Medusa: a programming framework for crowd-sensing applications
Medusa: a programming framework for crowd-sensing applications
复制标题
DOI：
10.1145/2307636.2307668
复制
发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Moo-Ryong Ra;Bin Liu;Thomas F. La Porta;Ramesh Govindan
中科院分区：
文献类型：
--
作者：
Moo-Ryong Ra;Bin Liu;Thomas F. La Porta;Ramesh Govindan
智能手机的无处不在及其车载传感功能激发了人群感知，这是一种利用人群的力量从大量手机用户那里收集传感器数据的能力。与以前的无线传感工作不同，人群传感提出了几个新的要求：支持环路中的人触发传感操作或审查结果，需要激励措施，以及隐私和安全。除了现有的众包系统外，人群感知还利用了移动设备的感知和处理能力。在本文中，我们设计并实现了一种新的人群感知编程框架Medusa，它满足了这些需求。Medusa提供高级抽象来指定完成人群感知任务所需的步骤，并采用分布式运行时系统来协调这些任务在智能手机和云上的集群之间的执行。我们已经在美杜莎的原型上实现了10个人群感知任务。我们发现，Medusa任务描述比实现这些人群感知任务所需的独立系统小两个数量级，并且运行时具有低开销，并且对动态和资源攻击具有健壮性。
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.