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Special Issue "Consensus and Intelligent Negotiation in Sensors Networks"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Prof. Dr. Juan M. Corchado
Website
Guest Editor
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 of BISITE - Bioinformatics Intelligent Systems and Educational, Technology Research Group, Salamanca, Spain
Interests: artificial intelligence; machine learning; edge computing; distributed computing; blockchain; consensus model; smart cities; smart grid
Special Issues and Collections in MDPI journals
Prof. Dr. Enrique Herrera-Viedma
Website
Guest Editor
Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, 18071, Spain
Interests: group decision making; consensus; fuzzy logic; linguistic modeling; bibliometrics; recommender systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Consensus in group decision making involves discussion and deliberation among a group of 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 2000 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.

Keywords

  • Consensus
  • Negotiation
  • Machine Learning
  • Artificial Intelligence

Published Papers (4 papers)

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Research

Open AccessArticle
UAV Mission Planning Resistant to Weather Uncertainty
Sensors 2020, 20(2), 515; https://doi.org/10.3390/s20020515 - 16 Jan 2020
Cited by 1
Abstract
Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to [...] Read more.
Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning. Full article
(This article belongs to the Special Issue Consensus and Intelligent Negotiation in Sensors Networks)
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Open AccessArticle
Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka–Volterra Ecosystem Model
Sensors 2019, 19(21), 4642; https://doi.org/10.3390/s19214642 - 25 Oct 2019
Abstract
Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges [...] Read more.
Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges encountered by IoT networks is that the the data they collect may be unreliable since IoT devices can lose accuracy for several reasons (sensor wear, sensor aging, poorly constructed buildings, etc.). The aim of our work is to study the evolution of IoT networks over time in smart buildings. The hypothesis we have tested is that, by amplifying the Lotka–Volterra equations as a community of living organisms (an ecosystem model), the reliability of the system and its components can be predicted. This model comprises a set of differential equations that describe the relationship between an IoT network and multiple IoT devices. Based on the Lotka–Volterra model, in this article, we propose a model in which the predators are the non-precision IoT devices and the prey are the precision IoT devices. Furthermore, a third species is introduced, the maintenance staff, which will impact the interaction between both species, helping the prey to survive within the ecosystem. This is the first Lotka–Volterra model that is applied in the field of IoT. Our work establishes a proof of concept in the field and opens a wide spectrum of applications for biology models to be applied in IoT. Full article
(This article belongs to the Special Issue Consensus and Intelligent Negotiation in Sensors Networks)
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Open AccessArticle
A Novel Distributed State Estimation Algorithm with Consensus Strategy
Sensors 2019, 19(9), 2134; https://doi.org/10.3390/s19092134 - 08 May 2019
Cited by 2
Abstract
Owing to its high-fault tolerance and scalability, the consensus-based paradigm has attracted immense popularity for distributed state estimation. If a target is neither observed by a certain node nor by its neighbors, this node is naive about the target. Some existing algorithms have [...] Read more.
Owing to its high-fault tolerance and scalability, the consensus-based paradigm has attracted immense popularity for distributed state estimation. If a target is neither observed by a certain node nor by its neighbors, this node is naive about the target. Some existing algorithms have considered the presence of naive nodes, but it takes sufficient consensus iterations for these algorithms to achieve a satisfactory performance. In practical applications, because of constrained energy and communication resources, only a limited number of iterations are allowed and thus the performance of these algorithms will be deteriorated. By fusing the measurements as well as the prior estimates of each node and its neighbors, a local optimal estimate is obtained based on the proposed distributed local maximum a posterior (MAP) estimator. With some approximations of the cross-covariance matrices and a consensus protocol incorporated into the estimation framework, a novel distributed hybrid information weighted consensus filter (DHIWCF) is proposed. Then, theoretical analysis on the guaranteed stability of the proposed DHIWCF is performed. Finally, the effectiveness and superiority of the proposed DHIWCF is evaluated. Simulation results indicate that the proposed DHIWCF can achieve an acceptable estimation performance even with a single consensus iteration. Full article
(This article belongs to the Special Issue Consensus and Intelligent Negotiation in Sensors Networks)
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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 - 20 Mar 2019
Cited by 3
Abstract
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 Negotiation in Sensors Networks)
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