| Fellowship University of Alberta | Award | 2020-01-14 | Musilek, P. |
| Senior Research FellowshipRWTH Aachen Senior Research Fellowship, Institute of Automation of Complex Power Systems, E.ON Research
Center, RWTH Aachen, Germany University of Alberta | Award | 2019-07-01 | Musilek, P. |
| ETX: A Flexible Simulation Framework for Design of Transactive Energy SystemsS. ZHANG, D. MAY, P. ATRAZHEV, M. Gul, A. LEach, T. Weis, P. Musilek, ETX: A Flexible Simulation Framework for Design of Transactive Energy Systems, 32nd IEEE Canadian Conference of Electrical and Computer Engineering (CCECE 2019), Edmonton, AB, Canada, May 5-8, 2019 University of Alberta | Publication | 2019-05-05 | Steven (Shida) Zhang, Daniel May, Peter Atrazhev, Musilek, P. |
| Towards A Scalable DAG-based Distributed Ledger for Smart CommunitiesIn recent years, Distributed Ledger Technology (DLT) has been playing a more and more important role in building trust and security for Internet of Things (IoT). However, the unacceptable performance of the current mainstream DLT systems such as Bitcoin can hardly meet the efficiency and scalability requirements of IoT. In this paper, we propose a scalable transactive smart homes infrastructure by leveraging a Directed Acyclic Graph (DAG) based DLT and following the separation of concerns (SOC) design principle. Based on the proposed solution, an experiment with 40 Home Nodes is conducted to prove the concepts. From the results, we find that our solution provides a high transaction speed and scalability, as well as good performance on security and micropayment which are important in IoT settings. Then, we conduct an analysis and discuss how the new system breaks out the well-known Trilemma, which claims that it is hard for a DLT platform to simultaneously reach decentralization, scalability and security. Finally, we conclude that the proposed DAG-based distributed ledger is an effective solution for building an IoT infrastructure for smart communities. University of Alberta | Publication | 2019-02-01 | Caixiang (Stephen) Fan, Sara Ghaemi, Khazaei, H., Musilek, P. |
| Dependable and Autonomic Computing Platform for Managing Transactive MicrogridsThis a poster in which we outlined the objective of the project and initial literature review that we had done. University of Alberta | Activity | 2018-10-02 | Sara Ghaemi, Khazaei, H., Musilek, P. |
| T-REX: Transactive Renewable Energy eXchangeParticipation in Energy.AI^3 Accelerator - a workshop organized by the Energy Futures Lab University of Alberta | Activity | 2019-10-01 | Musilek, P., Daniel May, Peter Atrazhev, Steven (Shida) Zhang |
| A Comprehensive Review of Blockchain Consensus MechanismsSince the advent of distributed ledger technologies, they have provided diverse opportunities in a wide range of application domains. This article brings a comprehensive review of the fundamentals of distributed ledger and its variants. Analyzing 185 publications, ranging from academic journals to industry websites, it provides a comparative analysis of 130 consensus algorithms using a novel architectural classification. The distribution of the reviewed algorithms is analyzed in terms of the proposed classification and different application domains, along with the applicability of each class among the top 10 platforms in the most prominent blockchain application domains. Additional conclusions are drawn from the evolution of consensus mechanisms, and the analysis concludes envisaging future prospects for consensus as an important part of distributed ledger technology. University of Alberta | Publication | 2021-03-12 | "Bahareh Lashkari", Musilek, P. |
| DAG-based Distributed Ledger Performance Evaluation: A Hybrid Model for IOTAIn recent years, distributed ledger technologies (DLTs) have witnessed an exponential growth in both academy and industry. Blockchain (BC) technology has been playing a more and more important role in building trust and security for Internet of Things (IoT). However, the poor performance of the current mainstream BC systems can hardly meet the efficiency and scalability requirements of IoT. In this paper, we firstly conduct a comprehensive literature review on the performance evaluation work for DLTs including simulation and analytical modeling. Then, we conduct a research on DAG-based DL performance evaluation. Specifically, simulations and experiments are conducted under the same settings to explore the system’s performance behaviours by comparisons. From the collected data, two models are built to answer the questions on transaction confirmation rate (confirmed transactions per second, CTPS): Black-box Model and Hybrid model. The Black-box Model only takes all data to seek the mathematical relationship between transaction arrival rate $\lambda$ and the conformation rate CTPS. And, we find that this is an approximate linear relationship under the COO consensus and low $\lambda$s. The Hybrid model contains both fitted confirmed transactions distribution on each DAG layer from the latest issued milestone, and the analytical model combined with confirmations ratio analysis between the latest two milestones. This model tells more detailed findings on confirmation distributions rather than a simple linear formula, which can provide users more practical directions on IOTA performance choices. Finally, we conclude that these two models provide answers for different questions on performance and are meaningful to the Tangle performance research University of Alberta | Publication | 2019-03-23 | Caixiang (Stephen) Fan, Sara Ghaemi, Khazaei, H., Musilek, P. |
| Performance Analysis of the IOTA DAG-Based Distributed Ledger York University, University of Alberta | Publication | 2021-09-01 | Caixiang (Stephen) Fan, Sara Ghaemi, Khazaei, H., Yuxiang Chen, Musilek, P. |
| Performance Evaluation of Blockchain Systems: A Systematic SurveyIn this paper, we conduct a systematic survey on the blockchain performance evaluation, trying to cover all existing blockchain evaluation studies. We classify all reviewed solutions into two general types, which are empirical analysis and analytical modelling. York University, University of Alberta | Publication | 2020-05-01 | Caixiang (Stephen) Fan, Sara Ghaemi, Khazaei, H., Musilek, P. |
