Mobile Crowd Sourcing/Sensing (MCS)
Guarding the child or pet in crowded places full of attractions is nontrivial. Locating the lost child/pet in open and uncontrolled areas without high cost cellular and GPS trackers are mission impossible. In this paper, we target the application of finding and locating the lost child in crowds via low-complexity wearable tags without Internet connection. Conventional location tracking approaches require fixed anchor networks, or fingerprinting points as references, or bulky devices with GPS and cellular connection. However, all these solutions are costly and not fine-grained enough for locating the child in open and uncontrolled areas. To overcome the communication coverage trade-off of the miniaturization of the wearable devices, we propose Mobile Crowd Sourcing/Sensing (MCS) based collaborative localization via nearby opportunistically connected participators with smartphones. To obtain sufficient measurements for better location resolution, we utilize one-hop and multi-hop assistants to reach more participators for transparent sensing assistance. Semidefinite Programming (SDP) based global optimization approaches are reformulated to overcome the bias and unsolvable problem caused by insufficient measurements in crowd sensing. Multi-Hop sparse ranging and coarse-grained location information are leveraged to jointly optimize the location of the wearable tag and assistant with unknown locations. 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. Utilizing the ubiquitousness of “crowds” of sensor-rich smartphones, our proposed approach has enormous potential to truly unleash the power of collaborative locating and searching at a societal scale.
Liu, K., and Li, X. (2014) “FindingNemo: Finding Your Lost Child in Crowds via Mobile Crowd Sensing.” The 11th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2014), Philadelphia, Pennsylvania. [pdf]