Real-time data analytics and event detection for IoT-enabled communication systems

Ali, Muhammad Intizar; Ono, Naomi; Kaysar, Mahedi; Shamszaman, Zia Ush; Pham, Thu-Le; Gao, Feng; Griffin, Keith; Mileo, Alessandra

doi:10.1016/j.websem.2016.07.001

Cited by 33 publications

(4 citation statements)

References 19 publications

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“…Le-Phuoc et al introduced the Graph of Things (GoT) system, which ingests massive streams of heterogeneous data and allows access and querying through a SPARQL endpoint [45]. Ali et al developed a semantically enriched system for event detection and data analytics using distributed and heterogeneous data streams from IoT-enabled communication systems [46]. Cirillo et al [47] requirements.…”

Section: Smart City Data Modelingmentioning

confidence: 99%

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Hamed

Gaglione

Gluhak

et al. 2023

ACM Trans. Internet Things

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Cities are increasingly getting augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, for example, a city council). Over the last few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community – beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new start-ups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset), (ii) higher network communication and bandwidth requirements, and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities towards creating a business model that offers data on-demand within smart city Internet of Things (IoT) data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises (SMEs)) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., trade-offs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.

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Section: Smart City Data Modelingmentioning

confidence: 99%

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Hamed

Gaglione

Gluhak

et al. 2023

ACM Trans. Internet Things

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“…The robustness and performance of the data acquisition and transmission system is critical in object detection due to potential issues and bottlenecks in real-time data transmission and processing. 53 Numerous studies used offline detection methods in which data are first acquired and later exported to a computer for post-processing. This post-processing method reduces the monitoring efficiency.…”

Section: Design Of the Novel Inspection Systemmentioning

confidence: 99%

A novel intelligent inspection robot with deep stereo vision for three-dimensional concrete damage detection and quantification

Yuan

Xiong

et al. 2021

Structural Health Monitoring

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Crack assessment of reinforced concrete structures using stereo cameras is a potential way for increasing the efficiency and safety of infrastructure maintenance routines. However, existing damage methods for reinforced concrete structures are based on the segmentation of two-dimensional planes without consideration to the actual size of concrete damage. Furthermore, on-site structural monitoring requires the installation of a large number of contact-based sensing devices, resulting in the potentially excessive consumption of time and financial resources. Therefore, a new vision-based damage assessment method for reinforced concrete structures using a novel intelligent inspection robot with Internet of things–enabled data communication system is proposed in this article. In the first part of this article, the data acquisition system of the inspection robot and the algorithm for three-dimensional structural reconstruction using a stereo camera is discussed. The discussion is followed by a description of the method for crack quantification based on a new proposed deep-learning technique. Finally, to accomplish damage localization, the quantified concrete damage with actual size information is projected onto a three-dimensional surface point cloud reconstruction of the inspected structure. To verify the proposed method, a reinforced concrete column that has undergone cyclic loading failure is used as an inspection subject. The validation experiment demonstrated the ability of the proposed system to segment, localize, and quantify the damage in three-dimensional space with high accuracy.

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“…The real-time event detection system is designed to detect and classify the events for high way traffic data by Khazaei et al [16]. An event detection system designed for real-time data analytics of IoT enabled communication system by Ali et al [17]. A real-time monitoring system using social big data is proposed for disaster management by Choi and Bae [18].…”

Section: Related Workmentioning

confidence: 99%

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Manjunatha

Annappa

2020

SCPE

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Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts.

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Real-time data analytics and event detection for IoT-enabled communication systems

Cited by 33 publications

References 19 publications

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

A novel intelligent inspection robot with deep stereo vision for three-dimensional concrete damage detection and quantification

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

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