Big Data and Cognitive Computing

1667 KiB

Open AccessArticle

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 27 | Viewed by 6867

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

Open AccessArticle

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 5541

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

Open AccessArticle

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 12 | Viewed by 4790

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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1265 KiB

Open AccessReview

Smart Cities, Big Data, and Sustainability Union

by Sonali Kudva and Xinyue Ye

Big Data Cogn. Comput. 2017, 1(1), 4; https://doi.org/10.3390/bdcc1010004 - 29 Sep 2017

Cited by 25 | Viewed by 7077

Abstract

Media convergence has changed relationships between existing technologies, industries, markets, and audiences. Smart cities are seen as the logical outcome of media convergence. Big Data form the basis and the output of smart technologies. In the last twenty years, there has been much [...] Read more.

Media convergence has changed relationships between existing technologies, industries, markets, and audiences. Smart cities are seen as the logical outcome of media convergence. Big Data form the basis and the output of smart technologies. In the last twenty years, there has been much discussion on smart cities, big data, and the need for sustainability in technological endeavors. This article combines these by providing an overview of the three subjects about their points of intersection. Identifying these points of intersection will help smart city researchers to better understand where there is need for further development towards better standards of living and increased sustainability. This review will provide directions for further research and provide a brief historical overview of how far research has come in the three intertwined identified areas towards designing, adapting, and managing smarter communities. Full article

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3598 KiB

Open AccessArticle

Function Modeling Improves the Efficiency of Spatial Modeling Using Big Data from Remote Sensing

by John Hogland and Nathaniel Anderson

Big Data Cogn. Comput. 2017, 1(1), 3; https://doi.org/10.3390/bdcc1010003 - 13 Jul 2017

Cited by 21 | Viewed by 5074

Abstract

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using [...] Read more.

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this paper, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. Furthermore, we introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called function modeling. Results from simulations show a 64.3% reduction in processing time and an 84.4% reduction in storage space attributable to function modeling. In an applied case study, this translated to a reduction in processing time from 2247 h to 488 h and a reduction is storage space from 152 terabytes to 913 gigabytes. Full article

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1261 KiB

Open AccessArticle

A 5G Cognitive System for Healthcare

by Min Chen, Jun Yang, Yixue Hao, Shiwen Mao and Kai Hwang

Big Data Cogn. Comput. 2017, 1(1), 2; https://doi.org/10.3390/bdcc1010002 - 30 Mar 2017

Cited by 147 | Viewed by 15420

Abstract

Developments and new advances in medical technology and the improvement of people’s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this [...] Read more.

Developments and new advances in medical technology and the improvement of people’s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients’ physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients’ emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient’s health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems. Full article

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142 KiB

Open AccessEditorial

Welcome to the New Interdisciplinary Journal Combining Big Data and Cognitive Computing

by Min Chen

Big Data Cogn. Comput. 2017, 1(1), 1; https://doi.org/10.3390/bdcc1010001 - 8 Dec 2016

Cited by 12 | Viewed by 4193

Abstract

Welcome to Big Data and Cognitive Computing (BDCC). Full article

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Big Data Cogn. Comput., Volume 1, Issue 1 (December 2017) – 7 articles

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