Affiliations: [a] Faculty of Technology, RK University, Rajkot, Gujarat, India | [b] Institute of Management, Nirma University, Ahmedabad, Gujarat, India
Correspondence:
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Corresponding author: Ashwin Raiyani, Institute of Management, Nirma University, Sarkhej – Gandhinagar Hwy, Gota, Ahmedabad, Gujarat 382481, Gujarat, India. E-mail: ashwin.rkcet@gmail.com.
Abstract: This work is to present a new approach – the Resource Allocation Weighted Random Walk (RA-WRW) algorithm, based on IOTA-Distributed Ledger Technology (DLT), for the optimization of transaction processing within the IOTA network. The objectives of improved execution time, better CPU usage, enhanced network efficiency, and better scalability are met in accordance with stringent security measures. The Python-based algorithm considers node resources and transaction weights for the selection of the best tips. The authentication operation of the sender with private keys ensures the integrity of the data, while verification procedures confirm the authenticity of the tips and the validity of transactions. Implementation of this algorithm greatly improves the efficiency of IOTA network transaction processing. The experiment is run on a commonly used dataset available in Kaggle and some system-specific configurations, which depicts a significant improvement in execution time, CPU usage, network efficiency, and scalability. The tips selected are very authentic and consistent, thus proving the efficacy of this algorithm. It proposes a new RA-WRW algorithm based on IOTA-DLT, efficiently fusing resource allocation with weighted random walk strategies for improving the security, efficiency, and scalability in distributed ledger transactions. This has been a colossal development toward the betterment of processing transactions across the IOTA network and feels the pulse of such a newer approach in applications across the real world.
Keywords: Datasets, transaction weights, random walk, tip selection, private key
