IoT 16(7): e5

Research Article

An analytical model of information spreading through conjugation in bacterial nanonetworks

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  • @ARTICLE{10.4108/eai.14-12-2015.151107,
        author={G.  Castorina and L.  Galluccio and S.  Palazzo},
        title={An analytical model of information spreading through conjugation in bacterial nanonetworks},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={2},
        number={7},
        publisher={ACM},
        journal_a={IOT},
        year={2015},
        month={12},
        keywords={Information spreading, bacterial nano networks},
        doi={10.4108/eai.14-12-2015.151107}
    }
    
  • G. Castorina
    L. Galluccio
    S. Palazzo
    Year: 2015
    An analytical model of information spreading through conjugation in bacterial nanonetworks
    IOT
    EAI
    DOI: 10.4108/eai.14-12-2015.151107
G. Castorina1, L. Galluccio1, S. Palazzo1
  • 1: CNIT Research Unit at Dipartimento di Ingegneria Elettrica Elettronica e Informatica, University of Catania, Italy

Abstract

Molecular communications are a powerful tool to implement communication functionalities in environments where the use of electromagnetic waves becomes critical, e.g. in the human body. Molecules such as proteins, DNA, RNA sequences are used to carry information. To this aim a novel approach relies on the use of genetically modi ed bacteria to transport enhanced DNA strands, called plasmids, where information can be encoded and then transferred among bacterial cell using the so called conjugation process. Information transfer is thus based on bacteria motility, i.e. self-propelled motion which under appropriate circumstances is exhibited by certain bacteria, which is still not completely understood. In this paper we propose an analytical model to characterize information spreading in bacterial nanonetworks by employing an epidemic approach similar to the one used to model Delay Tolerant Networks (DTNs) and we show that such modeling can be pro tably used to represent information spreading in conjugation-based bacterial nanonetworks