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Special Issue "Consensus and Intelligent Negociation in Sensors Networks"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Prof. Dr. Juan Manuel Corchado Rodríguez

Air Institute
IoT European Digital Innovation Hub
Full Professor at the University of Salamanca
Visiting Professor at the Osaka Institute of Technology
Visiting Professor at the Universiti Malaysia Kelantan
President of the IEEE SMC (Spanish Chapter)
Director BISITE - Bioinformatics Intelligent Systems and Educational
Technology Research Group, Salamanca, Spain
Website 1 | Website 2 | Website 3 | E-Mail
Interests: artificial Intelligence, machine learning, edge computing, distributed computing, Blockchain, consensus model, smart cities, smart grid
Guest Editor
Dr. Enrique Enrique Herrera

Department of Computer Science and Artificial Intelligence, E.T.S. de Ingenieria Informatica y de Telecomunicacion, University of Granada, 18071, Granada, Spain
Website | E-Mail
Interests: artificial intelligence; machine learning fuzzy sets fuzzy decision making computing with words multiple criteria decision making consensus

Special Issue Information

Dear Colleagues,

Consensus in group decision making involves discussion and deliberation between the group sensors, with the aim of reaching an acceptable decision that reflects the opinion of every network member. Traditionally, the consensus reaching problem is modelled theoretically as a multi stage negotiation process. In a dynamic sensor network, the negotiation scenario changes with time. Many real-life problems require the development of dynamic consensus process models that represent the dynamic world effectively and realistically.

This Special Issue calls for innovative work that explores new frontiers and challenges in the fields of consensus reaching models for dynamic environments, intelligent negotiation processes, and AI algorithms that improve decision making in sensor networks. The works in this Special Issue may include new machine learning models, distributed AI proposals, group decision making, consensus processes, negotiation protocols, decision support systems, multi period decision making, adaptive consensus models, and so on, as well as case studies or reviews of the state-of-the-art, in all cases related to consensus or negotiation.

The topics of interest include, but are not limited to, the following:

  • Distributed artificial intelligence models for sensor networks.
  • Machine learning models for dynamic sensor networks.
  • Group decision making for sensor networks.
  • Decision support systems for sensor networks.
  • Consensus processes for sensor networks.
  • Multi period decision making for sensor networks.
  • Adaptive consensus models for sensor networks.
  • Clustering and classification algorithms for sensor networks.
  • Deep and reinforcement learning for Sensor Networks.
  • Fuzzy systems proposals for sensor networks control.
  • Expert systems for sensor networks negotiation.
  • Intelligent real time algorithms for sensor networks coordination and negotiation.
  • Intelligent security proposals for distributed network sensors.
  • Multi agent consensus-based systems.
  • Negotiation in virtual organizations.
  • Consensus-based applications for: energy, IoT, Industry 4.0, etc.

Prof. Dr. Juan Manuel Corchado
Dr. Enrique Enrique Herrera
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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.


  • Consensus
  • Negotiation
  • Machine Learning
  • Artificial Intelligence

Published Papers (1 paper)

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Open AccessArticle Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment
Sensors 2019, 19(6), 1381; https://doi.org/10.3390/s19061381
Received: 5 March 2019 / Revised: 13 March 2019 / Accepted: 15 March 2019 / Published: 20 March 2019
PDF Full-text (2299 KB) | HTML Full-text | XML Full-text
Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by multisensors with a consensus. In [...] Read more.
Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by multisensors with a consensus. In this paper, we first transform the track association problem to multiattribute group decision making (MAGDM), and describe the MAGDM with nested probabilistic-numerical linguistic term sets (NPNLTSs). Then, a consensus model with NPNLTSs is constructed which has two key processes. One is a consensus checking process, and the other is a consensus modifying process. Based on which, a track association algorithm with automatic modification is put forward based on the consensus model. After that, the solution of a case study in practice is given to obtain the corresponding track by the proposed method, and it provides technical support for the track association problems. Finally, we make comparisons with other methods from three aspects, and the results show that the proposed method is effective, feasible, and applicable. Moreover, some discussions about the situation where there is only one echo point at a time are provided, and we give a discriminant analysis method. Full article
(This article belongs to the Special Issue Consensus and Intelligent Negociation in Sensors Networks)

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