Special Issue "Swarms and Network Intelligence"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 30 March 2022.

Special Issue Editors

Dr. Yaniv Altshuler
E-Mail Website
Guest Editor
MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA
Interests: federated learning; machine learning; network theory; social physics; swarm robotics
Prof. Dr. Francisco Camara Pereira
E-Mail Website
Guest Editor
Department of Technology, Management and Economics, Technical University of Denmark, DTU, Kgs. 2800 Lyngby, Denmark
Interests: intelligent transportation systems; machine learning and pattern recognition; transport modeling; computational creativity
Dr. Eli David
E-Mail Website
Guest Editor
Department of Computer Science, Bar-Ilan University, Ramat-Gan 5290002, Israel
Interests: deep learning; evolutionary computation

Special Issue Information

Dear Colleagues,

The last decade has seen a transformative change in the paradigms, tools and processes utilized for the analysis, modeling and design of data-driven systems. A macrolevel-oriented design is gradually being replaced by bottom–up design methodologies, that emphasize micro-level interactions and efficient composability for the emergence of an optimal macroscopic result. Spanning from rapid penetration of autonomous vehicles and decentralized intelligent ride-sharing systems, through the increasing dominance of autonomous trading machines in the equities, currencies and commodities markets, to the growing appetite of decentralized autonomous scalable big-data dependent mechanisms in the intelligence and defense spaces.

This Special Issue aims to be a forum for the presentation of new and improved techniques for the modeling and analysis of swarm architectures and network-driven system designs. In particular, the analysis and interpretation of such approaches in real-world natural and engineered environments falls within the scope of this Special Issue.

We particularly welcome original research works that focus on information-driven theoretic analysis or modeling approach.

  • Information diffusion in real-world networks: temporal influence analysis and mutual information dynamics among nodes in real-life
  • Collective and swarm intelligence: decentralized information processing and decision making
  • Urban computing and smart cities: theoretical aspects of large-scale dynamic metropolitan multi-layered networks
  • Autonomous collaborative security: self-organization and resilient information processing
  • Intelligence transportation systems: information flow in geographically-embedded networks
  • Smart contracts and cryptocurrencies: computation limitation, convergence and information leakage
  • Decentralized finance (DeFi): effects of composability of information processing units on the future of economy

Dr. Yaniv Altshuler
Prof. Francisco Camara Pereira
Dr. Eli David
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

Article
Organisational Structure and Created Values. Review of Methods of Studying Collective Intelligence in Policymaking
Entropy 2021, 23(11), 1391; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111391 (registering DOI) - 24 Oct 2021
Viewed by 482
Abstract
The domain of policymaking, which used to be limited to small groups of specialists, is now increasingly opening up to the participation of wide collectives, which are not only influencing government decisions, but also enhancing citizen engagement and transparency, improving service delivery and [...] Read more.
The domain of policymaking, which used to be limited to small groups of specialists, is now increasingly opening up to the participation of wide collectives, which are not only influencing government decisions, but also enhancing citizen engagement and transparency, improving service delivery and gathering the distributed wisdom of diverse participants. Although collective intelligence has become a more common approach to policymaking, the studies on this subject have not been conducted in a systematic way. Nevertheless, we hypothesized that methods and strategies specific to different types of studies in this field could be identified and analyzed. Based on a systematic literature review, as well as qualitative and statistical analyses, we identified 15 methods and revealed the dependencies between them. The review indicated the most popular approaches, and the underrepresented ones that can inspire future research. Full article
(This article belongs to the Special Issue Swarms and Network Intelligence)
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Article
Leadership Hijacking in Docker Swarm and Its Consequences
Entropy 2021, 23(7), 914; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070914 - 19 Jul 2021
Viewed by 626
Abstract
With the advent of microservice-based software architectures, an increasing number of modern cloud environments and enterprises use operating system level virtualization, which is often referred to as container infrastructures. Docker Swarm is one of the most popular container orchestration infrastructures, providing high availability [...] Read more.
With the advent of microservice-based software architectures, an increasing number of modern cloud environments and enterprises use operating system level virtualization, which is often referred to as container infrastructures. Docker Swarm is one of the most popular container orchestration infrastructures, providing high availability and fault tolerance. Occasionally, discovered container escape vulnerabilities allow adversaries to execute code on the host operating system and operate within the cloud infrastructure. We show that Docker Swarm is currently not secured against misbehaving manager nodes. This allows a high impact, high probability privilege escalation attack, which we refer to as leadership hijacking, the possibility of which is neglected by the current cloud security literature. Cloud lateral movement and defense evasion payloads allow an adversary to leverage the Docker Swarm functionality to control each and every host in the underlying cluster. We demonstrate an end-to-end attack, in which an adversary with access to an application running on the cluster achieves full control of the cluster. To reduce the probability of a successful high impact attack, container orchestration infrastructures must reduce the trust level of participating nodes and, in particular, incorporate adversary immune leader election algorithms. Full article
(This article belongs to the Special Issue Swarms and Network Intelligence)
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Article
Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions
Entropy 2021, 23(7), 801; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070801 - 24 Jun 2021
Viewed by 1334
Abstract
A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the [...] Read more.
A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote. Full article
(This article belongs to the Special Issue Swarms and Network Intelligence)
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Article
Socioeconomic Patterns of Twitter User Activity
Entropy 2021, 23(6), 780; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060780 - 19 Jun 2021
Viewed by 646
Abstract
Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their [...] Read more.
Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual’s income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups. Full article
(This article belongs to the Special Issue Swarms and Network Intelligence)
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Article
Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning
Entropy 2021, 23(1), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/e23010028 - 27 Dec 2020
Cited by 4 | Viewed by 1222
Abstract
In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. [...] Read more.
In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach. Full article
(This article belongs to the Special Issue Swarms and Network Intelligence)
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