Big Data analysis tools in IoT and their challenges in open researches

Constante-Nicolalde, Fabián-Vinicio; Silva, Fernando José; Herrera, Boris; Pereira, António

doi:10.23919/cisti.2018.8399297

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Nicolalde et al (2018) investigated research efforts directed toward big data processing technologies. The authors discussed some associated challenges, such as data storage and analysis, knowledge discovery and computational complexities, scalability and data visualization, and information security.…”

Section: Background and Related Workmentioning

confidence: 99%

Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Rahmani

Azhir

Ali

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Rahmani

Azhir

Ali

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

A review on performance analysis of data mining methods in IoT

Dubey¹,

Kapoor²,

Kashyap³

2020

IJATEE

View full text Add to dashboard Cite

IoT is capable and helpful in interdisciplinary areas along with the convergence of multiple technologies and platforms. This study adheres the adaptation of data mining technologies along with big data and cloud computing with the IoT system with detailed discussion. This paper supports and provide systematic review and analysis based on the computational parameters and performance analysis. The analysis and discussion are based on the communication capability, system component communication, aspects of data mining, big data and cloud computing in IoT. Different types of transmission and communication barriers have also been discussed and analyze. Finally, based on the study and analysis a framework has been suggested for the smooth functioning of the IoT protocols.

show abstract

Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Coelho

Glória

Sebastião

2020

IoT

View full text Add to dashboard Cite

Water is a crucial natural resource, and it is widely mishandled, with an estimated one third of world water utilities having loss of water of around 40% due to leakage. This paper presents a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. The complete system architecture is detailed, including hardware, communication, and data analysis. A study to discover the best machine learning algorithm between random forest, decision trees, neural networks, and Support Vector Machine (SVM) to fit leak detection is presented, including the methodology, training, and validation as well as the obtained results. Finally, the developed system is validated in a real-case implementation that shows that it is able to detect leaks with a 75% accuracy.

show abstract

Big Data analysis tools in IoT and their challenges in open researches

Cited by 3 publications

References 12 publications

Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

A review on performance analysis of data mining methods in IoT

Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Contact Info

Product

Resources

About