Paper Info
Title
A framework for collaborative sensing and processing of mobile data streams: demo.
Abstract
Emerging mobile applications involve continuous sensing and complex computations on sensed data streams. Examples include cognitive apps (e.g., speech recognition, natural language translation, as well as face, object, or gesture detection and recognition) and anticipatory apps that proactively track and provide services when needed. Unfortunately, today's mobile devices cannot keep pace with such apps, despite advances in hardware capability. Traditional approaches address this problem by computation offloading. One approach offloads by sending sensed streams to remote cloud servers via cellular networks or to cloudlets via Wi-Fi, where a clone of the app runs [2, 3, 4]. However, cloudlets may not be widely deployed and access to cloud infrastructure may yield high network delays and can be intermittent due to mobility. Morever, users might hesitate to upload private sensing data to the cloud or cloudlet. A second approach offloads to accelerators by rewriting code to use DSP or GPU within mobile devices. However, using accelerators requires substantial programming effort and produces varied benefits for diverse codes on heterogeneous devices.
Year
DOI
Venue
2016
10.1145/2973750.2985620
MobiCom
Field
DocType
Citations 
Cloudlet,Computer science,Upload,Gesture recognition,Computer network,Computation offloading,Mobile device,Cellular network,Mobile broadband,Cloud computing,Distributed computing
Conference
3
PageRank 
References 
Authors
0.42
4
3
Name
Order
Citations
PageRank
Songchun Fan1455.24
Theodoros Salonidis2124793.31
Benjamin C. Lee393151.85