I am an Assistant Professor in the CS department at the Virginia Commonwealth University. My responsibilities are teaching, research, service, and mentoring students. My research interests include mobile and pervasive computing, cyber-physical systems, social networks, wireless charging, data science, cyber security, privacy and trust, cloud, Internet of Things (IoT), crowd-sensing, network data mining and supervised learning,
Industry specialties: Mobile Packet Core (3G, LTE) gateways (GGSN, PGW, SGW, MME, SGSN, SAEGW, HA, IPSG), Femtocell gateways (HNB-GW, HeNB-GW), IMS, WiFi Offloading (ePDG, SaMOG, eWAG), Network protocols and models. Real traffic analysis and modeling. Cisco ASR 5000, ASR 5500, QvPC-SI, QvPC-DI.
Industry Expertise (4)
Areas of Expertise (4)
Ranked with Exceptional Performance (professional)
Spotlight Paper (professional)
IEEE Student Travel Grant (professional)
Gold and Bronze Medals (professional)
In Nationwide Math Olympiad, 2008-2010.
Rensselaer Polytechnic Institute: Ph.D., Computer Science 2011
Bilkent University: M.S., Computer Engineering 2007
Bilkent University: B.S., Computer Engineering 2005
Research Grants (1)
US Ignite: Collaborative Research: Focus Area 1: Rapid and Resilient Critical Data Sourcing for Public Safety and Emergency Response
National Science Foundation $380,000
In public safety and emergency response, the key for a successful recovery is on-time and reliable communication between the people reporting the incident and the emergency management authorities. Such capability should further be coupled with rapid collection of eyewitness data for successful investigation of criminals or root causes of the incident. Recently, crowd sourcing applications have proven to be promising tools to gather information with tremendous participation from the crowd. In emergency scenarios, however, such crowd sourcing solutions should be robust and resilient as the network could be congested or the underlying cellular infrastructure is damaged or temporarily lost. Moreover, an agile analysis of the massive amount of data collected should be supported with necessary equipment so that time-sensitive feedback could be provided and shared between different agencies. This project addresses these challenges by introducing a novel framework that integrates various technologies and tools for modeling and operation of large-scale crowd sourcing-based emergency response systems.
The project enhances the current systems by integrating a cloud-based rapid processing of collected data and augmenting the system by device-to-device (D2D) communications and network slicing. The project's integrated research and education plan investigates (i) large-scale critical data collection via a mobile app and management process to be used in the investigation of an emergency incident, (ii) near-real-time processing of the gathered heterogeneous data in a cloud computing environment for critical information extraction such as faces of people in the videos and photos, (iii) adoption of D2D-based communication as a complementary component to improve system resilience in case of congestion's and failures in network infrastructure, and (iv) utilization of Global Environment for Network Innovations (GENI) network slices as dedicated bandwidth for time-sensitive communication in emergency response as well as to enhance wide area resilience of the system. The project paves the way towards emergency preparedness which is a national priority; and supports progress toward smart and connected communities. The anticipated enhancements expedite the response to emergency cases, save people's lives and reduce public safety operation costs.
CMSC 491 Mobile Programming - iOS
This course covers the fundamentals of Swift and XCode for programming and design of iOS applications. Students develop different types of iOS applications (game, web based etc.) completely functional throughout the course. They also test the developed applications on simulator and on actual device.
• Introduction to Swift, Xcode and iOS Frameworks
• Swift basics: variables, operations, loops, if statements, functions, optionals
• Object oriented programming, polymorphism, inheritance
• Auto Layout, Stack view, Scroll view, Interface development
• ViewControllers and Segues
• Tableview, Collectionview
• Model, View, Controller (MVC)
• Touches and Gestures
• Data Persistence
• Animations and SpriteKit
• MapKit, Core Location, CoreMotion
• Web services, networking, multipeer connectivity
• Submission to Apple Store
CMSC 401 Algorithm Analysis with Advanced Data Structures
This course covers the basic principles of algorithm design and analysis. Main topics covered:
• Foundations of algorithm and complexity analysis
• Divide and conquer approach
• Data Structures
• Sorting and Order Statistics
• Advanced design and analysis techniques
• Graph algorithms
• Dynamic programming
Selected Articles (6)
Fueled by users’ demand, mobile devices are becoming more complex, increasing their power requirements. However, the battery technology is advancing slower which results in shrinking battery lives (i.e., currently around a day). Hence, users are required to charge their devices frequently, mostly by tethering them to a cord. Anxiety of losing power in the middle of a critical task during which users may not have access to charging facilities have even caused opportunistic charging behavior with the aim of keeping the devices with as much power as possible. Recently power sharing technologies and gadgets have emerged enabling harvesting power from other mobile devices in the user’s vicinity. In this paper, we discuss the energy sharing in mobile social networks whose nodes are human-carried mobile devices operating on batteries. We investigate the limits of power sharing among mobile devices by analyzing their current charging patterns
and the social (i.e., close-proximity) interactions between the people carrying these devices. Moreover, we propose an energy sharing model by pairing the nodes in a mobile network into power buddies. Simulation results show that a typical application scenario of energy sharing among power buddies provides a remarkable amount of saving in the utilization of power available in the entire network
Growth models have been proposed for constructing the scale-free overlay topology to improve the performance
of unstructured peer-to-peer (P2P) networks. However, previous growth models are able to maintain the limited scale-free topology when nodes only join but do not leave the network; the case of nodes leaving the network while preserving a precise scaling parameter is not included in the solution. Thus, the full dynamic of node participation, inherent in P2P networks, is not considered in these models. In order to handle both nodes joining and leaving the network, we propose a robust growth model E-SRA, which is capable of producing the perfect limited scale-free overlay topology with user-defined scaling parameter and hard cut-off. Scalability of our approach is ensured since no global information is required to add or remove a node. E-SRA is also tolerant to individual node failure caused by errors or attacks. Simulations have shown that E-SRA outperforms other growth models by producing topologies
with high adherence to the desired scale-free property. Search algorithms, including flooding and normalized flooding, achieve higher efficiency over the topologies produced by E-SRA.
Routing in delay tolerant networks is a challenging problem due to the intermittent connectivity between nodes resulting in the frequent absence of end-to-end path for any source-destination pair at any given time. Recently, this problem has attracted a great deal of interest and several approaches have been proposed. Since Mobile Social Networks (MSNs) are increasingly popular type of Delay Tolerant Networks (DTNs), making accurate analysis of social network properties of these networks is essential for designing efficient routing protocols. In this paper, we introduce a new metric that detects the quality of friendships between nodes accurately. Utilizing this metric, we define the community of each node as the set of nodes having close friendship relations with this node either directly or indirectly. We also present Friendship-Based Routing in which periodically differentiated friendship relations are used in forwarding of messages. Extensive simulations on both real and synthetic traces show that the introduced algorithm is more efficient than the existing algorithms.
Overlay network topology together with peer/data organization and search algorithm are the crucial components of unstructured peer-to-peer (P2P) networks as they directly affect the efficiency of search on such networks. Scale-free (powerlaw) overlay network topologies are among structures that offer high performance for these networks. A key problem for these topologies is the existence of hubs, nodes with high connectivity. Yet, the peers in a typical unstructured P2P network may not be willing or able to cope with such high connectivity and its associated load. Therefore, some hard cutoffs are often imposed on the number of edges that each peer can have, restricting feasible overlays to limited or truncated scale-free networks. In this paper, we analyze the growth of such limited scale-free networks and propose two different algorithms for constructing perfect scale-free overlay network topologies at each instance of such growth. Our algorithms allow the user to define the desired scalefree exponent (gamma). They also induce low communication overhead when network grows from one size to another. Using extensive simulations, we demonstrate that these algorithms indeed generate perfect scale free networks (at each step of network growth) that provide better search efficiency in various search algorithms than the networks generated by the existing solutions.
Recently, there has been a tremendous increase
in demand for mobile data driven by the wide-spread usage of smartphone like devices. Besides the efforts of studies that propose ways for operators to handle this huge growth, there is also an emerging research area of analyzing this new type of user data. In this paper, we perform an analysis on mobile user data to understand general aggregated user behavior based on several
parameters. We also investigate the reasons for the user behavior deviating from its general trend. Finally, we discuss several ways of utilizing the results of the analysis in this study in solving current problems operators face.
Recently, there has been a tremendous increase in mobile data usage with the wide-spread proliferation of smartphone like devices. However, this increased demand from users has caused severe traffic overloading in cellular networks. Offloading the traffic through several other devices (femtocells, WiFi access points) have been considered to be immediate remedy for such a problem. Thus, in this paper, we study the deployment of WiFi access points (AP) in a metropolitan area for efficient offloading of mobile data traffic. We analyze a large scale real user mobility traces and propose a deployment algorithm based on the density of user data request frequency. In simulations, we present offloading ratio that our algorithm can accomplish with different number of APs. The results demonstrate that our algorithm can achieve close to optimal offloading ratio that is higher than offloading ratios that existing algorithms can achieve with the same number of APs.