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Eyuphan Bulut, Ph.D. - VCU College of Engineering. Richmond, VA, US

Eyuphan Bulut, Ph.D.

Associate Professor | VCU College of Engineering


Professor Bulut's research interests include wireless networks and mobile computing, cyber-physical systems, and social networks





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I am an Associate 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, cyber security, Internet of Things (IoT), wireless sensing, network data mining and learning, and applied machine learning.

Industry Expertise (6)


Computer Networking

Computer/Network Security

Computer Software



Areas of Expertise (5)

Mobile Computing: Wireless Networks Cyber-physical Systems Mobile Social Networks Wireless Charging

Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensing

Mobile (3G/4G) Systems: Mobile Packet Core Cloud Mobile Data Offloading

Security: Cyber Security Network Security Privacy and Trust

Applied Machine Learning with Sensor Data and Wireless Signals

Accomplishments (7)

Percom 2020 - Best Demo Award (professional)


Our paper (http://www.people.vcu.edu/~ebulut/percom20-demo.pdf) is awarded best demo award in Percom 2020..

VCU Teaching and Publication Awards (professional)


VCU Computer Science Departmental Teaching and Publication Awards, 2017-2020.

Air Force Research Lab (AFRL), Summer Faculty Fellowship, 2019. (professional)

Attended Summer Program in the summer of 2019.

Ranked with Exceptional Performance (professional)


Cisco Systems

Spotlight Paper (professional)


TPDS Magazine

IEEE Student Travel Grant (professional)



Gold and Bronze Medals (professional)


In Nationwide Math Olympiad, 2008-2010.

Education (3)

Rensselaer Polytechnic Institute: Ph.D., Computer Science 2011

Bilkent University: M.S., Computer Engineering 2007

Bilkent University: B.S., Computer Engineering 2005

Research Grants (6)

Daily Activity Tracking and Monitoring of Older Adults with WiFi Sensing

NIH $150,000


Maintaining independence in daily activities and mobility is critical for healthy aging. The loss of independence in older age marks the transition from health to disability. Older adults who lose the ability to move or care for themselves are at a high risk of adverse health outcomes, such as falls, and a decreased quality of life. Aging in place poses challenges for low-income older adults who often contend with multiple chronic conditions, disabilities, limited access to healthcare, and restricted social capital. Therefore, it is essential to routinely monitor the daily activities and mobility of these individuals to detect early declines before clinical symptoms emerge. Traditional activity and mobility assessment tools are primarily self-reported, subjective, and episodic. These assessments are prone to recall bias, especially among older adults with memory impairments. Additionally, they do not capture variability over time, making it challenging to track a patient’s decline in function. Recently, digital health technologies have been proposed to obtain objective, high-frequency, and remote monitoring. The existing systems use camera, sound, infrared sensors or wearables. While being accurate, they can cause a severe invasion of privacy and data confidentiality issues. Moreover, all these approaches require deployment of new dedicated sensors and devices, which bring significant additional cost. Contrary to these approaches, we proposed a WiFi sensing based system that offers a very low-cost solution as WiFi signals are widely available thanks to ubiquitous WiFi devices. WiFi sensing is also not invasive compared to wearable sensors; it can perform regardless of lighting conditions unlike camera-based sensing, and can penetrate through walls. We have been funded both from internal grants (in collaboration with nursing) and external grants (NIH based PennAITech center) for different stages of this project. We have deployed our WiFi sensing solution in around 10 senior housing setting and collected a week long data (i.e., both WiFi CSI data and video captures) from each of them. We are currently analyzing this data to see how successfully WiFi sensing can detect the activities of older adults. Our initial results show that we can achieve over 90% accuracy in detecting several common daily activities without any wearable on the people. We are also currently seeking large grants (e.g., NSF SCH, NIH R01) using our preliminary studies and results.

Low-cost and Long-lasting Soil Moisture Sensing with WiFi signals and Energy Harvesting

Commonwealth Cyber Initiative (CCI) $100,000


A recent projection shows that the total global food demand is expected to increase by 35% to 56% by 2050. This requires the development of innovative solutions to ensure sufficient future food production as well as to use the available resources (e.g., water) efficiently. Understanding soil moisture in crop fields can help optimize beneficial use of agricultural water through the application of variable rate irrigation (VRI) technology. Water usage efficiency during VRI can be improved with a real-time and dynamic prescription map generation and adapting the actual crop water needs during irrigation application. This can be achieved through a dense deployment of soil moisture sensors and automatic collection and processing of their data in the VRI system. However, current soil moisture sensing technologies are not best-suited for such a dense deployment due to their high adoption and maintenance costs. While there exist some low-cost soil moisture sensors, they are mostly unreliable and do not provide commercial-grade accurate measurements. Moreover, such sensors do not last long once they deployed in the field. Thus, there is a need for low-cost, reliable and long-lasting new sensing solutions that can be deployed densely. To address this, building on our expertise on WiFi sensing on edge devices we have considered to use the our ESP32 microcontrollers and WiFi sensing solution to sense the soil moisture through the relation in signal propagation (CSI data) and the water amount in the soil. Our initial published results are promising for a scalable solution that will support VRI systems. This is a pilot study funded by CCI and we are collaborating with UVA on this.

iTREAT – Improved Treatment using Advanced Technologies

Commonwealth Health Review Board (CHRB) $200,000


Physical therapy (PT) exercises are critically important for the rehabilitation of patients with motor deficits. While rehabilitation exercises can be most effective when performed properly under the supervision of a physical therapist, it can be costly in terms of several aspects and may not be a viable option for all patients. At-home systems offer more accessible and less costly solutions to patients while also providing flexibility in scheduling prescribed exercises. However, current systems mostly depend on camera based solutions that have limitations (i.e., deployment cost, requiring patients to be in the sight of camera, potential privacy violations) or wearable solutions that are cumbersome and intrusive. To this end, we proposed to use our WiFi sensing solution for tracking the exercises prescribed to patients during their rehabilitation. This project is recently funded by Commonwealth Health Review Board (CHRB). So far, through our experiments, we have shown that we can successfully recognize different types of such as hand and finger movements, limb movements and movements performed with exercise equipment. Moreover, we showed that our system can recognize the person performing different activities and can identify when they are at rest or actively performing an exercise. There is currently a journal submission under review that includes not only the recognition of physical therapy exercises but also their segmentation in real time and counting the number of repetitions of each exercise performed by the patient. Future directions in this are is pretty open and we are currently working on deploying and testing our system with real patients.

CyberMobile: Secure Mobile (iOS) Development through Experiential Learning

Commonwealth Cyber Initiative (CCI) $100,000


This two-stage program will train 10 VCU and VUU students in the fundamentals of mobile iOS app programming and security-related practices in the classroom and through industry collaborators. Trained students will be highly sought by employers seeking iOS developers who can craft security features for apps during design and development. Students will form contacts and create networks during their collaborations with industry professionals.

NeTS: Small: Collaborative Research: Improving Spectrum Efficiency for Hyper-Dense IoT Networks

National Science Foundation $162,500


The emerging Internet of Things (IoT) technology will enable a whole new set of applications, imposing far reaching influence on many aspects of society, including management of health, transportation, agriculture, safety and emergency and disaster response. At the same time, the massive growth of IoT deployments poses several grand challenges to the wireless industry for their successful operation. This project aims to develop novel IoT architectures and algorithms to enable large-scale IoT deployments. In particular, through close collaboration of three academic institutions and industry, the proposed research will make advances by introducing holistic, cross-layer design framework and protocols for improving spectral/energy efficiency and latency of massive IoT networks. The research outcomes will support emerging standardization activities to enable IoT in next-generation wireless networks. This project aims at tackling the following challenges: 1) System-Centric Waveform Design for Massive IoT: The researchers will couple stochastic geometry techniques and ambiguity functions of carrier waveforms to perform system-wide capacity analysis, compare waveform candidates for deployment suitability, develop joint equalizer and waveform designs for massive IoT deployments, and propose new waveforms having a good spectral efficiency, interference resilience, and latency characteristics. 2) Graph-based Radio Resource Management: Judicious radio resource management is critical to the proper functioning of hyper-dense IoT networks. The project will exploit weighted partitioning and matching tools from graph theory to develop waveform-aware dynamic resource allocation schemes to suppress mutual interference among a diverse set of wireless IoT links and improve spectrum utilization efficiency in single-hop and multi-hop massive IoT deployments. 3) Core network Connection Efficiency: Rethinking the core network functions for connection of massive IoT is required as they are not designed considering the IoT traffic characteristics. The project will develop and rigorously study an aggregation based connectivity model to manage multiple IoT device traffic using the same resources (i.e., bearers). For efficient groupings of the IoT devices that will share the same connection, researchers will use efficient evolutionary clustering methods, while giving preference to the interference minimizing groups obtained in earlier thrusts.

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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.

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Courses (3)

CMSC 628 Mobile Networks

The course will assume undergraduate-level background in algorithms, programming (e.g., Java), calculus, and probability. Upon successful completion of this course, the student will be able to understand the major concepts about mobile networks; be familiar with various mobile network applications (e.g., ad hoc and sensor networks, mobile social networks, delay tolerant networks, vehicular networks and cellular networks); learn how to model mobile networks with stochastic processes and real datasets; be able to use different networking simulators; understand various routing algorithms and analyze their behavior. Topics covered: • Different mobile network applications (e.g., Ad hoc and sensor networks, Delay tolerant networks, Mobile Social Networks, Vehicular networks) and challenges • Device-to-Device communication technologies (e.g., Bluetooth, WiFi-Direct, LTE-direct) • Routing algorithms for content distribution and delivery • Mobility models • Mathematical tools to analyze and model mobile networks • Network simulators (ns-2, ONE etc.) • Data driven simulations and evaluation • Emerging Networks and Technologies (Internet of Things, Machine to Machine Networks, Connected cars, DSRC, Aerial networks)

CMSC 428 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. Topic covered: • 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 (7)

WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World Systems

IEEE Communications Surveys & Tutorials

Steven M. Hernandez, and Eyuphan Bulut


In this work, we evaluate the feasibility of deploying ubiquitous WiFi sensing systems at the edge and consider the applicability of existing techniques on constrained edge devices and what challenges still exist for deploying WiFi sensing devices outside of laboratory environments. Through an extensive survey of existing literature in the area of WiFi sensing, we discover common signal processing techniques and evaluate the applicability of these techniques for online edge systems. Based on these techniques, we develop a topology of components required for a low-cost WiFi sensing system and develop a low-cost WiFi sensing system using ESP32 IoT microcontroller edge devices. We perform numerous real world WiFi sensing experiments to thoroughly evaluate machine learning prediction accuracy by performing Tree-structured Parzen Estimator (TPE) hyperparameter optimization to independently identify optimal hyperparameters for each method. Additionally, we evaluate our system directly on-board the ESP32 with respect to computation time per method and overall sample throughput rate. Through this evaluation, we demonstrate how an edge WiFi sensing system enables online machine learning through the use of on-device inference and thus can be used for ubiquitous WiFi sensing system deployments.

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QoS-based Budget Constrained Stable Task Assignment in Mobile Crowdsensing

IEEE Transactions on Mobile Computing (TMC)

Fatih Yucel, Murat Yuksel and Eyuphan Bulut

2020 One of the key problems in mobile crowdsensing (MCS) systems is the assignment of tasks to users. Most of the existing work aim to maximize a predefined system utility (e.g., quality of service or sensing), however, users (i.e., task requesters and performers/workers) may value different parameters and hence find an assignment unsatisfying if it is produced disregarding these parameters that define their preferences. While several studies utilize incentive mechanisms to motivate user participation in different ways, they do not take individual user preferences into account either. To address this issue, we leverage Stable Matching Theory which can help obtain a satisfying matching between two groups of entities based on their preferences. However, the existing approaches to find stable matchings do not work in MCS systems due to the many-to-one nature of task assignments and the budget constraints of task requesters. Thus, we first define two different stability conditions for user happiness in MCS systems. Then, we propose three efficient stable task assignment algorithms and discuss their stability guarantees in four different MCS scenarios. Finally, we evaluate the performance of the proposed algorithms through extensive simulations using a real dataset, and show that they outperform the state-of-the-art solutions.

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Using perceived direction information for anchorless relative indoor localization

Journal of Network and Computer Applications (JNCA)

Steven Hernandez and Eyuphan Bulut

2020 Identifying the positions of mobile devices within indoor environments allows for the development of advanced context-based applications and general environmental awareness. Classic localization methods require GPS; an expensive, high power consuming and inaccurate solution for indoor scenarios. Relative positioning instead allows nodes to recognize location in relation to neighboring nodes without the requirement of GPS. To triangulate their own position however, indoor localization methods either use Received Signal Strength Indication (RSSI) retrieved from neighboring devices to determine distance or simple binary contact information denoting whether two nodes are in communication range of one another. RSSI however is plagued by many sources of noise, thus decreasing distance prediction accuracy as well as being unreliable for networks of heterogeneous devices. Further, using only binary contacts provides a limited information for localization. In our work, we first demonstrate the unreliable nature of RSSI in heterogeneous networks. We then demonstrate our intermediate solution between unreliable RSSI and oversimplified binary classifications by introducing Perceived Direction Information (PDI) composed of three states: approaching, retreating and invisible. Through real world experiments, we demonstrate that PDI can be predicted using a Dense Neural Network with more than 95% accuracy even on devices not used during training. We then describe an anchorless Monte Carlo Localization (MCL) algorithm which uses PDI to achieve higher accuracy and a reduction of communication over the state-of-the-art MCL based methods.

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Spatio-Temporal Non-intrusive Direct V2V Charge Sharing Coordination

IEEE Transactions on Vehicular Technology (TVT)

Eyuphan Bulut, Mithat Kisacikoglu, and Kemal Akkaya

2019 Direct Vehicle-to-vehicle (V2V) charge sharing system has the potential to provide more flexibility to electric vehicle (EV) charging without depending on the charging station infrastructure or building designated parking lots. It can also provide an opportunity to shift peak time utility load to off-peak times. However, the assignment between the EVs that demand energy and the EVs with surplus energy or existing charging stations is a challenging problem as it has to be performed in real-time considering their spatio-temporal distribution, availability and grid load. In this paper, we study this assignment problem specifically in a supplier non-intrusive scenario (without changing their mobility) and aim to understand the potential benefits of a direct V2V charge sharing system. To this end, we present two new algorithms to match the demander EVs to suppliers for charging. In the first one, the maximum system benefit is targeted under multiple system objectives with different priorities and considering the grid load. In the second one, individual EV priorities are taken into account and a stable matching process depending on predefined user preferences is provided. We conduct extensive simulations using real user commuting patterns and existing charging station locations in three different cities together with a newly developed probabilistic EV charging behavior model and EV mobility. Simulation results show that direct V2V charge sharing can reduce the energy consumption of EVs by 20-35% by providing a closer charging facility compared to charging station only case, offload grid charging power in peak times by 35-55%, and help sustain up to 2.2x EV charging requests without building new charging infrastructure, while causing a marginal increase (i.e., 3-9%) in the energy cycling of supplier EVs. The results also show that the proposed matching algorithms offer 80-90% more energy consumption reduction compared to the intrusive V2V charging at designated parking lots.

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Exploiting peer-to-peer wireless energy sharing for mobile charging relief

Elsevier Ad hoc Networks

Aashish Dhungana, Tomasz Arodz, and Eyuphan Bulut

2019 In this paper, we investigate the utilization of peer-to-peer wireless energy sharing to relieve the users from the burden of cord-based charging. The devices of users can make use of energy available from other users' devices based on their meeting patterns so that the battery level of their devices could be maintained within an acceptable level without the need of charging it through a cable frequently. We first use dynamic programming-based optimization to find out the minimum number of cord-based charging sessions to obtain the highest possible mobile charging relief through collaborative charge sharing among pairs of nearby user devices. Then, we map our problem to roommate matching problem and find out the best matching among users that will achieve the highest network-wide relief while satisfying all users with their assigned partners. With an extensive empirical analysis based on real device charging patterns and meeting patterns between mobile users, we evaluate the charging relief offered to users in various scenarios. The results show that users can get up to 13-17% relief from their charging burden using cooperative energy exchanges without changing their existing usage habits.

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Energy sharing based Content Delivery in Mobile Social Networks

IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

Aashish Dhungana and Eyuphan Bulut

2019 In mobile social networks, the mobility and connectivity of nodes are often non-deterministic. Source and destination nodes may not have a meeting opportunity and the content dissemination and delivery most of the time require the cooperation of nodes. However, this causes nodes spend energy, thus, they may be reluctant to participate in the dissemination process to conserve energy. One approach to motivate user participation is to transfer energy for their service so that their potential loss is compensated. However, this makes routing problem much challenging as the source node needs to decide not only the best relay nodes but also the amount of energy transfer to them. In this paper, we study this energy sharing based content delivery problem in mobile social networks. To this end, we assume that a node is willing to carry the content as long as the energy received for this delivery lasts, after which it drops the content (i.e., time-to-live). We utilize optimal stopping theory and dynamic programming to model the content delivery problem under this energy sharing paradigm between the nodes. The simulation results show that energy sharing based content delivery can potentially increase the routing performance under certain settings.

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Efficient and Privacy Preserving Supplier Matching for Electric Vehicle Charging

Elsevier Ad hoc Networks

Fatih Yucel, Kemal Akkaya and Eyuphan Bulut

2018 Electric Vehicle (EV) charging takes longer time and happens more frequently compared to refueling of fossil-based vehicles. This requires in-advance scheduling on charging stations depending on the route of the demander EVs for efficient resource allocation. However, such scheduling and frequent charging may leak sensitive information about the users which may expose their driving patterns, whereabouts, schedules, etc. The situation is compounded with the proliferation of EV chargers such as V2V charging where any two EVs can charge each other through a charging cable. In such cases, the matching of these EVs is typically done in a centralized manner which exposes private information to third parties which do the matching. To address this issue, in this paper, we propose an efficient and privacy-preserving distributed matching of demander EVs with charge suppliers (i.e., public/private stations, V2V chargers) using bichromatic mutual nearest neighbor (BMNN) assignments. To this end, we use partially homomorphic encryption-based BMNN computation through local communication (e.g., DSRC or LTE-direct) between users while hiding their locations. The proposed matching algorithm provides not only a satisfactory assignment for all parties but also achieves an efficient matching in dynamic environments where new demanders and suppliers show up and some leave. The simulation results indicate that the proposed matching of suppliers and demanders can be achieved in a distributed fashion within reasonable computation and convergence times while preserving privacy of users. Moreover, due to the nature of its design, it provides a more efficient matching process for dynamic environments compared to standard stable matching algorithm, reducing the average waiting time for users until matching.

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