A Photo-Based Mobile Crowdsourcing Framework for Event Reporting

Hamrouni, Aymen; Ghazzai, Hakim; Frikha, Mounir; Massoud, Yehia

doi:10.1109/mwscas.2019.8884949

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…To explore the applicability of visual-based information, they employed visual models which are subsequently used to assign an image to a cluster. Hamrouni A et al [57] proposed photo-based Mobile Crowdsourcing (MCS) framework for event reporting, which used an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. A summary of the representation of image based event detection is shown in Table 1.…”

Section: Image Based Data Representationmentioning

confidence: 99%

A Survey of Data Representation for Multi-Modality Event Detection and Evolution

2022

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The rapid growth of online data has made it very convenient for people to obtain information. However, it also leads to the problem of “information overload”. Therefore, how to detect hot events from the massive amount of information has always been a problem. With the development of multimedia platforms, event detection has gradually developed from traditional single modality detection to multi-modality detection and is receiving increasing attention. The goal of multi-modality event detection is to discover events from a huge amount of online data with different data structures, such as texts, images and videos. These data represent real-world events from different perspectives so that they can provide more information about an event. In addition, event evolution is also a meaningful research direction; it models how events change dynamically over time and has great significance for event analysis. This paper comprehensively reviews the existing research on event detection and evolution. We first give a series of necessary definitions of event detection and evolution. Next, we discuss the techniques of data representation for event detection, including textual, visual, and multi-modality content. Finally, we review event evolution under multi-modality data. Furthermore, we review several public datasets and compare their results. At the end of this paper, we provide a conclusion and discuss future work.

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Section: Image Based Data Representationmentioning

confidence: 99%

A Survey of Data Representation for Multi-Modality Event Detection and Evolution

2022

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“…Mobile Spatial Automated Sensing Task Recruitment [38,47] Advanced routing strategies beyond minimizing time traveled; powered through crowd-sourced data X X X [30] Spectrum sensing assignment X [11] Multi-objective task allocation X X [31] Ratings system for crowdsourced workers X [36] Spatial Crowdsourcing Task assignment survey X X [37] Crowdsourcing assignment for mobile crowdsensing X Edge Computing [33,48] Edge computing architectures for mobile crowdsensing X [10,49] Efficient data processing and handling on the edge X [27] Survey of IoT; discussion of edge computing X X X Security/Privacy [40,41] End-to-end encrypted mobile crowdsourcing X [42][43][44][45] Obfuscation of identifying information of crowdsourcing by reducing granularity of data X X X [46] Obfuscation of data in edge computing servers X [39] Perspectives and suggestions of engineering "privacy by design" in smart vehicles X…”

Section: Concept Reference(s) Contribution Crowdsourcing Variantmentioning

confidence: 99%

“…Edge computing is a recent development that aims to utilize computers closer to the data collection sources to minimize bandwidth utilization in the network [27]. We reeviewed papers that related on applying edge computing in crowdsourcing applications [33,48], and how data processing on the edge is more efficient [10,49].…”

Section: Concept Reference(s) Contribution Crowdsourcing Variantmentioning

confidence: 99%

“…Anomaly detection systems would be a viable candidate for the edge filtration system; however, these require a certain amount of training data to be able to build a proper distribution of the data input. The type of data inputted into the system will also determine which kind of anomaly-detection system to use (e.g., for images, an auto-encoder network may be the best type of system to use [49]). Additionally, the devices on the edge are likely to be embedded and therefore resource-scarce devices.…”

Section: Data Filtration On the Edgementioning

confidence: 99%

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Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

et al. 2020

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The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by mobile, spatial, and passive sensing crowdsourcing techniques due to improved connectivity, higher throughput, smart vehicles containing many embedded systems and sensors, and novel distributed processing techniques. These crowdsourcing systems have the capability of profoundly transforming transportation systems for the better by providing more data regarding (but not limited to) infrastructure health, navigation pathways, and congestion management. In this paper, we review and discuss the architecture and types of ITS crowdsourcing. Then, we delve into the techniques and technologies that serve as the foundation for these systems to function while providing some simulation results to show benefits from the implementation of these techniques and technologies on specific crowdsourcing-based ITS systems. Afterward, we provide an overview of cutting edge work associated with ITS crowdsourcing challenges. Finally, we propose various use-cases and applications for ITS crowdsourcing, and suggest some open research directions.

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“…Aerial photo data points were applied in this study to ground-truth modelled and observed (satellite) flood extent by a comparative analysis of the percentage of hits and misses. Aerial photos mostly derived through low-cost systems, e.g., volunteer GIS (Geographic Information System), unmanned area vehicles (UAVs) and social media, have become an important part of flood model validation in recent decades [53,54].…”

Section: Aerial Photosmentioning

confidence: 99%

Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology

Ekeu-wei

Blackburn

2020

IJGI

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Consistent data are seldom available for whole-catchment flood modelling in many developing regions, hence this study aimed to explore an integrated approach for flood modelling and mapping by combining available segmented hydrographic, topographic, floodplain roughness, calibration, and validation datasets using a two-dimensional Caesar-Lisflood hydrodynamic model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Available segments of remotely-sensed and in situ datasets (including hydrological, altimetry, digital elevation model, bathymetry, aerial photo, optical imagery, and radar imagery data) available to different degrees in the Niger-South hydrological area were systematically integrated to draw maximum benefits from all available data. Retrospective modelling, calibration, and validation were undertaken for the whole Niger- South hydrological catchment area of Nigeria, and then these data were segmented into sub-domains for re-validation to understand how data variability and uncertainties impact the accuracy of model outcomes. Furthermore, aerial photos were applied for the first time in the study area for flood model validation and for understanding how different physio-environmental properties influenced the synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria. This study demonstrates how the complementary strengths of open, readily available geospatial datasets and tools can be leveraged to model and map flooding within acceptable levels of uncertainty for flood risk management.

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A Photo-Based Mobile Crowdsourcing Framework for Event Reporting

Cited by 12 publications

References 11 publications

A Survey of Data Representation for Multi-Modality Event Detection and Evolution

A Survey of Data Representation for Multi-Modality Event Detection and Evolution

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology

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