Paper Info
Title
Finding Nemo: Finding Your Lost Child in Crowds via Mobile Crowd Sensing
Abstract
Mobile Crowd Sourcing/Sensing (MCS), as a new paradigm for participatory sensing, is suitable for large-scale hard tasks that are costly, or infeasible with conventional methods. Utilizing the ubiquitousness of \"crowds\" of sensor-rich smartphones, MCS has enormous potential to truly unleash the power of collaborative locating and searching at a societal scale. In this paper, we target the application of finding and locating the lost child in crowds via MCS. Conventional localization approaches require fixed anchor networks or fingerprinting points as references. It is not effective for locating the child in open and uncontrolled areas. We propose MCS-based collaborative localization via nearby opportunistically connected participators. To obtain sufficient measurements, we utilize one-hop and multi-hop assistants to reach more participators. Semidefinite Programming (SDP) based global optimization approaches are proposed to leverage all the location and ranging measurements in a best-effort way. We conduct extensive experiments and simulations in various scenarios. Compared with other classic algorithms, our proposed approach achieves significant accuracy improvement and could locate the \"unlocalizable\" child.
Year
DOI
Venue
2014
10.1109/MASS.2014.79
MASS
Keywords
Field
DocType
localization,sdp based global optimization approaches,fingerprinting points,mathematical programming,mobile crowd sensing, finding, localization,finding,anchor networks,sensor-rich smartphones,mobile crowd sourcing,multihop assistants,collaborative locating,semidefinite programming based global optimization approach,smart phones,sensors,findingnemo,mcs-based collaborative lost child localization,collaborative searching,groupware,mobile crowd sensing,mobile computing,one-hop assistants,police data processing
Crowds,Global optimization,Computer science,Computer network,Ranging,Participatory sensing,Semidefinite programming,Distributed computing
Conference
ISSN
Citations 
PageRank 
2155-6806
0
0.34
References 
Authors
14
2
Name
Order
Citations
PageRank
Kaikai Liu119020.37
Xiaolin Li 0001231.74