Special Issue "Consensus and Intelligent Negotiation in Sensors Networks"
Deadline for manuscript submissions: closed (30 September 2020).
Interests: artificial Intelligence; machine learning; edge computing; distributed computing; Blockchain; consensus model; smart cities; smart grid
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Interests: intelligent decision-making; group decision-making; consensus models; fuzzy linguistic modeling; aggregation of information; information retrieval; recommender systems
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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
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 2200 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.
- Machine Learning
- Artificial Intelligence