Authors - Swapnil M Maladkar, Praveen M Dhulavvagol, S G Totad Abstract - Blockchain technology has emerged as a powerful tool for secure, decentralized data management across various industries, but it faces significant scalability challenges due to the limitations of existing sharding methods. Traditional static sharding approaches often result in inefficient resource allocation, while adaptive sharding techniques can lead to increased complexity and delayed adjustments, hampering overall system performance. This paper proposes an innovative blockchain network management approach by integrating Long Short-Term Memory (LSTM) models with dynamic sharding. This system leverages predictive analytics to optimize real-time sharding adjustments, significantly enhancing blockchain performance. By addressing the shortcomings of both static and adaptive sharding methods, the proposed approach avoids the extra infrastructure and delays associated with Layer 2 solutions. Future research will focus on advancing LSTM techniques, integrating them with other optimization strategies, and testing in real-world scenarios to further enhance scalability and efficiency. This LSTM-integrated dynamic sharding method represents a significant step forward in blockchain network optimization, offering a more efficient and adaptable solution for contemporary blockchain applications. Experimental results reveal a 22% increase in transaction throughput and a 25% reduction in latency compared to conventional static sharding.
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