Data and Information Fusion for Wireless Sensor Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (15 January 2019) | Viewed by 32201
Special Issue Editors
Interests: data fusion; machine learning; Internet of Things (IoT); ambient intelligent; AAL; privacy
Special Issues, Collections and Topics in MDPI journals
Interests: information fusion; artificial intelligence; machine vision; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In today’s digital world, information is the key factor to make decisions. Ubiquitous electronic sources, such as sensors and video, provide a steady stream of data, while text-based data from databases, the Internet, email, chat and VOIP, and social media are growing exponentially. The ability to make sense of data by fusing it into new knowledge would provide clear advantages in making decisions.
Fusion systems aim to integrate sensor data and information in databases, knowledge bases, contextual information, etc., in order to describe situations. In a sense, the goal of information fusion is to attain a global view of a scenario in order to make the best decision.
The key aspect in modern DF applications is the appropriate integration of all types of information or knowledge: Observational data, knowledge models (a priori or inductively learned), and contextual information. Each of these categories has a distinctive nature and potential support for the result of the fusion process.
- Observational Data: Observational data are the fundamental data about a dynamic scenario, as collected from some observational capability (sensors of any type). These data are about the observable entities in the world that are of interest
- Contextual Information: Contextual information has become fundamental to develop models in complex scenarios. Context and the elements of what could be called Contextual Information could be defined as “the set of circumstances surrounding a task that are potentially of relevance to its completion.” Because of its task-relevance; fusion or estimating/inferring task implies the development of a best-possible estimate taking into account this lateral knowledge.
- Learned Knowledge: DF systems combine multi-source data to provide inferences, exploiting models of the expected behaviors of entities (physical models like cinematics or logical models like expected behaviors depending on context). In those cases where a priori knowledge for DF process development cannot be formed, one possibility is to try and excise knowledge through online machine learning processes, operating on observational and other data. These are procedural and algorithmic methods for discovering relationships among, and behaviors of, entities of interest.
This Special Issue invites contributions on the following topics (but is not limited to them):
- Data fusion of distributed sensors
- Context definition and management
- Machine learning techniques
- Integration of data fusion
- Ambient intelligence
- Data fusion on autonomous systems
- Virtual and augmented reality
- Human computer interaction
- Visual pattern recognition
- Environment modeling and reconstruction from images
Prof. Dr. Jose Molina López
Dr. Jesús García-Herrero
Guest Editors
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