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Overview
My research interests cover several aspects of wireless networking and mobile computing
systems. I am particularly interested in smart wireless systems, mobile & edge computing,
software-defined networks, network security and privacy, Internet-of-things & smart city systems, vehicular networks,
intelligent transportation systems, and location determination systems.
My interest in wireless networking and mobile computing started during my PhD years motivated by the observation that various properties of wireless networks, such as mobility, frequent disconnections and varying channel conditions, make designing efficient protocols for such networks a challenging task. Therefore, enhancing the performance of wireless networks requires alleviating the effect of the physical layer characteristics (e.g., channel noise) and developing cross layer mechanisms to exploit the those characteristics in favor of enhancing network performance. In my dissertation work, I focused on the impact of cannel noise, physical layer capture effect, and the use of the directional antenna on the design of reliable and efficient routing and MAC protocols taking into account cross layer interaction between both layers as well as the physical layer. Active Projects
Completed Projects
Funding
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Research |
mobile traffic data over wireless links (i.e., Wi-Fi and cellular
links) is exploding. A recent Cisco report predicts that by 2019,
enough mobile devices will exist to create more than 24 exabytes
(24,000,000 terabytes) of traffic per month.
To cope with the explosion of mobile devices coupled with a growing
proliferation of cloud or edge-based applications, it is now necessary
to have greater visibility and control over the traffic generated from
the client devices in order to deliver optimal performance and a high
Quality of Experience (QoE) to a variety of users and applications.
With the recent advancement in Software Defined Network (SDN), we
believe that an SDN-like paradigm needs to be pushed to wireless-edges
and mobile clients (i.e., network edge as shown) to provide optimal
network performance between the cloud and wirelessly connected clients.
In this project, we aim to design, develop and evaluate SMILE - SMart
and Intelligent wireLess Edge framework that supports SDN-like paradigm
on user's smart devices and network wireless-edges. SMILE enables
network wireless-edges to become more active and to host several
services (including partial cloud services) to enhance users quality of
experience.
over-the-top
video today. Unfortunately, HAS comes with several drawbacks,
especially in mobile environments. HAS clients (players) are shown to
exhibit instability, stalls and poor quality when they compete over the
bottleneck link. In this project we develop FlexStream, a framework
that leverages: (i) a centralized or edge manager component in the
network that specifies a policy controlling resource allocation (e.g.,
bandwidth), and (ii) a distributed SDN component, which implements that
policy via Open vSwitch (OVS), essentially
offloading the fine-grained functionality to the end device. We refer
to these SDN components on mobile end devices as extreme SDN
applications and is the major technology behind smart computing domains
like smart homes,
connected health, connected cars, automated enterprise workflows, Smart
Cities and Smart grid. Ericsson predicts the number of connected IoT
devices to be around 18 billion by 2022. This significant growth and
penetration of smart and IoT devices come along with a tremendous
increase in the
number of smart and IoT applications. These various applications, which
support various domains and services,
generate and access different data patterns such as periodic,
event-based, realtime and continuous data. Consequently, these
different applications result in diverse traffic characteristics that
require different performance levels of reliability, loss, and latency.
To cope with this various traffic characteristics and requirements, it
is now necessary to have greater visibility and control over the
traffic generated from smart and IoT devices in order to guarantee an
optimized performance of smart and IoT applications as well as high
quality of experience to users. In this research, we design and develop
an open-source, flexible,
and programmable networked edge device that collates and mediates
access to our sensitive and personal data,
under the data subjects control as well as to cope with various
characteristics and requirements of smart and IoT applications that
access this data in order to provide better performance and quality of
experience to users.
efforts. However, these designed protocols, typically, are non-optimum
with limited flexibility under several network scenarios and
conditions. Moreover, due to evolving network technologies as well as
increasing demands of modern applications, ”general-purpose” protocol
stacks are not always adequate and need to be replaced by application
tailored protocols. Therefore, replacing this inefficient human-based
protocol designing process by a novel paradigm that enables rapid
design of efficient, flexible, and high performance protocols that
intelligently adapt to different device characteristics, application
requirements, user objectives, and network conditions is highly
desired. In this project, DeepMAC, we explore the first basic steps
toward our vision of replacing the human driven network communication
design by machine using ML techniques. This vision considers an
intelligent system that automates the design of on-line adaptive
protocols only by interacting with and learning from the environment,
without having any prior knowledge. In this envisioned framework,
network protocol stack is decomposed into core functionalities (e.g.,
switching, routing, congestion control, reliable connection, Backoff,
etc.) in which the intelligent agent designs an efficient protocol by
selecting the optimum set of functionalities in response to device
characteristics, application requirements, user objectives, and network
conditions.
Researchers
seized the moment with complex algorithms and data models to process
the data
over the cloud and extract as much information as possible. However,
the pace and amount of data generation, in addition to, networking
protocols transmitting data to
cloud servers failed short in touching more than 20% of what was
generated on the edge of the network. On the other hand, smart devices
carry a large set of resources,
e.g., CPU, memory, and camera, that sit idle most of the time. Studies
showed that for plenty of the time resources are either idle, e.g.,
sleeping and eating, or underutilized,
e.g. inertial sensors during phone calls. These ndings articulate a
problem in processing large data sets, while having idle resources in
the close proximity. In this project, we flip the concept of cloud
computing, instead of sending massive amounts of data for processing
over the cloud, we distribute lightweight applications
to process data on users' smart devices. We develop LAMEN, a three-tier
framework to orchestrate anonymous devices in the proximity and prepare
them for hosting complex services currently performed over the cloud.
We envision this approach to enhance the network's bandwidth, grant
access to larger datasets, provide low latency responses,
and more importantly involve up-to-date user's contextual information
in processing.
advantage
of the desirable attributes and features it possesses in comparison to
other wireless technologies. Bluetooth devices are going to become a
major player in the much-hyped Internet of Things (IoT) market. The
objectives of this project are to design, develop, and disseminate a
flexible and extensible Bluetooth Open-Source Stack (BOSS) platform
that will enable new research opportunities for the wireless and mobile
computing community. The platform will enable development and
evaluation of schemes, services, and applications across all layers of
the Bluetooth stack, through the creation of a community-maintained,
open-access repository. More specifically, BOSS targets providing an
open source implementing for the Bluetooth protocol firmware shown in
the yellow section in the figure based on Bluetooth Low Energy (BLE)
specifications Version 4.0. 
that
can be exploited to solve a number of problems in health care delivery.
In this paper we use accelerometer, gyroscope, and compass sensors to
solve a location tracking problem common to many emergency departments.
An emergency department is not friendly to be visually surveyed, layout
consists of many isolated islands, and workstation layout is not
standardized. An automated tool to create spaghetti diagrams of
movements of personnel in a non-intrusive way is the problem we are
reporting in this paper. A preliminary prototype shows very encouraging
results of producing paths. We also identify challenges and our
approach to meet them. 
However, the Wi-Fi network still has several inefficiencies in terms of
high energy consumption, unfairness between co-located nodes, and
bandwidth poor utilization. In this project we like to address these
issues of the Wi-Fi network by integrating the mic/speaker of the smart
phones as a parallel communication channel. Our idea is to propose a
novel framework of communication using mic/speaker in order to develop
a more efficient Wi-Fi network communication for smart devices. The
non-interferential nature with Wi-Fi network and low power consumption
is the biggest advantage of using audio communication channel in
parallel with WiFi. On the other hand, slow propagation and low data
rate of the acoustic channel are some biggest challenges we are
addressing in order to implement the Audio-WiFi framework.
concept that is getting a rising attention in the last decade, several
smart devices-based low-cost services have been developed addressing
ITS challenges. Therefore, one of my research directions is on how to
utilize wireless technologies and mobile computing in developing smart
systems and services for traffic transportation. One of these research
projects is BlueSys - a novel distributed Bluetooth-based system to
enhance safety and driving experience in metropolitan areas. BlueSys is
a cost-effective, low maintenance and efficient sensing platform that
utilizes the Bluetooth devices existing nowadays in vehicles (i.e.,
built-in Bluetooth, on-board mobile phones, hands-free devices) to
collect traffic data such as the actual number of vehicles, their
speeds, positions, queue lengths, lane blockages, etc. at the
signalized intersections. 
Conventional techniques suffer from different challenges, like the
high installation and maintenance cost, connectivity and communication
problems, and the limited set of data. The recent massive spread of
smartphones among drivers
encouraged the ITS community to use them to solve ITS challenges. In
this project, we develop SenSys - a smartphone framework that collects
and processes traffic data and then analyzes and extracts vehicle
dynamics and vehicle activities which can be used by developers and
researchers to create their navigation, communication,
and safety ITS applications. SenSys framework fuses and filters
smartphone's sensors readings which result in enhancing the accuracy of
tracking and analyzing
various vehicle dynamics such as vehicle's stops, lane changes, turn
detection, and accurate vehicle speed calculation that, in turn, will
enable development of new ITS
applications and services.
