Dr. Ali Tekeoglu

Dr. Ali Tekeoglu

I'm an assistant professor in the Network Computer Security Department at State University of New York Polytechnic Institute. I do research on Internet of Things Security, Machine Learning for Cybersecurity and Blockchain Technologies.

Parameterized Pulsed Transaction Injection Computation Model and Performance Optimizer for IOTA-Tango

Published in 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, Taichung, Taiwan, 2018

Recommended citation: Bruno Andriamanalimanana, Chen-Fu Chiang, Jorge Novillo, Sam Sengupta, Ali Tekeoglu, "Parameterized Pulsed Transaction Injection Computation Model And Performance Optimizer For IOTA-Tango", The 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC'18), October 27, 2018, Taichung, Taiwan.

Abstract: To keep a cryptocurrency system at its optimal performance, it is necessary to utilize the resources and avoid latency in its network. To achieve this goal, dynamically and efficiently injecting the unverified transactions to enable synchronicity based on the current system configuration and the traffic of the network is crucial. To meet this need, we design the pulsed transaction injection parameterization (PTIP) protocol to provide a preliminary dynamic injection mechanism. To further assist the network to achieve its subgoals based on various house policies (such as maximal revenue to the network or maximum throughput of the system), we turn the house policy based optimization as a 0/1 knapsack problem. To efficiently solve these NP-hard problems, we adapt and improve a fully polynomial time approximation scheme (FPTAS) and dynamic programming as components in our approximate optimization algorithm.