scholarly journals A NETWORK OF INTELLIGENT PROXIMITY IOT DEVICES FOR OBJECT LOCALIZATION, INFORMATION COMMUNICATION, AND DATA ANALYTICS BASED ON CROWDSOURCING

2018 ◽  
Author(s):  
Mike Qu ◽  
Yu Sun
Keyword(s):  
Data Analytics ◽  
Object Localization ◽  
Information Communication ◽  
Iot Devices ◽  
Localization Information

Design of Software and Data Analytics for Self-Powered Wireless IoT Devices

2018 ◽  
Author(s):  
Linga Reddy Cenkeramaddi ◽  
Ashish Goyal ◽  
Asheesh Bhuria ◽  
Srinivas M.B. ◽  
Soumya J
Keyword(s):  
Data Analytics ◽  
Self Powered ◽  
Iot Devices

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Emulator for data analytics and IoT development projects

2020 ◽  
Vol 14 (1) ◽  
pp. 57-63
Author(s):  
Andrés Armando Sánchez Martin ◽  
Luis Eduardo Barreto Santamaría ◽  
Juan José Ochoa Ortiz ◽  
Sebastián Enrique Villanueva Navarro
Keyword(s):  
Data Analysis ◽  
Software Design ◽  
Design Patterns ◽  
Data Analytics ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Academic Environments ◽  
Software Design Patterns ◽  
Iot Devices

One of the difficulties for the development and testing of data analysis applications used by IoT devices is the economic and temporary cost of building the IoT network, to mitigate these costs and expedite the development of IoT and analytical applications, it is proposed NIOTE, an IoT network emulator that generates sensor and actuator data from different devices that are easy to configure and deploy over TCP/IP and MQTT protocols, this tool serves as support in academic environments and conceptual validation in the design of IoT networks. The emulator facilitates the development of this type of application, optimizing the development time and improving the final quality of the product. Object-oriented programming concepts, architecture, and software design patterns are used to develop this emulator, which allows us to emulate the behavior of IoT devices that are inside a specific network, where you can add the number of necessary devices, model and design any network. Each network sends data that is stored locally to emulate the process of transporting the data to a platform, through a specific format and will be sent to perform Data Analysis.

scholarly journals Cyber-Storms Come from Clouds: Security of Cloud Computing in the IoT Era

2019 ◽  
Vol 11 (6) ◽  
pp. 127 ◽  
Author(s):  
Michele De Donno ◽  
Alberto Giaretta ◽  
Nicola Dragoni ◽  
Antonio Bucchiarone ◽  
Manuel Mazzara
Keyword(s):  
Cloud Computing ◽  
Data Analytics ◽  
The Internet ◽  
Utility Computing ◽  
Security Issues ◽  
Potential Impact ◽  
Iot Devices ◽  
Computational Resources ◽  
The Internet Of Things ◽  
Security Of Iot

The Internet of Things (IoT) is rapidly changing our society to a world where every “thing” is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity and data collection relies more and more on the Cloud, where data analytics and intelligence actually reside. Cloud computing has indeed revolutionized the way computational resources and services can be used and accessed, implementing the concept of utility computing whose advantages are undeniable for every business. However, despite the benefits in terms of flexibility, economic savings, and support of new services, its widespread adoption is hindered by the security issues arising with its usage. From a security perspective, the technological revolution introduced by IoT and Cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. While the literature mostly focuses on the security of IoT and Cloud computing as separate entities, in this article we provide an up-to-date and well-structured survey of the security issues of cloud computing in the IoT era. We give a clear picture of where security issues occur and what their potential impact is. As a result, we claim that it is not enough to secure IoT devices, as cyber-storms come from Clouds.

scholarly journals Fog Computing: An Overview of Big IoT Data Analytics

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Rizwan Anawar ◽  
Shangguang Wang ◽  
Muhammad Azam Zia ◽  
Ahmer Khan Jadoon ◽  
Umair Akram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Key Characteristics ◽  
Challenges And Opportunities ◽  
Cloud Framework ◽  
Iot Devices ◽  
Health Care Monitoring ◽  
Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

scholarly journals Big Data and Personalisation for Non-Intrusive Smart Home Automation

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Suriya Priya R. Asaithambi ◽  
Sitalakshmi Venkatraman ◽  
Ramanathan Venkatraman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Smart Devices ◽  
Home Automation ◽  
Open Standard ◽  
Iot Devices ◽  
And Control

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

scholarly journals Composing High-level Stream Processing Pipelines

2020 ◽  
Author(s):  
Tanmaya Mahapatra
Keyword(s):  
Data Analytics ◽  
State Of The Art ◽  
Stream Processing ◽  
Detailed Comparison ◽  
Application Development ◽  
Actor Model ◽  
Data Transformations ◽  
Simulation Scenario ◽  
Iot Devices ◽  
High Level

Abstract The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.

Data Analytics Pipeline for Prediction and Decision Making in Complex Products and Systems Development

2015 ◽  
Author(s):  
Eliab Z. Opiyo
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
System Development ◽  
Systems Development ◽  
Engineering Product ◽  
Raw Data ◽  
Complex Products ◽  
Specific Objective ◽  
Iot Devices ◽  
Support Decision Making

Facilitating data analytics for effective prediction in complex products or systems development is the focus of the research described in this paper. The specific objective was to develop strategies and a data analytics pipeline with a view to supporting exploration of the design space of complex products or systems upfront. The underlying challenges tackled included how to acquire and store raw data gathered by using both the traditional methods and advanced Internet of Things (IoT) devices, how to preprocess and transform raw data into a form suited for data analytics, and how to deal with analytics. A pipeline for data analytics to support decision making in complex products or systems development is proposed and its applicability illustrated with a practical example. The incorporation of advanced analytics techniques into the proposed pipeline allows users to acquire data and to insightfully and intelligently predict aspects such as cost and assembly time early on, and to make decisions based on data that may otherwise deemed to be inaccessible or unusable. This work contributes to the efforts directed toward applying data analytics techniques in a way that can have a profound impact on an engineering product or system development process.

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