Detecting the road surface condition by using mobile crowdsensing with drive recorder

Bin, Piao; Aihara, Kenro

doi:10.1109/itsc.2017.8317818

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Besides, the authors used the concept of Smart and Connected Community (SCC) to indicate an evolution of smart cities, extending its application also to urban science. Piao and Aihara [96] used motion sensors' data to apply Crowdsensing to infrastructure. They aimed to monitor road surfaces, especially in snowy areas, through sensors embedded in smartphones, such as accelerometers and eventually cameras.…”

mentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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This paper presents a review of technologies under the paradigm 4.0 applied to the study of the thermal comfort and, implicitly, energy efficiency. The research is based on the analysis of the Internet of Things (IoT) literature, presenting a comparison among several approaches adopted. The central objective of the research is to outline the path that has been taken throughout the last decade towards a people-centric approach, discussing how users switched from being passive receivers of IoT services to being an active part of it. Basing on existing studies, authors performed what was a necessary and unprecedented grouping of the IoT applications to the thermal comfort into three categories: the thermal comfort studies with IoT hardware, in which the approach focuses on physical devices, the mimicking of IoT sensors and comfort using Building Simulation Models, based on the dynamic modelling of the thermal comfort through IoT systems, and Crowdsensing, a new concept in which people can express their sensation proactively using IoT devices. Analysing the trends of the three categories, the results showed that Crowdsensing has a promising future in the investigation through the IoT, although some technical steps forward are needed to achieve a satisfactory application to the thermal comfort matter.

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mentioning

confidence: 99%

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Tomat

Ramallo-González

Skarmeta-Gómez

2020

Sensors

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“…Crowd sensing refers to a large group of individuals collectively sharing mobile sensed data. Crowd sensing have already been used in, e.g., monitoring air quality (Liu et al 2018) and assessing road conditions (Piao and Aihara 2017). These methods could be also utilized, at least to some extent, in recording anomalies in forest environments, especially in areas of high human population density, such as urban forests.…”

Section: Developing Remote Sensing In Insect Disturbance Monitoringmentioning

confidence: 99%

Forest health monitoring in transition: Evaluating insect-induced disturbances in forested landscapes at varying spatial scales

Kantola¹

2019

Diss. For.

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“…to solve multiple problems ranging from roadside parking management [3], road condition detection [4], environmental pollution monitoring [5] and urban WiFi characterization [6] to digital map updating [7]. In general, a MCS system consists of three components: task requester, service platform and mobile user.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Target Tracking Approach Through Mobile Crowdsensing

et al. 2019

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As the widespread of mobile devices in recent years, mobile crowdsensing (MCS) has become a powerful mechanism to produce knowledge by collecting the individual contributed sensor data. In this paper, we aim to solve the target tracking problem through mobile crowdsensing. The traditional tracking method tends to rely on photos or videos provided by pre-deployed monitors, which may consume much power resources. Different from the traditional tracking method, the tracking approach through mobile crowdsensing (TAMC) proposed in this paper utilizes the wireless communication of mobile users to collect and contribute the valuable information about the target's whereabouts. Specifically, whenever the mobile users witness the target person, they will take photos of the target person and report the location and time of witnessing the target to the platform. Due to the fact that mobile users communicate with the platform only when they witness the target, the crowdsensing network composed of mobile users can be seen as a green network. In this way, the visited location history and corresponding time sequence of the target are available through the reports of mobile users. Once a new report is uploaded to the platform, the location history is updated. Then, according to the latest report, we apply a tree-based location prediction model named XGBoost, which is a scalable machine learning system, to predict the next place to be visited by the target. Finally, we conduct extensive experiments on a large-scale real-world dataset, namely, the Gowalla check-in dataset. The experimental results show that compared with the baseline methods, the tracking approach can predict the next places accurately.INDEX TERMS Mobile crowdsensing, target tracking, mobility prediction.

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Detecting the road surface condition by using mobile crowdsensing with drive recorder

Cited by 10 publications

References 12 publications

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

A Comprehensive Survey about Thermal Comfort under the IoT Paradigm: Is Crowdsensing the New Horizon?

Forest health monitoring in transition: Evaluating insect-induced disturbances in forested landscapes at varying spatial scales

An Efficient Target Tracking Approach Through Mobile Crowdsensing

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