Internet of Things Data Storage Infrastructure in the Cloud Using NoSQL Databases

Vanelli, Brunno; Silva, Madalena Pereira da; Manerichi, Guilherme; Pinto, Alex Sandro Roschildt; Dantas, Mário A. R.; Ferrandin, Mauri; Boava, Adão

doi:10.1109/tla.2017.7896402

Cited by 17 publications

(6 citation statements)

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“…As a consequence, they have become the option of choice for applications involving geographically distributed data, large amounts of data, or scalability requirements [ 47 , 56 , 88 ]. This is particularly the case for services relying on Internet of Things (IoT) technology [ 89 ]. For example, in a recent case study where IoT enabled sensors provide measurements to monitor manufacturing defects in the automobile industry, usage of a NoSQL database allowed real-time data processing and, thus, the detection of faults at early stages of the manufacturing process [ 33 ].…”

Section: State-of-the-art Reviewmentioning

confidence: 99%

“…Since then, numerous companies and organisations have developed their own non-relational databases [67,87]. 2 Many studies have shown that non-relational databases enable better performance in terms of speed and flexibility [36,38,89]. Indeed, availability, real-time response, advanced data analysis, and the ability to manage bigdata remain weaknesses which are displayed by relational databases [28].…”

Section: Definitions and Theory-based Comparisonsmentioning

confidence: 99%

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Cloud based evaluation of databases for stock market data

Singh

Martyr

Medland³

et al. 2022

J Cloud Comp

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About fifty years ago, the world’s first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution.

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Section: State-of-the-art Reviewmentioning

confidence: 99%

Section: Definitions and Theory-based Comparisonsmentioning

confidence: 99%

Cloud based evaluation of databases for stock market data

Singh

Martyr

Medland³

et al. 2022

J Cloud Comp

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“…Lately, an effort to state shared application programme interfaces (APIs) for the automation of building energy systems was recommended by the project Connected Home over Internet Protocol (IP) [ 61 ], sponsored by big hi-tech enterprises and Zigbee Alliance. In this architecture, all the data produced by the sensors are transferred to remote cloud servers, where the data are deposited in no-structured query language (NOSQL) databases [ 62 ] and utilised by cognitive algorithms to gather information regarding consumers’ behaviours that is then further utilised to enhance the comfort and the energy reductions of the monitored building. In this scheme ( Figure 17 c), part of the intelligence is situated nearby in the building.…”

Section: Case Studies Of the Iot Applied To Building Energy Systemmentioning

confidence: 99%

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

Yaïci

Krishnamurthy²,

Entchev

et al. 2021

Sensors

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This paper summarises a literature review on the applications of Internet of Things (IoT) with the aim of enhancing building energy use and reducing greenhouse gas emissions (GHGs). A detailed assessment of contemporary practical reviews and works was conducted to understand how different IoT systems and technologies are being developed to increase energy efficiencies in both residential and commercial buildings. Most of the reviewed works were invariably related to the dilemma of efficient heating systems in buildings. Several features of the central components of IoT, namely, the hardware and software needed for building controls, are analysed. Common design factors across the many IoT systems comprise the selection of sensors and actuators and their powering techniques, control strategies for collecting information and activating appliances, monitoring of actual data to forecast prospect energy consumption and communication methods amongst IoT components. Some building energy applications using IoT are provided. It was found that each application presented has the potential for significant energy reduction and user comfort improvement. This is confirmed in two case studies summarised, which report the energy savings resulting from implementing IoT systems. Results revealed that a few elements are user-specific then need all be considered in the decision processes. Last, based on the studies reviewed, a few aspects of prospective research were recommended.

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“…Recently, an attempt to define common application program interfaces (APIs) for the automation of building was proposed by the project Connected Home over Internet Protocol (IP) [43], sponsored by big tech companies and Zigbee Alliance. In this architecture, all the data generated by the sensors are transmitted to remote cloud servers, where the data are stored in no-Structured Query Language (SQL) databases [44] and used by cognitive algorithms to infer information about users' habits and preferences, which is used to optimize the comfort (and the energy savings) of the monitored building. The strong point of a pure cloud-based approach, as well as its biggest weakness, is that the "intelligence" of the system is located on remote server.…”

Section: A Cognitive Renovation: Infrastructure Architecturementioning

confidence: 99%

A Cognitive-Driven Building Renovation for Improving Energy Efficiency: The Experience of the ELISIR Project

et al. 2020

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In the last few years, the technology re-evolution has deeply transformed several aspects of everyday life. For sure, one technology with a strong impact is the so-called Internet of Things (IoT). The IoT paradigm made it possible to break down the data barrier between the vertical domains on which the traditional information and communication technology (ICT) world was organized. Recently, the designers of home automation systems have begun looking to the IoT paradigm to ease the deployment of systems that are able to collect data from different plants. Such a situation has driven further evolution from the traditional automation system, where logic is defined by the programmer or by the user, to a cognitive system that is able to learn from the user’ habits regarding what should be the best configuration of plants. Several countries are funding renovations of public and private buildings for improving energy efficiency. Generally, such renovations are only focusing on the structure of the building and of its energy performance (e.g., the thermal envelope, window units, air-conditioning plants, and renewable generators) and largely ignoring the use of intelligent devices. On the contrary, scientific literature and practice have demonstrated that the wider use of IoT sensors, as well as distributed and remote intelligence, is fundamental to optimize energy consumption. This research work aimed to identify issues due the application of cognitive solutions during the renovation phase of buildings. In particular, the paper presents a cognitive architecture to support the operation and management phases of buildings, thanks to the massive digitalization of the entire supply chain of the construction sector from the single building element to the entire construction process. Such an architecture is capable of combining data from the IoT sensors and actuators of smart objects installed during the renovation phase, as well as legacy building automation systems. As an indication of the capability of the proposed solution, an intelligent window device was developed and validated. Within the Energy, Life Styled, and Seismic Innovation for Regenerated Buildings (ELISIR) project, window units are equipped with sensors to monitor indoor and outdoor condition behaviours of users. In addition, windows are able to react to changes in the environment by means of actuators that enable motorized opening and shading. Thanks to the cognitive layer designed in the project, the window is able to automatically define the best rules for opening and shading by using the local controller to satisfy user’s habits and energy efficiency targets. The cognitive layer defines the appropriate rules for opening and shading using the decision tree algorithm applied to the data generated by the sensors in order to infer users’ preferences. For this research, two prototypes of the window units were installed in two offices of Politecnico di Milano, Italy. The accuracy of this algorithm to classify the users’ behaviour and preferences was found to be around 90%, considering an observation interval of two months.

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Internet of Things Data Storage Infrastructure in the Cloud Using NoSQL Databases

Cited by 17 publications

References 0 publications

Cloud based evaluation of databases for stock market data

Cloud based evaluation of databases for stock market data

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

A Cognitive-Driven Building Renovation for Improving Energy Efficiency: The Experience of the ELISIR Project

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