Medusa: a programming framework for crowd-sensing applications

  • Authors:

  • Moo-Ryong Ra;Bin Liu;Tom F. La Porta;Ramesh Govindan

  • Affiliations:

  • University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA;Pennsylvania State University, University Park, PA, USA;University of Southern California, Los Angeles, CA, USA

  • Venue:
  • Proceedings of the 10th international conference on Mobile systems, applications, and services
  • Year:
  • 2012

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Abstract

The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.