A distributed event-driven architectural model based on situational awareness applied on internet of things

Almeida, Ricardo Borges; Junes, Victor Renan Covalski; Machado, Roger; Rosa, Diórgenes Yuri Leal da; Donato, Lucas Medeiros; Yamin, Adenauer; Pernas, Ana Marilza

doi:10.1016/j.infsof.2019.04.001

Cited by 22 publications

(15 citation statements)

References 14 publications

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“…Almeida et al [34] presented an architectural model to support scalability, flexibility, autonomy, and heterogeneity demands from IoT environments. Their work provides event collection, situation detection through on-the-fly event processing, and customizable dynamic reaction features.…”

Section: B Big Data and Machine Learning Tools Applied To Iotmentioning

confidence: 99%

“…In contrast to previous studies [31], [33], [34], this work focusses on providing methods to enable efficient development of ML applications with IoT data. Unlike the studies mentioned previously, this research examines the orchestration and automation of end-to-end ML workflows to support parallel training and deployment of multiple ML models with IoT data.…”

Section: B Big Data and Machine Learning Tools Applied To Iotmentioning

confidence: 99%

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ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data

2019

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Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may not have a final desirable outcome. This paper proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows, particularly on large volumes of data series. The ML4IoT framework enables the implementation of several types of ML models, each one with a different workflow. These models can be easily configured and used through a simple pipeline. ML4IoT has been designed to use container-based components to enable training and deployment of various ML models in parallel. The results obtained suggest that the proposed framework can manage real-world IoT heterogeneous data by providing elasticity, robustness, and performance. INDEX TERMS Big data, container-based virtualization, IoT, machine learning, machine learning workflow, microservices. JOSÉ M. ALVES received the B.Sc. degree in information systems from USP, Brazil, in 2010. He is currently pursuing the M.E.Sc. degree in software engineering with Western University, Canada. From 2010 to 2017, he was involved in numerous software development and data analytics projects for telecommunications companies in Brazil. His current research interests include big data, machine learning, the IoT, and cloud computing.

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Section: B Big Data and Machine Learning Tools Applied To Iotmentioning

confidence: 99%

Section: B Big Data and Machine Learning Tools Applied To Iotmentioning

confidence: 99%

ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data

2019

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“…The internet of things (IoT) allows the connection of devices to establish a strong infrastructure to control and monitor real-world physical systems [ 1 , 2 ]. An IoT system consists of many devices connected to a gateway or multiple gateways, as an intermediate between the devices and the server equipped with an IoT platform [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Sensor Node Network for Remote Moisture Measurement in Timber Based on Bluetooth Low Energy and Web-Based Monitoring System

Saban

Medus

Casans

et al. 2021

Sensors

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This paper proposes an IoT system based on wireless BLE connectivity to monitor the moisture content of wood, using a compact and low-cost moisture device that relies on a resistance measurement method valid for an ultra-wide range of resistance values. This device is digitally controlled with a BLE-incorporated micro-controller characterized by its small size and low power consumption, providing long-life battery. The proposed system consists of two main parts: first, the BLE moisture device including the moisture content measurement and wireless capability (BLE); second, the cloud-based monitoring platform, providing remote visualization and control for all the sensor nodes of the network. The complete infrastructure shows how multiple nodes can read and transmit moisture content of timber in buildings using small and unattended devices, with data saved in a central database and monitored by multiple commercial devices such as PC, smartphone, tablet, etc. The proposed system is innovative, scalable and low cost, and it can be deployed in wooden buildings and the wood industry, providing a practical solution that will help to avoid rot and other damaging effects caused by the moisture content.

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“…With the in-depth development of Internet technology, the attention of smart logistics has gradually increased, and has been regarded by the industry as one of the inevitable trends in the future development of logistics [1][2]. Under the background of e-commerce and "Internet +", traditional logistics companies and logistics parks have explored the road of intelligent transformation.…”

Section: Introductionmentioning

confidence: 99%

Research on Smart Logistics Model Based on Internet of Things Technology

Tang

2020

IEEE Access

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Logistics industry is an important part of national economic and social information. The construction of information platform based on the Internet of Things and cloud computing has become the goal and direction of the development of the smart logistics park. In view of the shortcomings of the current logistics system, this paper takes a logistics park as an example and puts forward the planning plan for the construction of the information platform of the intelligent logistics park. Based on the research on the current situation of information construction and the information needs of logistics park, the demand analysis and system framework design of intelligent logistics information system based on the Internet of Things are carried out. The design scheme realizes the functions of intelligent analysis, perception, optimization of decision-making and efficient execution of logistics park. Through the analysis of the system test and user trial feedback information, it is shown that the intelligent logistics information system based on the Internet of Things is designed to have good application effect.

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A distributed event-driven architectural model based on situational awareness applied on internet of things

Cited by 22 publications

References 14 publications

ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data

ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data

Sensor Node Network for Remote Moisture Measurement in Timber Based on Bluetooth Low Energy and Web-Based Monitoring System

Research on Smart Logistics Model Based on Internet of Things Technology

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