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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ra, Moo-Ryong Govindan, Ramesh Liu, Bin La Porta, Tom F. |
| 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. |
| Starting Page | 481 |
| Ending Page | 482 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450313018 |
| DOI | 10.1145/2307636.2307693 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-06-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Macroprogramming Crowd-sensing Worker mediation Smartphone Programming framework |
| Content Type | Text |
| Resource Type | Article |
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