IoT and semantic web technologies for event detection in natural disasters

Greco, Luca; Ritrovato, Pierluigi; Tiropanis, Thanassis; Xhafa, Fatos

doi:10.1002/cpe.4789

Cited by 28 publications

(16 citation statements)

References 29 publications

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“…Event detection backed by IoT sensor data and social media streams can detect any incident within the first few seconds of its occurrence [47]. Pattern recognition mechanism offers the machine learning ability to detect the useful patterns of information from textual or spatial data sets crucial for disaster management [48]. Semantic engine can be utilized for effective information management, i.e., categorizing, searching and extracting of unstructured information.…”

Section: ) Data Analytics and Management Layermentioning

confidence: 99%

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

et al. 2019

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Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator, and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed. INDEX TERMS Big data analytics, Internet of Things, smart city, disaster management, Hadoop, spark, smart data analytics, geo-social media analytics, disaster resilient smart city.

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Section: ) Data Analytics and Management Layermentioning

confidence: 99%

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

et al. 2019

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“…IoT offers smart aggregation, integration, and analysis of multi-dimensional and multi-sourced data, which are the main steps for situational awareness for effective decision-making. In a study [37] the authors demonstrated how IoT with semantic web technologies can be successfully deployed for earthquakerelated event detection. The proposed system was able to semantically annotate streams that were retrieved from web services gathering IoT-based sensors data for effective earthquake event detection.…”

Section: B From Wsn To Iot For Disaster Managementmentioning

confidence: 99%

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

et al. 2019

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The recent development of big data analytics (BDA) and the Internet of Things (IoT) technologies create a huge opportunity for both disaster management systems and disaster-related authorities (emergency responders, police, public health, and fire departments) to acquire state-of-the-art assistance and improved insights for accurate and timely decision-making. The motivation behind this research is to pave the way for effective utilization of the available opportunities that the BDA and IoT collaboratively offer to predict, understand and monitor disaster situations. Most of the conventional disaster management systems lack the support for multiple new data sources and real-time big data processing tools that can assist decision makers with quick and accurate results. This paper highlights the importance of BDA and IoT for disaster management and investigates recent studies directed towards the same. We classify a thematic taxonomy with several related attributes and inspect the prevalent solutions to propose a conceptual reference model for the deployment of BDA-and IoT-based disaster management environments. The reference model with its proposed integrated parameters can provide guidelines to harvest, transmit, manage, and analyze disaster data from various data sources to deliver updated and valuable information for disaster management. We also enumerate some important use cases from a disaster management perspective. Finally, we highlight the main research challenges that need to be addressed in such an important field of research. INDEX TERMS Big data analytics, data sources, disaster communications, disaster management, Internet of Things, reference model, taxonomy.

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“…With the new generation of information technology, Internet of Things (IoT) has developed rapidly and has been applied to support applications in various fields such as environmental protection and military monitoring [1], [2]. IoT nodes connect and communicate with each other through sensing and perception.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

et al. 2020

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Internet of Things (IoT) has been widely used to facilitate environmental perception, where detecting the boundary regions for continuous objects with energy-efficient manner is a challenge to be explored to support domain applications. This paper proposes a novel approach for continuous objects boundary prediction and detection in IoT sensing network, which is called Cloud Model-IoT Sensing Network Collaborative (CM-IoTSNC). Specifically, when an event is potentially occurred, the atmospheric dynamic diffusion model deployed on the cloud is adopted to predict gas diffusion trend in ideal and complex environments, moreover prediction results are transmitted to the IoT devices in the real-time fashion for scheduling security plans in advance. One-hop neighbor nodes are activated by abnormal nodes to determine a more accurate boundary region. Compared our technique with two traditional methods, namely Wireless Sensor Monitoring and Activating One-hop Neighbor Nodes, experimental results show that our method has a good performance in reducing the energy consumption and prolonging the lifetime of the network. INDEX TERMS Boundary detection, continuous objects, IoT sensing networks, energy efficiency, gas dynamic diffusion model.

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IoT and semantic web technologies for event detection in natural disasters

Cited by 28 publications

References 29 publications

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

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