Data Aggregation, Data Fusion and IoT (Internet of Things)
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 748
Special Issue Editors
Interests: data fusion/aggregation; virtual network embedding; network slicing (5g); IoT (Internet of Things); deep reinforcement learning; machine learning; edge/fog/cloud computing
Interests: Internet of Things (IoT); radio resource management; m2m communication; fog/edge/cloud computing; next generation network
Interests: reinforcement learning; multi-agent-based reinforcement learning; deep learning; network engineering
Special Issue Information
Dear Colleagues,
The Internet of Things (IoT) has been one of the most relevant trends in the software industry for the last decade. The IoT environment of the real-world consists of several tiny devices equipped with sensors, actuators, and computational elements. IoT systems aim to create a world that enables the interconnection and integration of things in the physical world and cyberspace. IoT-based healthcare and wearable monitoring systems are increasing in popularity. Additionally, there are several useful applications of IoT, such as monitoring and gathering information from public infrastructure, natural disaster relief, healthcare, smart homes, and industries in smart cities. In the IoT system, information is the key factor to make decisions. However, raw IoT data mostly contains uncertainty and imperfection. Data from a single source may not be sufficient for making an accurate decision. Data aggregation and fusion techniques are applied to combine the data from multiple sources effectively and accurately. The main goal of data aggregation and fusion mechanisms is to achieve high Quality of Services (QoS), including optimal data transmission delay, reliability, and energy consumption. Artificial intelligence-based data aggregation and fusion techniques can effectively abstract the information, and extract important features and knowledge from the data. Some emerging areas would greatly benefit from data aggregation and sensors data fusion such as the Internet of Things (IoT), Industrial IoT (IIoT), autonomous vehicles, deep learning, smart cities, and many other industrial applications. Intelligent data fusion and aggregation are important to improving the accuracy of the decision-making process for the following reasons:
- The IoT system usually operates in a dynamic real-time environment, and thus, it is necessary to establish a smart network that can efficiently adjust its operation according to the operational
- IoT system designers and researchers need to utilize robust AI and ML techniques to make correct and reliable decision-based data gathered from IoT environments.
- In the IoT environment, smart decision-making and control are required. With AI and ML techniques different levels of knowledge can be used to make a decision and the tasks are dynamically performed based on contextual information.
This Special Issue encourages authors from academia and industry to submit new research results from the use of multiple sensor data fusion to generate IoT environments or other industrial applications. Topics of interest include, but are not limited to, the following:
- Artificial intelligence-based sensor data fusion and aggregation;
- Machine learning-based data aggregation for IoT;
- Intelligent multi-sensor fusion;
- Data aggregation and fusion in smart city;
- IoT environments using sensor data aggregation;
- Industrial IoT (IIoT) using sensor data aggregation and fusion;
- Preparation and filter techniques for data fusion and aggregation;
- Information fusion techniques and applications;
- Data analysis for multi-sensor fusion;
- IoT/sensor energy-efficient data aggregation and fusion;
- Multi-sensor-based planning and decision-making;
- Applications of multi-sensor fusion and aggregation;
- Data fusion of distributed sensors in the IoT environments.
Dr. Ihsan Ullah
Dr. Muhammad Sajjad Khan
Dr. Hyun-Kyo Lim
Dr. Gan Huang
Guest Editors
Manuscript Submission Information
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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. AI is an international peer-reviewed open access quarterly 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 1600 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
- multi-source data fusion
- data aggregation
- data clustering and classification
- smart IoT environment
- evidence theory
- machine learning
- decision-making system
- artificial intelligence
- data science
