The session chair Ganesh Ananthanarayanan started the proceedings by noting that this session will
officially be the longest session (albeit interesting) in the history of CoNEXT.
1st Paper - Low Bandwidth Offload for Mobile AR
The first talk was from Puneeth Jain - a researcher in HP Labs. Puneeth mentioned that this work was his last as a PhD student, and thus this presentation "makes him feel like he is graduating today !!!".
Environmental fingerprinting has been proposed as a key enabler to Augmented Reality (AR). Although the environment fingerprinting can be done in many ways (e.g., wireless RF, magnetic field), visual fingerprinting is the most attractive option due to the inherent heterogeneity in many indoor spaces. However, matching a unique visual signature against a database of millions requires either impractical computation for a mobile device, or to upload large quantities of visual data for cloud offload.
The authors propose a system called 'VisualPrint', which provides means to offload only the most distinctive visual data, that is, only those visual signatures which stand a good chance to yield a unique match. VisualPrint enables cloud-offloaded visual fingerprinting with efficacy comparable to using whole images, but with an order reduction in network transfer.
a) Have u considered applying for deep learning ?
No, deep learning was not consierd. For deep learning one needs a huge dataset. Here, the goal was to create a visualprint using one image. The idea behind the paper was to create a social networking app where users could leave comments when they see an image. With this being the purpose, the authors think their method is best suited.
b) Some of these AR apps are outdoors, when there are lot of people, specaially in a crowd. how does feature extraction happen.
This issue has been handled in other work of their's. Not used in this work.
2nd paper -
D-Watch: Embracing “bad” Multipaths for Device-Free Localization with COTS RFID Devices
Speaker: Ju Wang
With more and more applications dependent on localization, the authors of this paper have come up with an interesting idea of device-free localization, to help applications like intrusion detection, elderly monitoring. This paper introduces D-Watch, a device-free system built on top of low cost commodity-off-the-shelf (COTS) RFID hardware. D-Watch leverages the “bad” multipaths to provide a decimeterlevel localization accuracy without offline training. In order to do this, D-watch harnesses the angle-of-arrival (AoA) information from the RFID tags’ backscatter signals.
a) How frequently can locations be attained? specially for a moving target?
People generally move 1m to 2m per sec. The system gets data more frequently, and so can track people more accurately.
b) Can your system work in different enviornments (eg., if there are open spaces ? ).
Yes, the authors expect their systems to work.
c) How much does people moving around impact accuracy? Did you consider caliberating the system when people are moving around.
For initial caliberation, people are required to be static.
3rd paper -
Title: RT-OPEX: Flexible Scheduling for Cloud-RAN Processing
Speaker: Kassem Fawaz
With the Cloud-Radio Access Network (C-RAN), becoming the go-to architecture for deploying cellular networks in recent times, the paper discusses the challenges involved in designing baseband processing on these platforms. Since the signal processing is now implemented in the cloud, it becoomes imperative that the baseband signals are processed with minimal latency so as to ensure deadlines of processing wireless frames are met., e.g., 3ms to transport, decode and respond to an LTE uplink frame.
The authors through their in-depth analysis show that commonly used (e.g., partitioned) scheduling techniques for wireless frame processing are inefficient as they either over-provision resources or suffer from deadline misses.This inefficiency stems from the large variations in processing times due to fluctuations in wireless traffic. The authors present a new framework called RTOPEX, that leverages these variations and proposes a flexible approach for scheduling. Evaluation of RT-OPEX is done on a commodity GPP platform using realistic cellular workload traces. Results show that RT-OPEX achieves an order-of-magnitude improvement over existing C-RAN schedulers in meeting frame processing deadlines.
a) What factors cause the variation of processing time?
channel time and mcs used affects the processing time.
b) Is the system sclable with increase in the number of UEs and number of enodB's?
haven't verified the scalability, but the author doesn't expect the performance to drop when scaled.
Title of the paper - Enabling Automatic Protocol Behavior Analysis for Android Applications
speaker - Jeongmin Kim
Understanding app behaviour is crucial for network operations. However, this requires application layer protocol analysis which makes the task challenging. This paper presents Extractocol, the first system to offer an automatic and comprehensive analysis of application protocol behaviors for Android applications. Extractocol uses the android app binary as input and accurately reconstructs the HTTP request-response pair transactions.
Since the speaker was unable to understand any of the questions asked, the eventual questions/discussions was taken offline.
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