30 research outputs found
How do Wireless Chains Behave? The Impact of MAC Interactions
In a Multi-hop Wireless Networks (MHWN), packets are routed between source
and destination using a chain of intermediate nodes; chains are a fundamental
communication structure in MHWNs whose behavior must be understood to enable
building effective protocols. The behavior of chains is determined by a number
of complex and interdependent processes that arise as the sources of different
chain hops compete to transmit their packets on the shared medium. In this
paper, we show that MAC level interactions play the primary role in determining
the behavior of chains. We evaluate the types of chains that occur based on the
MAC interactions between different links using realistic propagation and packet
forwarding models. We discover that the presence of destructive interactions,
due to different forms of hidden terminals, does not impact the throughput of
an isolated chain significantly. However, due to the increased number of
retransmissions required, the amount of bandwidth consumed is significantly
higher in chains exhibiting destructive interactions, substantially influencing
the overall network performance. These results are validated by testbed
experiments. We finally study how different types of chains interfere with each
other and discover that well behaved chains in terms of self-interference are
more resilient to interference from other chains
OSCAR: A Collaborative Bandwidth Aggregation System
The exponential increase in mobile data demand, coupled with growing user
expectation to be connected in all places at all times, have introduced novel
challenges for researchers to address. Fortunately, the wide spread deployment
of various network technologies and the increased adoption of multi-interface
enabled devices have enabled researchers to develop solutions for those
challenges. Such solutions aim to exploit available interfaces on such devices
in both solitary and collaborative forms. These solutions, however, have faced
a steep deployment barrier.
In this paper, we present OSCAR, a multi-objective, incentive-based,
collaborative, and deployable bandwidth aggregation system. We present the
OSCAR architecture that does not introduce any intermediate hardware nor
require changes to current applications or legacy servers. The OSCAR
architecture is designed to automatically estimate the system's context,
dynamically schedule various connections and/or packets to different
interfaces, be backwards compatible with the current Internet architecture, and
provide the user with incentives for collaboration. We also formulate the OSCAR
scheduler as a multi-objective, multi-modal scheduler that maximizes system
throughput while minimizing energy consumption or financial cost. We evaluate
OSCAR via implementation on Linux, as well as via simulation, and compare our
results to the current optimal achievable throughput, cost, and energy
consumption. Our evaluation shows that, in the throughput maximization mode, we
provide up to 150% enhancement in throughput compared to current operating
systems, without any changes to legacy servers. Moreover, this performance gain
further increases with the availability of connection resume-supporting, or
OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput
Inferring Room Semantics Using Acoustic Monitoring
Having knowledge of the environmental context of the user i.e. the knowledge
of the users' indoor location and the semantics of their environment, can
facilitate the development of many of location-aware applications. In this
paper, we propose an acoustic monitoring technique that infers semantic
knowledge about an indoor space \emph{over time,} using audio recordings from
it. Our technique uses the impulse response of these spaces as well as the
ambient sounds produced in them in order to determine a semantic label for
them. As we process more recordings, we update our \emph{confidence} in the
assigned label. We evaluate our technique on a dataset of single-speaker human
speech recordings obtained in different types of rooms at three university
buildings. In our evaluation, the confidence\emph{ }for the true label
generally outstripped the confidence for all other labels and in some cases
converged to 100\% with less than 30 samples.Comment: 2017 IEEE International Workshop on Machine Learning for Signal
Processing, Sept.\ 25--28, 2017, Tokyo, Japa
Unconventional TV Detection using Mobile Devices
Recent studies show that the TV viewing experience is changing giving the
rise of trends like "multi-screen viewing" and "connected viewers". These
trends describe TV viewers that use mobile devices (e.g. tablets and smart
phones) while watching TV. In this paper, we exploit the context information
available from the ubiquitous mobile devices to detect the presence of TVs and
track the media being viewed. Our approach leverages the array of sensors
available in modern mobile devices, e.g. cameras and microphones, to detect the
location of TV sets, their state (ON or OFF), and the channels they are
currently tuned to. We present the feasibility of the proposed sensing
technique using our implementation on Android phones with different realistic
scenarios. Our results show that in a controlled environment a detection
accuracy of 0.978 F-measure could be achieved.Comment: 4 pages, 14 figure
A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems
Β© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.This work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant 7-684-1-127