On Opportunistic Coverage for Urban Sensing

Zhang, Dong; Ma, Huadóng; Liu, Liang; Zhao, Jing

doi:10.1109/mass.2013.66

Cited by 26 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…How would the system get maximum coverage of participants in the AoI for a minimal cost? One of the approach used is to determine the historical data of participants' spatiotemporal availability in an AoI, and use those historical traces to send sensing requests to participants with high-probability of availability in the AoI [4]- [6]. In [4], The dynamic tensor analysis algorithm is used to learn the time-series of trajectories so as to predict the future user's mobility path.…”

Section: Related Workmentioning

confidence: 99%

A Novel Quality and Reliability-Based Approach for Participants’ Selection in Mobile Crowdsensing

Barachi

Mathew

et al. 2019

IEEE Access

View full text Add to dashboard Cite

With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants' mobility, render the task of participants' selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors' quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants' selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants' selection. INDEX TERMS Data quality, mathematical modeling, mobile crowdsensing, participants' reliability, participants' selection.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Quality and Reliability-Based Approach for Participants’ Selection in Mobile Crowdsensing

Barachi

Mathew

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, the only work to take a perspective comparable to ours is that by Zhao et al [17], who aim at understanding the temporal and spatial frequency of sampling granted by opportunistic sensing. However, their approach is completely different from ours.…”

Section: Related Workmentioning

confidence: 99%

Driving Factors Toward Accurate Mobile Opportunistic Sensing in Urban Environments

Fiore

Nordio

Chiasserini

2016

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

The dramatic increase in the number and sensing capabilities of mobile devices is fostering opportunistic sensing as a paramount data collection paradigm in smart cities. According to this paradigm, sensing of large-scale phenomena is autonomously performed by mobile devices that provide irregular samples in time and space. The collected data is then transferred to a central controller, and processed so as to obtain a representation of the phenomenon. In this paper, we investigate the factors that impact the accuracy of mobile opportunistic sensing. Specifically, we characterize the accuracy of a phenomenon representation obtained from samples collected by mobile devices and processed through the popular LMMSE filter. We do so by drawing on random matrix theory, which allows us to deal with irregularly spaced samples. Our analytical expressions capture the fundamental relationships existing between the accuracy and the parameters of mobile opportunistic sensing. We apply our analytical results to a realistic scenario where atmospheric pollution samples are collected by vehicular and pedestrian users. We validate the proposed analytical framework, and then exploit the model to investigate the impact on mobile sensing accuracy of a number of parameters. These include the pedestrian and vehicle density, the participation ratio to the sensing application, the type of phenomenon to be sensed, and the level of noise and position errors affecting the collected samples.• M. Fiore and A. Nordio are with CNR-IEIIT,

show abstract

“…The compressed sensing approach was proposed to recover the sensing map from random and incomplete samples more effectively [3], [4]. The relationship between the sensing delay of an urban vehicular sensing system and the number of vehicles was analyzed based on taxis mobility traces [26]. In the second category, Sensorly [27] was a sensing platform for constructing cellular/WiFi network coverage maps.…”

Section: People-centric Sensingmentioning

confidence: 99%

COUPON: A Cooperative Framework for Building Sensing Maps in Mobile Opportunistic Networks

Zhang

Tang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

Self Cite

View full text Add to dashboard Cite

Abstract-Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for building various sensing maps. Most of existing mobile sensing applications consider users reporting and accessing sensing data through the Internet. However, this approach cannot be applied in the scenarios with poor network coverage or expensive network access. Existing data forwarding schemes for mobile opportunistic networks are not sufficient for sensing applications as spatial-temporal correlation among sensory data has not been explored. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated independently, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. Both the theoretic analysis and simulation results show that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead by both by theory and simulation analysis. We also evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93% compared with the non-cooperative scheme; ERF can reduce the transmission overhead by 78% compared with Epidemic Routing (ER); BSWF can increase the delivery ratio by 16%, and reduce the delivery delay and transmission overhead by 5% and 32% respectively, compared with Binary Spray-and-Wait (BSW).

show abstract

On Opportunistic Coverage for Urban Sensing

Cited by 26 publications

References 21 publications

A Novel Quality and Reliability-Based Approach for Participants’ Selection in Mobile Crowdsensing

A Novel Quality and Reliability-Based Approach for Participants’ Selection in Mobile Crowdsensing

Driving Factors Toward Accurate Mobile Opportunistic Sensing in Urban Environments

COUPON: A Cooperative Framework for Building Sensing Maps in Mobile Opportunistic Networks

Contact Info

Product

Resources

About