OVERVIEW


Phase 1

The collection of smart phones carried by people can be used for large scale sensing of the physical world by leveraging the cameras, microphones, GPSs, accelerometers, and other sensors on the phones. In order to verify that smartphone sensing is scalable, reliable, and cost-effective, we built and deployed McSense platform. For campus students to participate in the research study, McSense app is provided in Android market (download).

Phase 2

Serious games can be applied to solve interesting computer science problems. The purpose of this research is to understand the player's involvement, behavioral patterns and strategies while playing the location-based serious game. In order to verify that the serious games are scalable, reliable, and cost-effective to solve interesting problems, we built and deployed mobile game in Android platform. For campus students to participate in the research study, game is provided in Android market. (download).

RESEARCH DIRECTIONS


Phase 1 - Crowdsensing using the micro-payments

The main goal of this research is to set the course for mass adoption and automation of mobile people-centric sensing. We believe that mobile crowd sensing (i.e., crowd-sourced mobile sensing) with micro-payments represents the right approach to achieve this goal, but are aware that many challenges have to be overcome to make the vision reality. We plan to systematically explore the benefits and challenges of this people-centric sensing paradigm.

Sensor Data Validation

Ranging from manual photo tasks to automated sensing tasks for activity monitoring, any task can be crowd sourced to smart phones to sense data from different locations at reduced cost. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task. Therefore, it is important to validate the sensed data. Validating the context of every sensed data point of each participant is not a scalable solution. One alternative is to first validate the location associated with the sensed data points in order to achieve a certain degree of reliability about the sensed data. However, location validation without support from the wireless carriers is difficult. To address this problem, we propose ILR, a scheme in which we Improve the Location Reliability of mobile crowd sensed data with minimal human efforts.

Scheduling of Crowd Sensing Tasks

The mobile sensing infrastructure is shared among all applications in order to enable the largest number of users possible to participate in providing sensing data. Scheduling should maximize the number of task completions, find a good balance between real-time and long-term tasks, and maintain monetary fairness for both clients and providers. Additionally, it has to provide a fault-tolerance mechanism for incomplete tasks. Our goal is to create a flexible scheduling mechanism that allows us to experiment with different scheduling policies in order to study the effect of budget allocation and task priority on task completion and quality of data received. The scheduler will also be responsible for re-starting incomplete tasks.

Crowd Sensing in Smart Cities

McSense ParticipAct: This project is aimed at exploiring how and to what extent the power of mobile crowd sensing can be employed in smart cities. ParticipAct is a collaborative research with University of Bologna and please refer to ParticipAct for further details on this research direction.

Phase 2 - Mobile crowdsensing game

The main goals of the crowdsensing game user study are 1) to determine whether the mobile users are interested in playing the mobile crowdsensing games and 2) to compare the area coverage efficiency in mobile crowdsensing games vs crowdsensing using the micro-payments.


The WiFi area coverage maps for the McSense user study and the Alien vs. Mobile User game study in first 2-week period can be viewed from below links.
McSense WiFi Map
Game WiFi Map

The overall WiFi area coverage map for the McSense user study can be viewed from below link.
Overall McSense WiFi Map
The overall WiFi area coverage maps for the Alien vs. Mobile User game study can be viewed from below link.
Overall Game WiFi Map

PUBLICATIONS

  • Fostering ParticipAction in Smart Cities: A Geo-Social CrowdSensing Platform PDF
    Giuseppe Cardone, Luca Foschini, Cristian Borcea, Paolo Bellavista, Antonio Corradi, Manoop Talasila, Reza Curtmola
    To appear in IEEE Communications Magazine, Special Issue on Smart Cities, June 2013

FACULTY (Phase 1 & 2)

Cristian Borcea
Associate Professor
Department of Computer Science
New Jersey Institute of Technology

Reza Curtmola
Assistant Professor
Department of Computer Science
New Jersey Institute of Technology

Ph.D. STUDENT (Phase 1 & 2)


Manoop Talasila
Ph.D. Candidate
Department of Computer Science
New Jersey Institute of Technology

VISITING STUDENT (Phase 1)


Claudio Pellizzari
Visiting MS Student
University of Bologna

COLLABORATORS (Phase 1)


Faculty:
Antonio Corradi (Full Professor at University of Bologna)
Paolo Bellavista (Associate Professor at University of Bologna)
Luca Foschini (Assistant Professor at University of Bologna)

Ph.D. student:
Giuseppe Cardone (University of Bologna)

Phase 1 - Crowdsensing using micro-payments



Phase 2 - Crowdsensing game