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From
Temporal Analysis of an IoT Distributed Ledger Simulation using NetLogo and Agents.jl
Peter Kimemiah Mwangi, Stephen T. Njenga, Gabriel Ndung’u Kamau
Journal of Computer Sciences and Applications
.
2025
, 13(1), 16-28 doi:10.12691/jcsa-13-1-2
Table 1. Model Input and Output Parameters
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Table 2. Experimental Tools Setup and Configuration, Two Machines Used in the Experiment
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Table 3. Setup, Average Execution Time, Standard Error, Standard Deviation, and Speedup for NetLogo and Julia Simulations for Different Node Sizes on TOi3 Hardware
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Table 4. Experiment, Average Execution Time, Standard Error, Standard Deviation, and Speedup for NetLogo and Julia Simulations for Different Node Sizes on TOi3 Hardware"
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Table 5. Linear and Non-Linear Curve Fi for Setup and Run Experiment Speed-up NetLogo versus Julia
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Table 6. Setup, Average Execution Time, Standard Error, Standard Deviation, and speed-up for NetLogo and Julia simulations at different Node Sizes on HPi5 hardware
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Table 7. Experiment, Average Execution Time, Standard Error, Standard Deviation, and Speedup for NetLogo and Julia Simulations for Different Node Sizes on HPi5 hardware
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Table 8. Linear and Non-Linear Curve Fi for Setup and Run Experiment Speed-up NetLogo versus Julia
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Table 9. Time Taken to Execute 30 runs, Including Setup Time and Experiment Time on TOi3 and Comparing JL and NL
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Table 10. Time Taken to Execute 30 runs, Including Setup Time and Experiment Time on HPi3 and Comparing JL and NL
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Table 11. Regression Results for the “Setup_Time” Model, Including the Coefficients for the Intercept, Tool (NetLogo), Machine (TOi3), Number of Nodes, and Number of Transactions. Provided are the Estimated Coefficients, Standard Errors, t-values, p-values, and 95% Confidence Intervals for each Variable in the Model
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Table 12. Regression results for the “exp_time” Model, Including the Coefficients for the Intercept, Tool (NetLogo), Machine (TOi3), Number of Nodes, and Number of Transactions, Provided are the Estimated Coefficients, Standard Errors, t-values, p-values, and 95% Confidence Intervals for each Variable in the Model
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