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
  • SMILE – Towards Smarter Network Edges for Next Generation Networks
    • As the number of smart devices and their applications continue to growth, transmission of Core and edge Network 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.

  • FlexStream – Flexible Adaptive Video Streaming on End Devices using Extreme SDN
    • HTTP Adaptive Streaming (HAS) is the dominant approach to deliver FlexStream Overviewover-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

  • Secure and Flexible Personal Data Platform on the Edge
    • Internet of Things is becoming the key enabler for highly intelligent data richExtreme Data Hub 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.

  • DeepMAC – Towards A Deep Learning-Based Framework for Automated Design of Networking MAC Protocols
    • Networking protocols, practically, are designed through long-time and hard-work humanDeepMAC Architecture 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.

  • LAMEN - Leveraging Resources on Anonymous Mobile Edge Nodes
    • The intrusive nature of smart devices granted access to huge amounts of raw data. LAMEN OverviewResearchers 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.

Completed Projects
  • Bluetooth Open-Source Stack (BOSS) - A Flexible and Extensible Bluetooth Research Platform
    • Bluetooth technology continues to evolve and expand, taking Bluetooth Stackadvantage 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.

  • SmartSpaghetti - The Use of Smart Devices in Healthcare Lean Management
    • Mobile devices such as smart phones have a number of sensors Indoor Tracking for Lean Managementthat 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.

  • Acoustic-WiFi: Audio Channel Assisted Wi-Fi Network for Smart Devices
    • Wi-Fi is becoming widely popular network interface for data communication in smart devices.Acoustic-WiFi 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.

  • BlueSys - A Distributed Bluetooth-Based Framework for Intelligent Transportation Systems
    • Given that intelligent transportation systems (ITS) is a major critical aspect of smart citiesBlueSys Overview 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.

  • SenSys: A Smartphone-Based Framework for ITS Applications
    • Intelligent transportation systems (ITS) use different methods to collect and process traffic data.SenSys Framework 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.