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BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture

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Abstract

Today we live in a world of digital objects and digital technology; industry and humanities as well as technologies are truly in the midst of a digital environment driven by ICT and cyber informatics. A digital ecosystem can be defined as a digital environment populated by interacting and competing digital species. Digital species have autonomous, proactive and adaptive behaviors, regulated by peer-to-peer interactions without central control point. An interconnecting architecture with few highly connected nodes (hubs) and many low connected nodes has a scale- free architecture. A new bio-inspired analysis methodology (BIAM) environment, an investigation strategy for information flow, fault and error tolerance detection in digital ecosystems based on a scale-free architecture is presented in this paper. In order to extract the information about modules and digital species role, the analysis methodology, inspired by metabolic network working, implements a set of three interacting techniques, i.e., topological analysis, flux balance analysis and extreme pathway analysis. Highly connected nodes, intermodule connectors and ultra-peripheral nodes can be identified by evaluating their impact on digital ecosystems behavior and addressing their strengthen, fault tolerance and protection countermeasures. Two real case studies of ecosystems have been analyzed in order to test the functionalities of the proposed (BIAM) environment and the goodness of this approach.

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Authors and Affiliations

  1. Faculty of Engineering and Architecture, University of Enna KORE, 94100, Enna, Italy

    Vincenzo Conti & Simone Sante Ruffo

  2. Department of Biopathology and Medical Biotechnologies, University of Palermo, 90127, Palermo, Italy

    Salvatore Vitabile

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-higaashi, Higashi-ku, Fukuoka, Japan

    Leonard Barolli

Authors
  1. Vincenzo Conti
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  2. Simone Sante Ruffo
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  3. Salvatore Vitabile
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  4. Leonard Barolli
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Correspondence to Vincenzo Conti.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Conti, V., Ruffo, S.S., Vitabile, S. et al. BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture. Soft Comput 23, 1133–1150 (2019). https://doi.org/10.1007/s00500-017-2832-z

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  • DOI: https://doi.org/10.1007/s00500-017-2832-z

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