Challenges and Solutions of Big Data and IoT

2019 ◽  
pp. 264-272
Author(s):  
Jayashree K. ◽  
Abirami R. ◽  
Rajeswari P.
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Data Processing ◽  
The Internet ◽  
Huge Amount ◽  
Collection Data ◽  
Different Sources ◽  
The Internet Of Things

The successful development of big data and the internet of things (IoT) is increasing and influencing all areas of technologies and businesses. The rapid increase of more devices that are connected to IoT from which enormous amount of data are consumed indicates the way how big data is related with IoT. Since huge amount of data are obtained from different sources, analysis of these data involves much of processing at each and every level to extract knowledge for decision making process. To manage big data in a continuous network that keeps expanding leads to few issues related to data collection, data processing, analytics, and security. To address these issues, certain solution using bigdata approach in IoT are examined. Combining these two areas provides several opportunities developing new systems and identify advanced techniques to solve challenges on big data and IoT.

Cloud Provisioning and Modeling of Internet of Things (IoT) Systems for Evaluating Big Data Uncertainties

2020 ◽  
pp. 125-130
Author(s):  
Yuji Huang ◽  
Aravindhan K
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Actual World ◽  
The Internet ◽  
Data Systems ◽  
Internet Service ◽  
Research Gap ◽  
Big Data Systems ◽  
Different Sources ◽  
The Internet Of Things

The present cyber-physical schemes and the Internet of Things (IoT) schemes comprise of both complex and simple interactions defining the different sources of the IoT systems such as cloud information and the edge internet service centres. All the modeling frameworks have been established on the virtualization dimensions that include both the cloud and the edge structures. Apart from that, the systems deal with big data based on the connections of various forms of services and networks. In that case, various forms of data uncertainties are evident. These uncertainties include elasticity and actuation uncertainties. As a result, this leads to a number of challenges that affect the process of testing these uncertainties in the big data systems. Nonetheless, there is a research gap present to effectively model and design the precise infrastructure frameworks that handle the necessities for evaluating these emergent big data uncertainties. With that regard, this scholastic paper focusses on the techniques used to generate and determine the deployment configurations used in the process of evaluating both the cloud and IoT systems. In this research, the survey will consider the actual-world application for analysing and monitoring the transceiver frameworks.

The Rise of Big Data, Cloud, and Internet of Things

2018 ◽  
pp. 201-222 ◽  
Author(s):  
Reema Abdulraziq ◽  
Muneer Bani Yassein ◽  
Shadi Aljawarneh
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
End Users ◽  
Structured Data ◽  
The Internet ◽  
Huge Amount ◽  
Volume Velocity ◽  
The Cost ◽  
The Internet Of Things

Big data refers to the huge amount of data that is being used in commercial, industrial and economic environments. There are three types of big data; structured, unstructured and semi-structured data. When it comes to discussions on big data, three major aspects that can be considered as its main dimensions are the volume, velocity, and variety of the data. This data is collected, analysed and checked for use by the end users. Cloud computing and the Internet of Things (IoT) are used to enable this huge amount of collected data to be stored and connected to the Internet. The time and the cost are reduced by means of these technologies, and in addition, they are able to accommodate this large amount of data regardless of its size. This chapter focuses on how big data, with the emergence of cloud computing and the Internet of Things (IOT), can be used via several applications and technologies.

Physical Exercise Rehabilitation System Based on Internet of Things and Big Data Technology

2021 ◽  
Author(s):  
Nana Zhao ◽  
Yinzhong Yan ◽  
Xiao Han ◽  
Gaofei Zhang ◽  
Long Chen
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Physical Exercise ◽  
Data Processing ◽  
Human Vision ◽  
The Internet ◽  
Exercise Rehabilitation ◽  
Rehabilitation System ◽  
The Internet Of Things ◽  
Big Data Technology

Abstract With the combination of big data technology and the Internet of Things and exercise rehabilitation, we are also facing the problem of processing massive amounts of data. Whether these data can be used well has become the key to obtaining good business benefits and improving user experience. This paper applies the Internet of Things and big data technology to the physical exercise rehabilitation system to improve the sports data processing and combine with the big data processing technology to explore the factors affecting the effect of exercise rehabilitation to improve the effect of physical exercise rehabilitation. Moreover, this paper proposes a posture recognition algorithm based on human vision, and defines the human posture described by the feature, which improves the classifier's ability to classify different posture data. Finally, this paper combines experimental analysis to verify the effect of the method in this paper. From the research results, it can be seen that the method constructed in this paper has a good rehabilitation effect of physical exercise.

scholarly journals A Patent Analysis on Big Data Projects

2022 ◽  
Vol 9 (1) ◽  
pp. 1-14
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Ernesto D. R. Santibanez Gonzalez
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Academic Research ◽  
Data Driven ◽  
The Internet ◽  
Word Classes ◽  
Patent Documents ◽  
The Internet Of Things

Studies concerning Big Data patents have been published; however, research investigating Big Data projects is scarce. Therefore, the objective of this study was to conduct an exploratory analysis of a patent database to collect information about the characteristics of registered patents related to Big Data projects. We searched for patents related to Big Data projects in the Espacenet database on January 10, 2021, and identified 109 records.. The textual analysis detected three word classes interpreted as (i) a direction to cloud computing, (ii) optimization of solutions, and (iii) storage and data sharing structures. Our results also revealed emerging technologies such as Blockchain and the Internet of Things, which are utilized in Big Data project solutions. This observation demonstrates the importance that has been given to solutions that facilitate decision-making in an increasingly data-driven context. As a contribution, we understand that this study endorses a group of researchers that has been dedicated to academic research on patent documents.

Role of Real-Time Big Data Processing in the Internet of Things

2017 ◽  
pp. 239-262
Author(s):  
Miyuru Dayarathna ◽  
Paul Fremantle ◽  
Srinath Perera ◽  
Sriskandarajah Suhothayan
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
The Internet ◽  
Big Data Processing ◽  
The Internet Of Things

scholarly journals Research on Workshop-Based Positioning Technology Based on Internet of Things in Big Data Background

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
FengChun Liu ◽  
YaLou Liu ◽  
DongHao Jin ◽  
XueYong Jia ◽  
TingTing Wang
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Signal Strength ◽  
The Internet ◽  
Localization Algorithm ◽  
The Difference ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication ◽  
The Internet Of Things

This paper first analyzes the data collection and data management of the workshop, obtains the data of the workshop changes with time, and accumulates the data. There are bottleneck problems such as big data being difficult to be fully used. Then, the concept of the Internet of Things was introduced into the workshop positioning to realize the comprehensive use of the big data in the workshop. Finally, aiming at the positioning problem of manufacturing workshop items, the ZigBee positioning algorithm, the received signal strength indication algorithm RSSI and the trilateration algorithm, is applied, and the trilateral positioning algorithm is applied to the CC2430 wireless MCU, and the positioning node is designed and implemented. The three-sided localization algorithm was used to locate and simulate the horizontal and vertical comparisons of six groups of workshop terminals. The results showed that the difference between the simulated position and the actual position did not exceed 1m, which was in line with the positioning requirements of the workshop.

scholarly journals Lightweight Coordination Patterns for Applications of the Internet of Things

2020 ◽  
Vol 25 (2) ◽  
pp. 117-123
Author(s):  
Waseem Akhtar Mufti
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Computer Network ◽  
Programming Model ◽  
Data Communication ◽  
The Internet ◽  
Automatic Data ◽  
Distributed Cluster ◽  
The Internet Of Things

AbstractApplications of the Internet of Things (IoT) are famously known for connecting devices via the internet. The main purpose of IoT systems (wireless or wired) is to connect devices together for data collection, buffering and data gateway. The collected large size of data is often captured from remote sources for automatic data analytics or for direct decision making by its users. This paper applies the programming pattern for Big Data in IoT systems that makes use of lightweight Java methods, introduced in the recently published work on ClientNet Distributed Cluster. Considering Big Data in IoT systems means the sensing of data from different resources, the network of IoT devices collaborating in data collection and processing; and the gateways servers where the resulting big data is supposed to be directed or further processed. This mainly involves resolving the issues of Big Data, i.e., the size and the network transfer speed along with many other issues of coordination and concurrency. The computer network that connects IoT may further include techniques such as Fog and Edge computing that resolve much of the network issues. This paper provides solutions to these problems that occur in wireless and wired systems. The talk is about the ClientNet programming model and its application in IoT systems for orchestration, such as coordination, data communication, device identification and synchronization between the gateway servers and devices. These devices include sensors attached with appliances (e.g., home automations, supply chain systems, light and heavy machines, vehicles, power grids etc.) or buildings, bridges and computers running data processing applications. As described in earlier papers, the introduced ClientNet techniques prevent from big data transfers and streaming that occupy more resources (hardware and bandwidth) and time. The idea is motivated by Big Data problems that make it difficult to collect it from different resources through small devices and then redirecting it. The proposed programming model of ClientNet Distributed Cluster stores Big Data on the nearest server coordinated by the nearest coordinator. The gateways and the systems that run analytics programs communicate by running programs from other computers when it is essentially required. This makes it possible to let Big Data rarely move across a communication network and allow only the source code to move around the network. The given programming model greatly simplifies data communication overheads, communication patterns among devices, networks and servers.

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