Internet of Things and Ambient Intelligence

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 6794

Special Issue Editor


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Guest Editor
Department of Computer Science and Information Engineering, Tamkang University, Taipei 251, Taiwan
Interests: internet of things; home care technologies; wireless sensor networks and big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) connects the physical world and digital world and is a convergence of multiple technologies, such as sensors, embedded systems, wireless communications, real-time analytics, as well as machine learning. In IoT environments, intelligent decision-making leads to Ambient Intelligence, which supports smart, sensitive, interactive, adaptive, autonomous, and personalized services to human needs. The internet of Things (IoT) has been a key technology that provides the basic infrastructure for Ambient Intelligence, while Ambient Intelligence enhances the use and capabilities of IoT devices.

The aim of this Special Issue is to bring together cutting-edge research, current trends, and developments of innovative topologies and algorithms for Ambient Intelligence, along with applications relevant to the IoT. Topics of interest may include (but are not limited to):

  • Applications in health care: assisted living, fall detection, elderly care, patient monitoring, patient rehabilitation;
  • Applications of combined pervasive/ubiquitous computing with AI;
  • Wireless sensor technologies for IoTs;
  • Data analysis and machine learning techniques on data collected from IoT devices;
  • Innovative sensing devices and innovative uses of existing sensors;
  • Use of mobile, wireless, visual, and multimodal sensor networks in intelligent systems;
  • Virtual smart environments, interfaces with real world, social networks as smart environments;
  • Sensor data fusion and collaboration in multisensor systems and networks;
  • Mobile/wearable intelligence;
  • Behavior modeling;
  • Intention recognition;
  • Robotics applied to smart environments;
  • Distributed and collaborative computing and reasoning;
  • Cognitive and emotional awareness.

Prof. Dr. Chih-Yung Chang
Guest Editor

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Published Papers (1 paper)

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18 pages, 2789 KiB  
Article
Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI
by Yafei Zhao, Paolo Vincenzo Genovese and Zhixing Li
Future Internet 2020, 12(2), 30; https://doi.org/10.3390/fi12020030 - 10 Feb 2020
Cited by 34 | Viewed by 6370
Abstract
With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building [...] Read more.
With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort. Full article
(This article belongs to the Special Issue Internet of Things and Ambient Intelligence)
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