A proposed genome of mobile and situated crowdsourcing and its design implications for encouraging contributions

Abstract

A number of papers have surveyed mobile crowdsourcing systems and, to a lesser extent, situated crowdsourcing systems. These surveys have either contributed a comprehensive taxonomy of the diverse application domains where the systems have tapped into or characterized different components of the system platforms. In this paper, we present a survey of mobile and situated crowdsourcing systems by addressing fundamental questions about user contributions that system designers pose when building new systems or evaluating existing ones. We select and analyze 40 mobile and situated crowdsourcing systems that had prototypes deployed and studied in the real world. Inspired by the MIT's genetic model of collective intelligence, we propose our genetic model and new genes for mobile and situated crowdsourcing systems by examining user contributions to the selected systems. We present the observed patterns of different genes and discuss how the model can be used to create new applications and design ideas. We also provide examples to illustrate how the unique genes inspire new design suggestions for encouraging user contributions in the context of mobile and situated crowdsourcing by reviewing the existing design principles of online communities.

Studying User Experience of a Hybrid Location Sensing System

“…The automatic, also called opportunistic, sensing approach does not require users' explicit participation, which is typically used when it is difficult to keep constant user participation. On the other hand, the manual, also called participatory, approach requires users' explicit participation, but gives users more flexibility by allowing them to decide when to share data and how privacy mechanisms are to be set up each time [8,10]. Our work revealed a new promoting interaction between the two sensing approaches-that providing a participatory sensing approach had a positive effect on the adoption of opportunistic sensing.…”

“…Prior systems mostly applied only one sensing technology, and very few location-based systems applied hybrid sensing solutions [8]. Those that applied hybrid sensing approaches either did not study user behaviors (e.g., [13]) or could not support automatic proximity-based location sensing (e.g., [7]).…”

Toward a real-time and budget-aware task package allocation in spatial crowdsourcing

“…Ubiquitous technologies, such as smartphones and public displays, are now mature enough to allow users to contribute to a wide range of crowdsourcing tasks regardless of time and location [14]. Various mobile devices and wearable technologies equip people with unprecedented computing and sensing capabilities, giving birth to the prevalence of spatial crowdsourcing [13][14][15].…”

“…The main difference between online and mobile crowdsourcing is the working platform. With the development of location-based technology and the popularity of smartphones, sufficient location information can now be easily obtained [12], resulting in new and practical spatial crowdsourcing based on sensor devices [13][14][15]. Spatial crowdsourcing depends on web platforms or mobile apps and significantly considers the location of tasks and users [16][17][18].…”

Bluetooth Low Energy (BLE) Beacons Alone Didn't Work!

“…Based on the findings from the first study, we made three revisions to the app. First, instead of relying on opportunistic sensing by using BLE beacons, we added a participatory sensing feature [5]-students can manually check in to the class when the auto-sensing feature fails or is not preferred. A manual check in has to be validated with the user's GPS location, which needs to be within several meters from the classroom location.…”