The Internet of Things (IoT) becomes as one of the key technological developments that provide smart infrastructure for the cloud-edge services by interconnecting physical devices and virtual things between mobile applications and embedded devices. Several embedded software and hardware systems had been developed by developers to assist IoT systems in gathering information about safety-critical fields. By increasing development of IoT ecosystems, Intelligent Decision Making (IDM) paradigm is emerging as a high potential solution for processing and navigating the information of embedded IoT devices. IDM for embedded devices in IoT systems investigates the massive quantity of complex data to help industry, transportations, medical systems, microcontrollers and other smart applications. IoT, embedded devices, sensors, microprocessors, manual data entry and online sources are a few complex data sources for IDM. IDM make use of machine learning techniques to enhance the process of complex making decisions and prediction. AI tools such as Metaheuristic, Fuzzy Logic, artificial neural networks, deep learning and intelligent agents can be integrated to IDM for embedded systems in IoT. Finally, IDM-based embedded devices can be very beneficial to a range of IoT systems where complex and critical decisions are made under time pressure, decision-makers are on the move, and the environment is dynamic and uncertain.
Despite the importance of decision making methods on embedded software and hardware systems in IoT environments, this special issue invites researchers to publish selected original papers presenting intelligent trends to solve new challenges of IDM methods. We also are interested in review articles as the state-of-the-art of this topic, showing recent major advances and discoveries, significant gaps in the research and new future issues.
Topics are as below but are not limited to:
• Memetic-based IDM for embedded applications in IoT
• Machine learning methods for IDM in embedded IoT systems
• IDM for the embedded computer-aided diagnostic system.
• IDM for industrial embedded IoT applications.
• Embedded medical instrumentation and healthcare technologies in IoT
• Decision making for Wireless Body Area Network (WBAN) in wearable IoT systems
• Formal analysis of IDM-based embedded devices in IoT
• Energy prediction on embedded sensor-based IDSS systems in IoT
• Security and privacy aspects of embedded systems based on IDM in IoT
• Blockchain technology on IDM-based embedded IoT systems
• Big data management based on IDM in IoT systems
• IDM on vehicular communications in IoT systems
• IDM for robotics and micro-robotic embedded systems in IoT
• Smart city and smart home based on IDM in embedded IoT
• Decision-making enabled embedded smart farming and agriculture in IoT
• Decision-making on multi-processor systems on a chip in IoT applications
Deadline for submissions: 20 August 2021
Final Decision: 20 January 2022
Tentative Publication Date: Q3, 2022
Dr. Alireza Souri (Leading Guest Editor), Department of Computer Engineering, Islamic Azad University, Iran, [email protected]
Prof. Mu-Yen Chen, Department of Engineering Science, National Cheng Kung University, Taiwan, [email protected]
Dr. Pradip Kumar Sharma, Department of Computing Science, University of Aberdeen, UK, [email protected]
Dr. Masoud Daneshtalab, Intelligent Future Technology, University of Mälardalen, Sweden, [email protected]
Prof. Amir Masoud Rahmani, Department of computer science, Khazar University, Baku, Azerbaijan, [email protected]