in IoT environments through the development of the Enhanced Efficient Proof of Stake (EePoS) consensus algorithm. The objective is to provide a framework that optimizes validator selection, minimizes energy consumption, and ensures robust security against common blockchain threats. The proposed method employs a multi-layered architecture, selective validation, and a behavior-aware penalty-reward system to ensure efficient consensus. Key security metrics, including Probability of Successful Attack (PSA) and Forking Rate (FR), were evaluated to demonstrate the algorithm’s resilience. EePoS reduces PSA by dynamically adjusting validator selection based on stake, behavior, and transaction load while decreasing FR through cluster-based voting and hierarchical aggregation. Experimental results demonstrated 20% lower PSA, 30% reduced FR, and 8% faster consensus time compared to ePoS. Throughput improved to 296 TPS while reducing CPU and memory utilization, ensuring robust performance for resource-constrained IoT networks. The novelty of this work lies in the tailored enhancements to the PoS framework, specifically designed for IoT constraints, making EePoS a scalable, energy-efficient, and secure solution for IoT blockchain integration.Abstract
How to Cite
Downloads
Similar Articles
- Annalakshmi D., C. Jayanthi, An asymmetric key encryption and decryption model incorporating optimization techniques for enhanced security and efficiency , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Deena Merit C K , Haridass M, Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Priya Nandhagopal, Jayasimman Lawrence, ETTG: Enhanced token and tag generation for authenticating users and deduplicating data stored in public cloud storage , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Vijetna Singh, Alka Thakur, ECOLOGICAL ENGINEERING OF MICROALGAE FOR ENHANCED ENERGY PRODUCTION , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- L. Vamsi Narasimha Rao, P.S.Prakash, M.Veera Kumari, Improvement of power system operation using a novel hybrid optimization method for optimal allocation of facts devices in radial transmission line , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Vishal Panghal, Asha Singh, Dinesh Arora, Nidhi Ahlawat, Sunder S. Arya, Sunil Kumar, Horizontal flow biochar amended constructed wetlands as a sustainable approach for rural wastewater treatment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Muhammed Jouhar K. K., K. Aravinthan, A bigdata analytics method for social media behavioral analysis , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Balaji V, Purnendu Bikash Acharjee, Muniyandy Elangovan, Gauri Kalnoor, Ravi Rastogi, Vishnu Patidar, Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- V. Seethala Devi, N. Vanjulavalli, K. Sujith, R. Surendiran, A metaheuristic optimisation algorithm-based optimal feature subset strategy that enhances the machine learning algorithm’s classifier performance , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Arvind K Shukla, Balaji V, Dharani R, M Ananthi, R Padmavathy, Romala V. Srinivas, Precision agriculture predictive modeling and sensor analysis for enhanced crop monitoring , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
<< < 5 6 7 8 9 10 11 12 13 14 > >>
You may also start an advanced similarity search for this article.

