Scope and Aim:
Recent advancements in intelligent embedded systems are paving the way for modern large-scale data systems through a wide variety of protocols, architectures, services and configurations. Technologies such as smart sensing, RFID tagging, embedded internet, edge computing, and predictive data mining all work to permeate intelligence and decision making into the physical world with the ultimate aim of continually enhancing human experience in real-time. “Big data” is the recent buzzword in which analytics provides real-time insights, which need to be actioned upon quickly to support decisions, gain better value, and mitigate risk. Concurrently, artificial intelligence (AI), and its dominant form – machine learning, has been intensively applied to deal with large-scale heterogeneous data to help innovate and transform businesses. The convergence of these two technology paths is highly promising. Data is considered the “blood” of artificial intelligence, whereas AI systems learn from the data in order to accomplish their function.
The aim of this special issue is to discuss how large-scale data systems and AI can be leveraged to enhance the learning, reasoning, and decision-making in embedded systems, in real-time. Data governance, data integration, data storage, data quality and data security are some criticalities associated with this problem, while conventional embedded system architectures and protocols are used to prepare data are inadequate. Unlike traditional data sets that are commonly associated with embedded systems, big data tends to be unstructured, multi-modal, and in the case of human-centric text, perhaps multi-lingual. The incompleteness, fuzziness and uncertainty make it even more intricate to tap and analyse information using contemporary tools. We invite researchers to discuss intelligent embedded system protocols and architectures. Novel approaches to information discovery and decision making which use multiple intelligent technologies such as machine learning, deep learning, artificial intelligence, natural language processing and image recognition among others are required to understand data & then generate insights. We also welcome implementation papers on analyzing and processing of big data and practical data-driven decision making by discovering and understanding knowledge from the data.
The topics of interest include, but are not limited to:
Smart embedded system designs for cloud-based big data.
AI-empowered modelling of embedded systems for big data.
Intelligent embedded system protocols for high performance/parallel computing platforms.
Embedded-aware protocols for data quality and integrity.
Privacy preservation, trust and security in intelligent embedded systems.
Intelligent embedded computer vision and natural language processing systems using big data.
Intelligent sensing for smart cities.
Embedded systems for large-scale smart healthcare, avionics, transportation, and automotive.
Experimental test-beds of intelligent embedded system frameworks for big data.
All original manuscripts that fit within the scope are welcome.
General information for submitting papers to JSA can be found at https://www.journals.elsevier.com/journal-of-systems-architecture. Submissions should be made online at https://www.editorialmanager.com/jsa/. Please select the “VSI:ADES-BD” option as the type of the paper during the submission process. Please direct all enquiries regarding this SI to Deepak Kumar Jain.
JSA has adopted the Virtual Special Issue model to speed up the publication process, where Special Issue papers are published in regular issues, but marked as SI papers. Acceptance decisions are made on a rolling basis. Therefore, authors are encouraged to submit papers early, and need not wait until the submission deadline.
Paper submission due: December 30, 2020
Final decision: July 20, 2021
Joel J. P. C. Rodrigues
Federal University of Piauí (UFPI), Brazil;
Instituto de Telecomunicações, Portugal
University of Milan,Italy
Department of Computer Science
University of York, United Kingdom
Email Id: email@example.com
Jerry Chun-Wei Lin
Western Norway University of Applied Sciences, Norway
Deepak Kumar Jain
Institute of Automation
Chongqing University of Posts and Telecommunications, China