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Andrea Marin

Researcher at Ca' Foscari University of Venice

Publications -  124
Citations -  1071

Andrea Marin is an academic researcher from Ca' Foscari University of Venice. The author has contributed to research in topics: Queueing theory & Markov process. The author has an hindex of 16, co-authored 121 publications receiving 942 citations.

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Journal ArticleDOI

Petri nets for modelling metabolic pathways: a survey

TL;DR: A comprehensive review of recent research on the representation and analysis of metabolic pathways by using Petri nets is presented in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net.
Book ChapterDOI

Interconnected Wireless Sensors with Energy Harvesting

TL;DR: This paper models interconnected wireless sensors with the paradigm of Energy Packet Networks (EPN) and shows that under some reasonable conditions, assuming feedforward flow of data packets and local consumption and leakage of energy, such networks have product form solutions.
Journal ArticleDOI

Methodological construction of product-form stochastic Petri nets for performance evaluation

TL;DR: It is shown that the product-form condition for open nets depends, in general, on the transition rates, whereas closed nets have only structural conditions for a product-forms, except in rather pathological cases.
Journal ArticleDOI

Analysis of stochastic Petri nets with signals

TL;DR: This work shows that SPNs with signals are strict generalisations of G-networks with negative customers, triggers and catastrophes, and appeals to the Reversed Compound Agent Theorem to prove new product-form solutions for SPNs in which there are special transitions.
Journal ArticleDOI

Deep learning for intelligent IoT: Opportunities, challenges and solutions

TL;DR: This work states that next-generation wireless networks have to be robust and self-sustained and seamless integration demands overhauling the whole communication stack from physical layer to application layer, which makes it easy to widely deploy the network.