PublicSense: A Crowd Sensing Platform for Public Facility Management in Smart Cities

Wang, Zi; Guo, Bin; Yu, Zhiwen; Wu, Wenle; Zhang, Jiafan; Wang, Zhu; Chen, Huihui

doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0038

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Several research works deals with smart cities amongst the ones mentioned so far, as smart cities are quite a rich application domain for the usage of mobile devices as smart city sensors . Indeed, other platforms similar to City Soundscape have been developed, such as PublicSense for facility management, and ParticipAct for involving citizens in a living lab about urban and social sensing monitoring.…”

Section: Related Workmentioning

confidence: 99%

An osmotic computing infrastructure for urban pollution monitoring

2019

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Summary Urban pollution control systems suffer from the presence of fixed stations in a greater number than mobile monitoring devices. Data gathered from such stations provide detailed and reliable information, thanks to equipment quality and effective measuring protocols, but these sampled data are gathered from very limited areas and through discontinuous monitoring campaigns. Fortunately, the spread of technologies for mobility has fostered the development of new approaches like mobile crowdsensing (MCS), increasing the chances of using mobile devices, even personal ones, as suitable sensors for the urban monitoring scenario. Nevertheless, one of the open challenges is the management of integrated heterogeneous data flows, differing in terms of typology, technical specifications (eg, transmission protocols), and semantics. The osmotic computing paradigm aims at creating an abstract level between mobile devices/Internet‐of‐Things devices and a cloud platform, which enables opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and development of a middleware that integrates data coming from mobile and Internet‐of‐Things devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed for security management.

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Section: Related Workmentioning

confidence: 99%

An osmotic computing infrastructure for urban pollution monitoring

2019

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“…Zappatore et al 8 presented a mobile crowd sensing platform exploiting smartphone's built-in microphones as sound sensing devices for creating large-scale noise maps and for suggesting city managers' suitable noise reduction interventions. In order to facilitate public facility management and provide convenient services for smart city, Wang et al 9 developed a mobile crowd sensing platform (PublicSense) for participatory public information reporting, issues intelligent classification, and intelligent tagging. Traffic events detection is another area where mobile sensing can play an important role.…”

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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“…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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PublicSense: A Crowd Sensing Platform for Public Facility Management in Smart Cities

Cited by 8 publications

References 15 publications

An osmotic computing infrastructure for urban pollution monitoring

An osmotic computing infrastructure for urban pollution monitoring

Sparse sensing data–based participant selection for people finding

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

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