The amount and diversity of sensors on modern mobile devices, together with the computing performance that these devices can guarantee, make crowdsensing an important alternative to traditional sensor networks. Crowdsensing applications often exploit server-side components to aggregate, correlate, and manipulate the data collected my the mobile devices in the field. We advocate that this architecture limits the scenarios in which crowdsensing can be applied. Instead, we believe that crowdsensing should transition to a distributed foglike architecture, in which edge devices can be made responsible for most of the computation that needs to be achieved. In this paper we present A3Droid, a framework for developing crowdsensing applications for the Android platform. It supports a fog-like architecture, and helps the developer create robust and scalable applications that can collect and use high-quality data from the field. A3Droid was evaluated in a lab experiment that mimics a scenario in which geo-located data are collected from moving buses to gather up-to-date information about a city's traffic.

A3Droid: A framework for developing distributed crowdsensing

BARESI, LUCIANO;GUINEA MONTALVO, SAM JESUS;FILGUEIRA MENDONÇA, DANILO
2016

Abstract

The amount and diversity of sensors on modern mobile devices, together with the computing performance that these devices can guarantee, make crowdsensing an important alternative to traditional sensor networks. Crowdsensing applications often exploit server-side components to aggregate, correlate, and manipulate the data collected my the mobile devices in the field. We advocate that this architecture limits the scenarios in which crowdsensing can be applied. Instead, we believe that crowdsensing should transition to a distributed foglike architecture, in which edge devices can be made responsible for most of the computation that needs to be achieved. In this paper we present A3Droid, a framework for developing crowdsensing applications for the Android platform. It supports a fog-like architecture, and helps the developer create robust and scalable applications that can collect and use high-quality data from the field. A3Droid was evaluated in a lab experiment that mimics a scenario in which geo-located data are collected from moving buses to gather up-to-date information about a city's traffic.
2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
9781509019410
9781509019410
Computer Science Applications1707 Computer Vision and Pattern Recognition; Computer Networks and Communications; Human-Computer Interaction
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1009051
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