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Cognitive Services Integrating with Big Data, Clouds and IoT
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Cognitive Services Integrating with Big Data, Clouds and IoT

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 March 2018) | Viewed by 16873

Special Issue Editors

Prof. Dr. Victor C.M. Leung

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Guest Editor
Department of Electrical & Computer Engineering, University of British Columbia, Vancouver, BC V6T1Z4, Canada
Interests: blockchain systems; telecommunication networks information systems personal communications networking cloud and edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giancarlo Fortino

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Guest Editor
Computer Engineering, DIMES-Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy
Interests: body area networks; Internet of Things; agent-based computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yin Zhang

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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: mobile computing; edge intelligence; cognitive wireless communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, the computer and information industry has experienced rapid changes, in both the platform scale and the scope of applications. Computers, smart phones, clouds, and social networks demand, not only high performance, but also a high degree of machine intelligence. In fact, we are entering an era of big data analysis and cognitive computing. This trendy movement is observed by the pervasive use of mobile phones, storage and computing clouds, the revival of artificial intelligence in practice, extended supercomputer applications, and wide spread deployment of Internet of Things (IoT) platforms. To face these new computing and communication paradigms, we must upgrade the cloud and IoT ecosystems with new capabilities, such as machine learning, IoT sensing, and data analytics that can provide humans with cognitive services.

In the big data era, successful cloud systems, web services, and datacenters must be designed to store, process, learn, and analyze big data to discover new knowledge or make critical decisions. The purpose is to build up a big data industry to provide cognitive services to offset human shortcomings in handling labor-intensive tasks with high efficiency. These goals are achieved through hardware virtualization, machine learning, deep learning, IoT sensing, data analytics, and cognitive computing. For example, new cloud services have appeared, such as Learning as a Services (LaaS), Analytics as a Service (AaaS), or Security as a Service (SaaS), along with the growing practices of machine learning and data analytics.

Prof. Dr. Victor C.M. Leung
Dr. Yin Zhang
Dr. Giancarlo Fortino
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 submissions that pass pre-check are 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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • Novel architecture and infrastructure for cognitive services
  • Multi-modal fusion for cognitive services
  • Contextual data management and mining platforms
  • Cognitive computing, affective computing, machine learning
  • Intelligent, cognitive and interactive interface
  • Privacy protected discovery and adaptation in cognitive services

Published Papers (3 papers)

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1667 KiB  
Article
A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
by Salaheddin Hosseinzadeh, Mahmood Almoathen, Hadi Larijani and Krystyna Curtis
Big Data Cogn. Comput. 2017, 1(1), 7; https://doi.org/10.3390/bdcc1010007 - 14 Dec 2017
Cited by 24 | Viewed by 6391
Abstract
Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for [...] Read more.
Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in similar scenarios. This could facilitate network planning for outdoor–indoor environments. Full article
(This article belongs to the Special Issue Cognitive Services Integrating with Big Data, Clouds and IoT)
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785 KiB  
Article
Strong Cognitive Symbiosis: Cognitive Computing for Humans
by Csaba Veres
Big Data Cogn. Comput. 2017, 1(1), 6; https://doi.org/10.3390/bdcc1010006 - 10 Nov 2017
Cited by 5 | Viewed by 5063
Abstract
“Cognitive Computing” has become somewhat of a rallying call in the technology world, with the promise of new smart services offered by industry giants like IBM and Microsoft. The associated technological advances in Artificial Intelligence (AI) have thrown into the public sphere some [...] Read more.
“Cognitive Computing” has become somewhat of a rallying call in the technology world, with the promise of new smart services offered by industry giants like IBM and Microsoft. The associated technological advances in Artificial Intelligence (AI) have thrown into the public sphere some old questions about the relationship between machine computation and human intelligence. Much of the industry and media hype suggests that many traditional challenges have been overcome. On the contrary, our simple examples from language processing demonstrate that present day cognitive computing still struggles with fundamental, long-standing problems in AI. An alternative interpretation of cognitive computing is presented, following Licklider’s lead in adopting “man-computer symbiosis” as a metaphor for designing software systems that enhance human cognitive performance. A survey of existing proposals on this view suggests a distinction between weak and strong versions of symbiosis. We propose a Strong Cognitive Symbiosis, which dictates an interdependence rather than simply cooperation between human and machine functioning, and introduce new software systems, which were designed for cognitive symbiosis. We conclude that strong symbiosis presents a viable new perspective for the design of cognitive computing systems. Full article
(This article belongs to the Special Issue Cognitive Services Integrating with Big Data, Clouds and IoT)
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1369 KiB  
Article
Real-Time Information Derivation from Big Sensor Data via Edge Computing
by Kyoung-Don Kang, Liehuo Chen, Hyungdae Yi, Bin Wang and Mo Sha
Big Data Cogn. Comput. 2017, 1(1), 5; https://doi.org/10.3390/bdcc1010005 - 17 Oct 2017
Cited by 11 | Viewed by 4408
Abstract
In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading [...] Read more.
In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion. Full article
(This article belongs to the Special Issue Cognitive Services Integrating with Big Data, Clouds and IoT)
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