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Demo: Medusa: a programming framework for crowd-sensing applications by Moo-Ryong Ra; Bin Liu; Tom F. La Porta; Ramesh Govindan is a scholarly article available to read on EtoBox.

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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, crowdsensing poses several novel requirements: support for humans-inthe-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowdsourcing 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 crowdsensing 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 r

Author

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

Publisher

ACM

Published

2012

Language

EN