A novel localization and coverage framework for real-time participatory urban monitoring

Khan, Adnan; Imon, Sk Kajal Arefin; Das, Sajal K.

doi:10.1016/j.pmcj.2015.07.001

Cited by 20 publications

(12 citation statements)

References 45 publications

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“…For example, as a widely acknowledged difficult problem, author attribution and name disambiguation among different platforms require complicated processing models, even manpower especially, when the name structures of the users are different than each other (Wang et al , ; Tang et al , ; Zhang et al , ). However, the uncertainty inherent with crowdsourcing‐based collection such as inconsistency observation standards, subjective basis and data errors of missing data still must be solved (Khan et al , ; Tipaldo & Allamano, ).…”

Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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“…With the success of urban crowdsourced transportation systems (e.g., UberPOOL [22], Hitch [23]), a large amount of researchers have been interested in developing new crowdsourcing based intelligence transport prototypes [24][25][26][27] in recent years, but few studies are done in task allocation of urban crowdsourced transportation. By contrast, many task allocation algorithms [15,[28][29][30][31][32][33][34][35][36][37] have been proposed in mobile crowdsensing which is similar to urban crowdsourced transportation, and different task allocation algorithms are aimed at different optimization goals. Here, we highlight some most related work.…”

Section: Related Workmentioning

confidence: 99%

SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation

Wang

2019

Wireless Communications and Mobile Computing

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Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total saved distance) is an NP-hard problem. We propose a heuristic greedy algorithm called Saving Most First (SMF) which is simple and effective in assigning tasks. Then, an optimized SMF based genetic algorithm (SMF-GA) is devised to jump out of the local optimal result. Finally, we demonstrate the performance of SMF and SMF-GA with extensive evaluations, based on a large scale real vehicle traces. The evaluation with large scale real dataset indicates that both SMF and SMF-GA algorithms outperform other benchmark algorithms in terms of saved distance, participant profits, etc.

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“…Information about the thermal environment must be complemented with information about individuals’ locations and activities, particularly time outdoors, distances traveled, and use of transportation. These data can be collected through tracking devices, including dedicated GPS devices ( Kerr et al 2011 ), cell phone–based location information ( Khan et al 2015 ), light-sensing devices (e.g., Bernhard et al 2015 ), or through personal reports via logs, surveys, time–activity diaries, or interviews (e.g., Klepeis et al 2001 ; McCurdy and Graham 2003 ). These approaches require volunteers who are willing to report their travel patterns or to have them recorded.…”

Section: Estimating Personal Heat Exposure Indirectlymentioning

confidence: 99%

Opportunities and Challenges for Personal Heat Exposure Research

Kuras

Richardson

Calkins

et al. 2017

Environ Health Perspect

142

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Background:Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.Objectives:The first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.Discussion:We define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated indirectly through the combination of time–activity and meteorological data sets. Complementary information to understand individual-scale drivers of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches, and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment concerning extreme heat.Conclusions:Personal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when, where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of heat exposure. https://doi.org/10.1289/EHP556

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A novel localization and coverage framework for real-time participatory urban monitoring

Cited by 20 publications

References 45 publications

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation

Opportunities and Challenges for Personal Heat Exposure Research

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