Special Issue "Future Computing for Real Time Intelligent Systems"

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 31 December 2017

Special Issue Editors

Guest Editor
Dr. Simon James Fong

Department of Computer and Information Science, Data Analytics and Collaborative Computing Laboratory, University of Macau, Taipa, Macau SAR
Website1 | Website2 | E-Mail
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Guest Editor
Prof. Dr. Sabah Mohammed

Department of Computer Science, Lakehead University, Ontario, Canada
Website | E-Mail
Interests: Machine Learning; Web Intelligence; Big Data; IIoT; Health Informatics
Guest Editor
Prof. Dr. Jinan Fiaidhi

Department of Computer Science, Lakehead University, Ontario, Canada
Website | E-Mail
Phone: 807 3438224
Interests: Collaborative Learning; Web Mining; Calm Computing; Cloud Computing; IoT

Special Issue Information

Dear Colleagues,

Real-Time Computing covers a broad spectrum of the intensively developing area of low-latency priority-driven system responsiveness under certain time constrains, as well as essential and decisive human-computer and/or machine-to-machine interactions constantly using incoming data streams. Research on real-time intelligent systems is of a multi-disciplinary nature, exploiting concepts from areas as diverse as signal processing technologies, computational intelligence, location systems, data processing, digital document processing, image recognition, and embedded system design. To accomplish the goals of its real-time performance, systematic analysis is carried out when systems are working.

Over the past few decades, real-time intelligent computing has radically transformed human lifestyles. In today's competitive and highly dynamic environment, analyzing data in real time is a must to understand, in detail, the insights from underlying behavioral patterns, as well as how systems are processing data, so to reason outputs and anticipate trends in intelligent computing, have become critical.

To leverage the full potential of this opportunity, to build complex real-time systems, intense research is required and this conference will serve as one such platform for ongoing research in real time intelligent systems.

The objective of this Special Issue is to compile and publish novel ideas relating to the areas of real-time systems, especially on new applications, data stream mining, and related big data analytic technology. We would like to solicit contributions from researchers from different disciplines, industrial practitioners, government agencies, and academia to discuss new ideas, research questions, recent results, and future challenges in real time intelligent systems. The best papers from the conference are solicited, which are theoretically grounded, methodologically sound, from academia and industry, which address a variety of aspects and innovations related to real-time computing systems.

Assoc. Prof. Simon James Fong
Prof. Sabah Mohammed
Prof. Jinan Fiaidhi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Future Internet 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 550 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

  • Streaming data, streaming engines
  • Big data systems and applications for high-velocity data
  • Analysis in advanced domains such as energy, sensors, etc.
  • Artificial intelligence
  • Broadband intelligence
  • Cloud computing and intelligence
  • Collaborative intelligence
  • Crowdsourcing and crowd intelligence
  • Data capture in real-time
  • Intelligent database systems
  • Data mining
  • Intelligent data analysis
  • OLAP for real-time decision support
  • Data quality and cleansing
  • Intelligent fuzzy systems
  • Event-driven analytics
  • Visualizing real-time data and information
  • Intelligent soft computing
  • Privacy and security in intelligence
  • Architectures for intelligence
  • Internet of things
  • Intelligent robotic systems
  • Smart services and platforms
  • Intelligent transportation systems
  • Mobile smart systems
  • Trace-based intelligent real-time services (eye-tracking, image tracking)
  • Real-time intelligent alert systems
  • Machine translation in real time
  • Multilingual information access
  • Multiagent intelligent systems
  • Intelligent information systems
  • Adaptive vision algorithms
  • Real-time intelligent network solutions
  • Real-time distributed coding
  • Real-time modelling user’s information needs
  • Real-time noise removal systems
  • Real-time intelligent communication
  • Real-time remote access systems
  • Decision support systems in real time
  • Real-time multiprocessor systems

Published Papers (3 papers)

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Research

Open AccessArticle Efficient Traffic Engineering Strategies for Improving the Performance of TCP Friendly Rate Control Protocol
Future Internet 2017, 9(4), 74; doi:10.3390/fi9040074
Received: 31 August 2017 / Revised: 30 September 2017 / Accepted: 7 October 2017 / Published: 1 November 2017
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Abstract
Multimedia services will play a prominent role in the next generation of internet. With increasing real time requirements, internet technology has to provide Quality of Service (QoS) for various kinds of real time streaming services. When the bandwidth required exceeds the available network
[...] Read more.
Multimedia services will play a prominent role in the next generation of internet. With increasing real time requirements, internet technology has to provide Quality of Service (QoS) for various kinds of real time streaming services. When the bandwidth required exceeds the available network resources, network paths can get congested, which results in a delay in packet delivery and packet loss. This situation leads to the design of new strategies for congestion avoidance and control. One of the popular and appropriate congestion control mechanisms that is useful in transmitting multimedia applications in the transport layer is TCP Friendly Rate Control Protocol (TFRC). However, TFRC still suffers from packet loss and delay due to long distance heavy traffic and network fluctuations. This paper introduces a number of key concerns like enhanced Round Trip Time (RTT) and Retransmission Time Out (RTO) calculations, Enhanced Average Loss Interval (ALI) methods and improved Time to Live (TTL) features are applied to TFRC to enhance the performance of TFRC over wired networks. Full article
(This article belongs to the Special Issue Future Computing for Real Time Intelligent Systems)
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Open AccessArticle Signal Consensus in TSP of the Same Grid in Road Network
Future Internet 2017, 9(4), 69; doi:10.3390/fi9040069
Received: 4 September 2017 / Revised: 12 October 2017 / Accepted: 18 October 2017 / Published: 24 October 2017
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Abstract
In this paper, we propose a consensus algorithm with input constraints for traffic light signals in transit signal priority (TSP). TSP ensures control strategy of traffic light signals can be adjusted and applied according to the real-time traffic status, and provides priority for
[...] Read more.
In this paper, we propose a consensus algorithm with input constraints for traffic light signals in transit signal priority (TSP). TSP ensures control strategy of traffic light signals can be adjusted and applied according to the real-time traffic status, and provides priority for buses. We give the convergence conditions of the consensus algorithms with and without input constraints in TSP respectively and analyze the convergence performance of them by using matrix theory and graph theory, and PTV-VISSIM is used to simulate the traffic accident probability of three cases at intersections. Simulation results are presented that a consensus is asymptotically reached for all weights of priority; the algorithm with input constraints is more suitable for TSP than the algorithm without input constraints, and the traffic accident rate is reduced. Full article
(This article belongs to the Special Issue Future Computing for Real Time Intelligent Systems)
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Open AccessArticle A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
Future Internet 2017, 9(3), 37; doi:10.3390/fi9030037
Received: 14 June 2017 / Revised: 9 July 2017 / Accepted: 13 July 2017 / Published: 18 July 2017
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Abstract
Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy
[...] Read more.
Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy algorithm. Currently, the hybrid-copy algorithm only copies all memory pages once in advance. In a write-intensive workload, copy memory pages once may be enough. However, more iterative copy rounds can significantly reduce the page faults in a read-intensive workload. In this paper, we propose a new parameter to decide the appropriate time to stop the iterative copy phase based on real-time situation. We use a Markov model to forecast the memory access pattern. Based on the predicted results and the analysis of the actual situation, the memory page transfer order would be adjusted to reduce the invalid transfers. The novel hybrid-copy algorithm is implemented on the Xen platform. The experimental results demonstrate that our mechanism has good performance both on read-intensive workloads and write-intensive workloads. Full article
(This article belongs to the Special Issue Future Computing for Real Time Intelligent Systems)
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