Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2307636.2307693 | MobiSys |
Keywords | Field | DocType |
capabilities motivates crowd-sensing,crowd-sourcing system,medusa task description,crowd-sensing application,high-level abstraction,mobile device,novel programming framework,runtime system,mobile phone user,crowd-sensing task,novel requirement,programming framework,process capability,satisfiability | Crowds,Wireless,Computer science,Exploit,Real-time computing,Mobile device,Mobile phone,Software framework,Embedded system,Cloud computing,Runtime system | Conference |
Citations | PageRank | References |
151 | 4.98 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moo-Ryong Ra | 1 | 595 | 28.68 |
| Bin Liu | 2 | 1281 | 68.98 |
| t la porta | 3 | 243 | 8.53 |
| ramesh govindan | 4 | 15430 | 2144.86 |