CrowdWatch: Dynamic Sidewalk Obstacle Detection Using Mobile Crowd Sensing

Wang, Qianru; Guo, Bin; Wang, Leye; Xin, Tong; Du, Hejun; Chen, Huihui; Yu, Zhiwen

doi:10.1109/jiot.2017.2750324

Cited by 32 publications

(11 citation statements)

References 38 publications

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“…Traffic events detection is another area where mobile sensing can play an important role. Wang et al 10 presented a pedestrian safety alarm system by leveraging mobile crowd sensing and crowd intelligence aggregation to detect temporary obstacles and make effective alerts for distracted walkers. Rui et al 11 studied the problem of allocating location-dependent tasks in vehicular crowdsensing applications.…”

Section: Crowdsensingmentioning

confidence: 99%

Sparse sensing data–based participant selection for people finding

Tian

Tang

2019

International Journal of Distributed Sensor Networks

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With the emerging of participatory sensing, crowdsensing-based lost people finding is arising. As a special locationcentric task, participant selection is a key factor to determine success or failure of lost people finding result. Besides traditional influence, like Quality of Information contribution, candidate's spatial proximity to the lost people is crucial in participant selection procedure. In order to evaluate how possible a candidate can approach lost people, the probability distribution of their tracing points should be predicted. However, the sparse sensing data problem has been a bottleneck of estimating people's probable position. To overcome these issues, we study the problem of selecting optimal participants according to each candidate's spatial proximity and Quality of Information contribution. In the first procedure, we proposed a Received Signal Strength Indicator Positioning-Determined Compressive Sensing algorithm to interpolate missing Received Signal Strength Indicator data. Then, a location-important marking method is put forward to select a set of high-quality data for estimating missing people's location. In the third procedure, a double-condition greedy participant selection approach, which guarantees candidates' spatial proximity and Quality of Information contribution, is executed to select optimal participants. Simulation results demonstrate that the proposed mechanism outperforms the other algorithms both in accuracy of positioning and quality of uploaded sensing data.

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Section: Crowdsensingmentioning

confidence: 99%

Sparse sensing data–based participant selection for people finding

Tian

Tang

2019

International Journal of Distributed Sensor Networks

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“…This system collects global positioning system (GPS) information from smartphones of pedestrians and vehicle drivers through mobile networks. In [13], the authors propose CrowdWatch which leverages mobile crowd sensing and crowd intelligence aggregation to detect temporary obstacles and make effective alerts for distracted pedestrians. The authors of [14] present Bumpalert that provides a generic solution without requiring any prior knowledge of the user's environment by estimating distances to nearby objects using the phones' speakers and microphones.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian walking safety system based on smartphone built‐in sensors

Xue

Fan

et al. 2018

IET Communications

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People watching smartphones while walking causes a significant impact to their safety. Pedestrians staring at smartphone screens while walking along the sidewalk are generally more at risk than other pedestrians not engaged in smartphone usage. In this study, the authors propose Safe Walking, an Android smartphone-based system that detects the walking behaviour of pedestrians by leveraging the sensors and front camera on smartphones, improving the safety of pedestrians staring at smartphone screens. More specifically, Safe Walking first exploits a pedestrian speed calculation algorithm by sampling acceleration data via the accelerometer and calculating gravity components via the gravity sensor. Then, this system utilises a greyscale image detection algorithm to detect the face and eye movement modes based on OpenCV4Android to determine if pedestrians are staring at the screens. Finally, Safe Walking generates a vibration by a vibrator on smartphones to alert pedestrians to pay attention to road conditions. The authors implemented Safe Walking on an Android smartphone and evaluated pedestrian walking speed, the accuracy of eye movement, and system performance. The results show that Safe Walking can prevent the potential danger for pedestrians staring at smartphone screens with a true positive rate of 91%.

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“…The reason for choosing to demonstrate on these sensors is that they are the most power-hungry sensors of modern smartphones. Looking at the applications listed in Table 1 ( Section 2 ), we observe that road monitoring [ 13 , 14 , 16 , 60 , 72 ] and noise monitoring [ 45 , 47 , 53 , 58 , 59 ] are quite common applications. Among these works, we select NeriCell [ 13 ] (road bump detection) and Ear-Phone [ 47 ] (city noise map) as representatives since they have been very well recognized by the urban sensing community.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

“…Smartphones with integrated sensors have enabled the development of low-cost and reliable large-scale sensing systems including personal sensing [ 1 , 2 , 3 , 4 ], social behavior sensing [ 5 , 6 , 7 , 8 ], environmental monitoring [ 9 , 10 , 11 , 12 ], smart transportation and road monitoring [ 13 , 14 , 15 , 16 ], electromagnetic monitoring [ 17 ], radiation monitoring [ 18 ], and event monitoring [ 19 ]. Since smartphones are consumer devices, a sensing service design should consider a user’s role in performing sensing tasks: data collection, analysis, verification, and sharing.…”

Section: Introductionmentioning

confidence: 99%

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications

Nguyen

Scholten

et al. 2017

Sensors

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Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring.

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CrowdWatch: Dynamic Sidewalk Obstacle Detection Using Mobile Crowd Sensing

Cited by 32 publications

References 38 publications

Sparse sensing data–based participant selection for people finding

Sparse sensing data–based participant selection for people finding

Pedestrian walking safety system based on smartphone built‐in sensors

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications

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