Real time pothole detection using Android smartphones with accelerometers

Mednis, Artis; Strazdiņš, Ģirts; Zviedris, Reinholds; Kanonirs, Georgijs; Selavo, Leo

doi:10.1109/dcoss.2011.5982206

Cited by 392 publications

(212 citation statements)

References 7 publications

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“…Mednis et al [36] and Ghose et al [35] later both developed road condition monitoring applications for a smartphone which sends sensor data to a remote server and alerts drivers of potholes in the road.…”

Section: Environmental Informationmentioning

confidence: 99%

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Engelbrecht

Booysen

Rooyen

et al. 2015

IET Intelligent Transport Systems

155

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Road crashes are a growing concern of governments and is rising to become one of the leading preventable causes of death, especially in developing countries. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks and driver assistance systems, as well as other ITS applications. In this paper, existing approaches of using smartphones for ITS applications are analyzed and compared. Particular focus is placed on vehicle-based monitoring systems, such as driving behavior and style recognition, accident detection and road condition monitoring systems. Further opportunities for use of smartphones in ITS systems are highlighted, and remaining challenges in this emerging field of research are identified.

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Section: Environmental Informationmentioning

confidence: 99%

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Engelbrecht

Booysen

Rooyen

et al. 2015

IET Intelligent Transport Systems

155

View full text Add to dashboard Cite

show abstract

“…This includes proprietary embedded sensors in vehicles [3,5] or by using smartphone applications that make use of the smartphone's deployed sensors for detecting road conditions [15,16]. Machine learning plays a crucial part in analyzing such roughness index data using either special propose algorithms [4] or other machine learning techniques to identify road safety hazards [1,17].…”

Section: Related Workmentioning

confidence: 99%

Detection of Motorway Disorders by Processing and Classification of Smartphone Signals Using Artificial Neural Networks

Salih

Kattan

Cevik

2016

International Journal of Natural Sciences Research

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“…Mednis et al discuss the uses of mobile sensors to detect the real time irregularity on roads [24]. Based on several parameters such as, average load on road, vibration on road, the possibility of a road being damaged at a specific time is determined.…”

Section: Related Workmentioning

confidence: 99%

Cellcloud: Towards a Cost Effective Formation of Mobile Cloud Based on Bidding Incentive

Noor¹,

Hasan²,

Haque³

2015

STCC

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In recent years, cloud computing has become one of the most dominant computing paradigms. Researchers have explored the possibility of building clouds out of loosely associated mobile computing devices. However, most such efforts failed due to the lack of a proper incentive model for the mobile device owners. In this paper, we propose CellCloud -a practical mobile cloud architecture, which can be easily deployed on existing cellular phone network infrastructures. CellCloud is based on a novel reputation-based economic incentive model in order to compensate the mobile device owners for the use of their phones as cloud computing nodes. CellCloud offers a practical model for performing cloud operations, with lower costs compared to a traditional cloud. We provide an elaborate analysis of the model with security and economic incentives as the major focus. Along with presenting a cost equation model, we perform extensive simulations to evaluate the performance and also analyze the feasibility of our proposed model. Our simulation results show that CellCloud creates a win-win scenario for all three stakeholders (client, cloud provider, and mobile device owners) to ensure the formation of a successful mobile cloud architecture.

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Real time pothole detection using Android smartphones with accelerometers

Cited by 392 publications

References 7 publications

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Survey of smartphone‐based sensing in vehicles for intelligent transportation system applications

Detection of Motorway Disorders by Processing and Classification of Smartphone Signals Using Artificial Neural Networks

Cellcloud: Towards a Cost Effective Formation of Mobile Cloud Based on Bidding Incentive

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