Special Issue "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: 31 March 2018
Prof. Dr. Giancarlo Fortino
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
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. Big Data and Cognitive Computing 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) is waived for well-prepared manuscripts submitted to this issue. 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.
- 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
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Cognitive Symbiosis: Cognitive Computing for Humans
Author: Csaba Veres
Affiliation: University of Bergen; email@example.com
Abstract: Cognitive Computing has become a catchphrase in the technology world, with the promise of new intelligent services offered by industry giants like IBM and Google. Recent technological advances in Artificial Intelligence (AI) have thrown into the public sphere some old questions about the relationship between machine computation and human intelligence. While much of the industry and media hype suggests that many traditional challenges have been overcome, we show an example from language processing which demonstrates that present day Cognitive Computing still struggles with fundamental, long-standing problems with AI. An alternative conceptualization of artificial intelligence is presented, following Licklider’s lead in adopting human-machine symbiosis as a metaphor for designing software systems that enhance human cognitive performance. A survey of existing work based on this view suggests that a distinction can be made between weak and strong versions of symbiosis. We propose a strong Cognitive Symbiosis which dictates an interdependence between human and machine, with semantics as the interface of the interaction.
Title: Real-Time Information Derivation from Big Sensor Data via Edge Computing
Author: Kyoung-Don Kang
Affiliation: State University of New York at Binghamton; firstname.lastname@example.org
Abstract: In data-intensive real-time applications, e.g., mobile health (mHealth), transportation management and location-based services, 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 real-time sensor data analytics in an edge server by extending the map-reduce model originated in functional programming, while providing timely 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.
Title: A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real World measurements
Author: Hadi Larijani
Affiliation: Computer Networks and Intelligent Systems, Dept. Computer, Communications and Interactive Systems (CCIS), School of Engineering & Built Environment, Glasgow Caledonian University,
Cowcaddens Road, Glasgow, G4 0BA, UK
Abstract: Furthermore, to facilitate the 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 the multi-wall model. Subsequently, real-world measurements were collected and compared against different propagation models. For this benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: a) propagation of LoRaWAN is limited to a much shorter range in this investigated environment compared to outdoor reports. b) Log-distance and COST231 do not yield an accurate estimate of propagation characteristics correctly for outdoor-indoor scenarios. c) This lack of accuracy can be addressed by adjusting the COST231, to account for the outdoor propagation. d) A feedforward neural network combined with COST231 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 in practice facilitate network planning for outdoor-indoor environments.