Design of an Efficient & Highly Scalable Blockchain Model using Graph Learning for Selectively Truncated Shards

The recent spur in blockchain applications calls for scalable and efficient blockchain models to support an ever-increasing transaction load while keeping energy consumption and overheads at bay. Although extremely powerful, the current blockchain systems still have fundamental limits in scalability and efficiency that considerably increase mining delay, energy consumption, and inconsistencies of packet delivery for large-scale scenarios. This paper presents a new blockchain model, with an efficient ant lion-based bat optimizer and graph-based proof of temporal trust model in high-density networks. ALBO is proposed here to select optimal shard lengths and to truncate the block shards intelligently for improving the scalability of the system. Another parallel has a trust metric-based GPoTT model selecting miner nodes efficiently for its behest of both efficiency and security. The suggested model showed key performance metrics to have considerably improved. The results included mining delay reduction by 10.4%, improvement in energy efficiency by 3.5%, increase in throughput by 4.9%, reduction in overhead by 8.3%, and improvement in packet delivery consistency by 8.5%. These results showcase the potential of the proposed model in revolutionizing the landscape of blockchains toward more scalable, efficient, and trustworthy blockchain systems. The contribution of this paper on the roadmap to an optimized blockchain system is huge. By integrating graph learning with intelligent shard truncation, we have been able to circumvent model limitations and come up with a very highly scalable blockchain system efficient enough for modern applications.