62,159 research outputs found
Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)
Big data has been emerging as a new approach in utilizing large datasets to
optimize complex system operations. Big data is fueled with Internet-of-Things
(IoT) services that generate immense sensory data from numerous sensors and
devices. While most current research focus of big data is on machine learning
and resource management design, the economic modeling and analysis have been
largely overlooked. This paper thus investigates the big data market model and
optimal pricing scheme. We first study the utility of data from the data
science perspective, i.e., using the machine learning methods. We then
introduce the market model and develop an optimal pricing scheme afterward. The
case study shows clearly the suitability of the proposed data utility
functions. The numerical examples demonstrate that big data and IoT service
provider can achieve the maximum profit through the proposed market model
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
A REVIEW ON INTERNET OF THINGS ARCHITECTURE FOR BIG DATA PROCESSING
The importance of big data implementations is increased due to large amount of gathered data via the online gates. The businesses and organizations would benefit from the big data analysis i.e. analyze the political, market, and social interests of the people. The Internet of Things (IoT) presents many facilities that support the big data transfer between various Internet objects. The integration between the big data and IoT offer a lot of implementations in the daily life like GPS, Satellites, and airplanes tracking. There are many challenges face the integration between big data transfer and IoT technology. The main challenges are the transfer architecture, transfer protocols, and the transfer security. The main aim of this paper is to review the useful architecture of IoT for the purpose of big data processing with the consideration of the various requirements such as the transfer protocol. This paper also reviews other important issues such as the security requirements and the multiple IoT applications. In addition, the future directions of the IoT-Big data are explained in this paper
Deep learning and big data technologies for IoT security
Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects
Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics
In contemporary age, Computational Intelligence (CI) performs an essential
role in the interpretation of big biological data considering that it could
provide all of the molecular biology and DNA sequencing computations. For this
purpose, many researchers have attempted to implement different tools in this
field and have competed aggressively. Hence, determining the best of them among
the enormous number of available tools is not an easy task, selecting the one
which accomplishes big data in the concise time and with no error can
significantly improve the scientist's contribution in the bioinformatics field.
This study uses different analysis and methods such as Fuzzy, Dempster-Shafer,
Murphy and Entropy Shannon to provide the most significant and reliable
evaluation of IoT-based computational intelligence tools for DNA sequence
analysis. The outcomes of this study can be advantageous to the bioinformatics
community, researchers and experts in big biological data
IoT big data value map : how to generate value from IoT data
Huge sources of business value are emerging due to big data generated by the Internet of Things (IoT) technologies paired with Machine Learning (ML) and Data Mining (DM) techniques' ability to harness and extract hidden knowledge from data and consequently learning and improving spontaneously. This paper reviews different examples of analyzing big data generated through IoT in previous studies and in various domains; then claims their business Value Proposition Map deploying Value Proposition Canvas as a novel conceptual tool. As a result, the proposed unprecedented framework of this paper entitled "IoT Big Data Value Map" shows a roadmap from raw data to real-world business value creation, blossomed out of a kind of three-pillar structure: IoT, Data Mining, and Value Proposition Map. The result of this study paves the way for prototyping business models in this field based on value invention from huge data analysis generated by IoT devices in different industries. Furthermore, researchers may complete this map by associating proposed framework with potential customers' profile and their expectations
Big data assisted CRAN enabled 5G SON architecture
The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making. In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem
Real-Time Big Data Analytics in Smart Cities from LoRa-Based IoT Networks
The currently burst of the Internet of Things (IoT) tech-nologies
implies the emergence of new lines of investigation regarding not only to hardware
and protocols but also to new methods of pro-duced data analysis satisfying the
IoT environment constraints: a real-time and a big data approach. The Real-time
restriction is about the continuous generation of data provided by the endpoints
connected to an IoT network; due to the connection and scaling capabilities of an IoT
network, the amount of data to process is so high that Big data tech-niques
become essential. In this article, we present a system consisting of two main
modules. In one hand, the infrastructure, a complete LoRa based network designed,
tested and deployment in the Pablo de Olavide University and, on the other side, the
analytics, a big data streaming sys-tem that processes the inputs produced by the
network to obtain useful, valid and hidden information.Ministerio de Economía y Competitividad TIN2017-88209-C2-1-
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