Improving Blockchain Scalability with Spatial-Temporal Trust Models and Optimization
Improving Blockchain Scalability with Spatial-Temporal Trust Models and Optimization |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Author : Dharmendra Kumar Roy, Asha Ambhaikar |
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| DOI : 10.14445/22315381/IJETT-V72I11P123 | ||
How to Cite?
Dharmendra Kumar Roy, Asha Ambhaikar, "Improving Blockchain Scalability with Spatial-Temporal Trust Models and Optimization," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 225-237, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P123
Abstract
The escalating demand for scalable blockchain systems necessitates innovative approaches to boost performance and efficiency. Present blockchain models often struggle with scalability and resource efficiency limitations, notably concerning miner performance and consensus mechanisms. To tackle these challenges, this study introduces a groundbreaking spatial-temporal trust model harnessing miners’ unique attributes to optimize blockchain deployments. Our model integrates spatial metrics like miner proximity and energy levels with temporal aspects such as mining delays and past operation efficiencies, forming the core of the Proof of Miner Performance Trust (PoMPT) consensus. PoMPT ensures dependable and efficient miner performance. At the heart of our approach lies the Bat Grey Wolf Optimizer (BGWO), an inventive algorithm merging the Bat Optimizer with the Grey Wolf Optimizer (GWO) process. This optimization strategy crucially shards the blockchain to enhance Quality of Service (QoS) by distributing the workload optimally among miners. The BGWO’s fitness function is directly shaped by spatial and temporal QoS metrics, ensuring dynamic and performance-driven sharding for diverse use cases. Empirical evaluation, particularly in medical data contexts, showcases our model’s superiority over existing blockchain consensus and sharding methods. Our proposed model exhibits significant improvements: an 8.5% boost in energy efficiency, 9.4% in processing speed, 4.9% in throughput, and 6.5% in packet delivery ratio. These enhancements hold numerical significance and lay the groundwork for more sustainable and efficient blockchain deployments, particularly in critical sectors like healthcare. In conclusion, this study tackles the pressing issue of blockchain scalability and introduces a robust framework for integrating spatial and temporal metrics into blockchain technology. The successful implementation of PoMPT and BGWO underscores the potential of meta-heuristic-based approaches in revolutionizing blockchain efficiency and performance, marking a significant stride in blockchain technology.
Keywords
Bat grey wolf optimizer, Blockchain scalability spatial-temporal trust, PoMPT, Energy efficiency, Quality of service.
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