Social IoT-Enabled Emergency Event Detection Framework Using Geo-Tagged Microblogs and Crowdsourced Photographs
01 Jan 2019-pp 151-162
TL;DR: The streaming microblog tweets are investigated to detect the disaster events for a specified time and location and significant metadata features, namely photographs and its geo-tag, are incorporated to precisely identify the events in real time.
Abstract: Social Internet of things (SIoT) has obtained significant attention to address the computational intelligence for handling emergencies which can be sensed through the Internet of smart social things In recent years, humans act as a social sensor to disseminate the information via microblogs in Online Social Networks (OSNs) such as Twitter, Weibo At times, the reaction to certain microblogging prompts thousands of people to rethink and impulsively react on that which can handle humanity unrest situations during earthquakes, floods, Tsunamis, etc In this paper, the streaming microblog tweets are investigated to detect the disaster events for a specified time and location Firstly, the real-time Twitter streaming data is sensed via Apache Flume service agent Secondly, the tweets are preprocessed and filtered tweets are clustered using the microblog-DBSCAN algorithm The tweets belonging to various sliding window from diversified geographic event locations during disasters are aggregated In addition, the crowdsourced photographs are added with geo-tags (location) during the period of analysis stood up as supplementary evidence to detect an event which acts as crisis recovery The dynamic hive queries are executed to filter location-level tweets for the analysis In contrast to conventional approaches which mainly focus the microblog textual content, we incorporate significant metadata features, namely photographs and its geo-tag, to precisely identify the events in real time
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TL;DR: The aim of this paper is to review current activities in citizen science and crowdsourcing with respect to applications of pluvial flooding and classify them into four main themes.
Abstract: Pluvial flooding can have devastating effects, both in terms of loss of life and damage. Predicting pluvial floods is difficult and many cities do not have a hydrodynamic model or an early warning system in place. Citizen science and crowdsourcing have the potential for contributing to early warning systems and can also provide data for validating flood forecasting models. Although there are increasing applications of citizen science and crowdsourcing in fluvial hydrology, less is known about activities related to pluvial flooding. Hence the aim of this paper is to review current activities in citizen science and crowdsourcing with respect to applications of pluvial flooding. Based on a search in Scopus, the papers were first filtered for relevant content and then classified into four main themes. The first two themes were divided into (i) applications relevant during a flood event, which includes automated street flooding detection using crowdsourced photographs and sensors, analysis of social media, and online and mobile applications for flood reporting; and (ii) applications related to post-flood events. The use of citizen science and crowdsourcing for model development and validation is the third theme while the development of integrated systems is theme four. All four main areas of research have the potential to contribute to early warning systems and build community resilience. Moreover, developments in one will benefit others, e.g., further developments in flood reporting applications and automated flood detection systems will yield data useful for model validation.
73 citations
Cites background from "Social IoT-Enabled Emergency Event ..."
...…in real-time (Arthur et al., 2018), to detect, cluster and map flood events or to categorize different types of flood-related information (Kiatpanont et al., 2016; Pandey and Natarajan, 2016; Albahari and Schultz, 2017; Feng and Sester, 2018; Lin et al., 2018; Bhuvaneswari and Valliyammai, 2019)....
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TL;DR: This work proposes a resource-efficient clustering framework for AIS that hierarchically performs geo-textual clustering without significantly lowering the clustering quality, and achieves substantial time and memory efficiency while reducing the overall resource requirements for constrained end-user and edge devices compared to the standard hybrid geo- Textual Clustering framework.
Abstract: The Social Internet of Things (SIoT) paradigm incorporates social networking concepts with the Internet of Things (IoT) solutions to support novel services. The massive amount of data (big data) produced by SIoT necessitates efficient information processing frameworks to exploit social relationships and comprehend actionable information from real-world observations. Data from AI-enabled sensors (AIS) is typically geo-tagged, thus demanding geo-textual processing for information retrieval and analysis. Social media applications are the main source of geo-textual data as mobile users connect with millions of posts daily. The processing of big geo-textual data requires resource-efficient algorithms and frameworks. Clustering algorithms are often applied to geo-textual data to examine spatial, textual, and temporal information for event detection, sentiment analysis, and search query response. Clustering algorithms on big data are resource-hungry requiring comparisons among all data points to calculate similarity and distance metrics. Existing hybrid clustering techniques execute algorithms collectively on geo-textual data resulting in a enormous footprint for big data. We propose a resource-efficient clustering framework for AIS that hierarchically performs geo-textual clustering without significantly lowering the clustering quality. The proposed framework achieves substantial time and memory efficiency while reducing the overall resource requirements for constrained end-user and edge devices compared to the standard hybrid geo-textual clustering framework. Moreover, we augment the research work by developing open-source scripts for both hierarchical and hybrid clustering frameworks.
25 citations
TL;DR: 10 different machine learning algorithms are applied by utilizing sentiment analysis based on location-specific disaster-related tweets by aiming fast and correct response in a disaster situation to provide a quick response to earthquakes.
18 citations
TL;DR: A review of current integrated community-based approaches to urban pluvial flooding can be found in this article , where the limitations of these approaches to fully capture the multi-dimensional nature of UPF are explored in detail and research gaps are identified.
Abstract: Urban pluvial flooding (UPF) resulting from localized, intense, rainfall-generated ponding and overland flow causes a range of socio-environmental impacts. UPF is driven by a complex set of interconnected factors, including physical, historical, social, cultural, institutional, and economic conditions. Its impacts are increasing due to both biophysical change (e.g., global warming) and the interactions between the human and physical dimensions of the urban environment (e.g., land-use change). Notwithstanding its complexity and the rather low level of attention it has received in both research and practice, UPF is an issue that needs to be tackled from a comprehensive perspective. Different integrated approaches such as citizen science and socio-hydrology have tried to address UPF by coupling humans and environmental systems, reflecting the possible outcomes from the interaction between disciplines—albeit not without limitations. This paper presents findings on a review of current integrated community-based approaches to UPF research and discusses how scholars have approached this problem and its management. The limitations of these approaches to fully capture the multi-dimensional nature of UPF are explored in detail, and research gaps are identified. Finally, the paper provides suggestions for future research based on a transdisciplinary, transformative citizen science approach.
9 citations
TL;DR: The findings indicate that e-governments should be encouraged to attach great importance to microblogs, pay direct attention to emergencies during their life cycles, concentrate on multi-stage evaluations to discover their performance changes across the “turning points” of emergencies, and collaborate with superior or subordinate e- governors to improve their microblogs’ operational performances.
Abstract: Microblogging has become a crucial channel for e-governments to interact with the public and has been evolving as a powerful and promising tool to address emergency management. The operational performance of an e-government microblog under emergencies plays a key role in shaping the e-government’s overall performance. This study creatively proposes a novel multi-indicator system for evaluating this operational performance in the life cycles of emergencies and applies these multiple indicators in a data envelopment analysis method to establish an evaluation model. Twenty-four e-government microblogs concentrating on the bus crash emergency in the Wanzhou district of Chongqing city are evaluated by this model. The evaluation results indicate that the proposed multi-indicator system and evaluation model are feasible and effective. This study’s findings indicate that e-governments should be encouraged to attach great importance to microblogs, pay direct attention to emergencies during their life cycles, concentrate on multi-stage evaluations to discover their performance changes across the “turning points” of emergencies, and collaborate with superior or subordinate e-governments to improve their microblogs’ operational performances.
5 citations
References
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TL;DR: This paper presents a revision of the new methodologies that are designed to allow for efficient data mining and information fusion from social media and of thenew applications and frameworks that are currently appearing under the “umbrella” of the social networks, socialMedia and big data paradigms.
681 citations
TL;DR: A novel paradigm of "social network of intelligent objects", namely the Social Internet of Things (SIoT), based on the notion of social relationships among objects is introduced, and a preliminary architecture for the implementation of SIoT is presented.
Abstract: The actual development of the Internet of Things (IoT) needs major issues related to things' service discovery and composition to be addressed. This paper proposes a possible approach to solve such issues. We introduce a novel paradigm of "social network of intelligent objects", namely the Social Internet of Things (SIoT), based on the notion of social relationships among objects. Following the definition of a possible social structure among objects, a preliminary architecture for the implementation of SIoT is presented. Through the SIoT paradigm, the capability of humans and devices to discover, select, and use objects with their services in the IoT is augmented. Besides, a level of trustworthiness is enabled to steer the interaction among the billions of objects which will crowd the future IoT.
488 citations
Proceedings Article•
01 Jan 2010TL;DR: This paper reports on a use case of a distributed sensor-actor environment in which both humans and technical systems together form a socio-technical network.
Abstract: In this paper we investigate on the potential of combining social and technical networks to collaboratively provide services to both human users and technical systems. In the Internet of Things (IoT), things talk and exchange information to realize the vision of future pervasive computing environments. The common physical and social space emerges by the objects’ ability to interconnect, not only amongst themselves, but also with the human beings living and working in them. In this paper, we report on a use case of a distributed sensor-actor environment in which both humans and technical systems together form a socio-technical network.
138 citations
TL;DR: This paper extracts the “embedded” intelligence about individual, environment, and society, which can augment existing IoT systems with user, ambient, and social awareness, and attempts to enhance the IoT with intelligence and awareness under the W2T vision.
Abstract: The Internet of Things (IoT) represents the future technology trend of sensing, computing, and communication. Under the Wisdom Web of Things (W2T) vision, the next-generation Internet will promote harmonious interaction among humans, computers, and things. Current research on IoT is primarily conducted from the perspective of identifying, connecting, and managing objects. In this paper, however, we attempt to enhance the IoT with intelligence and awareness under the W2T vision. By exploring the various interactions between humans and the IoT, we extract the "embedded" intelligence about individual, environment, and society, which can augment existing IoT systems with user, ambient, and social awareness. The characteristics, major applications, research issues, the reference architecture, as well as our ongoing efforts to embedded intelligence are also presented and discussed.
120 citations
23 May 2012
TL;DR: The bi-directional effect between human and opportunistic IoT is characterized, the innovative application areas are presented, and the challenges raised by this new computing paradigm are discussed.
Abstract: The Internet of Things (IoT) is a technical revolution that represents the future of computing and communications. Under its vision, the next-generation Internet will promote the harmonious interaction between human, society, and smart things. The current research in IoT is mainly from the perspective of connecting and managing things. The humanized, social side of IoT, however, is still not explored. In this article, we intend to present the IoT from the human-centric perspective. By analyzing the tight-coupled relationship between human and opportunistic connection of smart things (e.g., mobile phones, vehicles), we propose Opportunistic IoT. It enables information sharing and dissemination within/among opportunistic communities that are formed with the movement and opportunistic contact nature of human. We characterize the bi-directional effect between human and opportunistic IoT, present the innovative application areas, and discuss the challenges raised by this new computing paradigm.
75 citations