April 18: MANET Unicast Routing reading report due April 21 (before lecture). 

April 7: MAC II reading reports due on April 14 (before lecture).

April 3: New project ideas posted.

April 3: Repository for lecture recordings created and posted; Lecture 2 recordings posted

April 3: Tutorial schedule posted

April 3: Office hours posted

April 2: MAC papers posted

General Information:

Instructor: Katia Obraczka (katia "at"

Lab: Internbetwork Research Group (i-NRG)  (

Office hours: Tue and Thu after class and by appointment


When: T Th 9:50-11:25am

Where: Kresge 319

Zoom link [Note that in person attendance is assumed but a Zoom room has been set up in case in person attendance is not possible]:

Lecture recordings:

Course Overview:

This class covers various topics relevant to wireless networking and mobile computing. It focuses on communication protocols for wireless and mobile networks from medium-access control to end-to-end transport and applications. The course requires extensive reading of research papers and in-class presentations, participation, and discussion. Programming proficiency is assumed since students will be required to complete a major class project.

Pre-requisites: CSE 250A or equivalent.

Topics covered:

. Medium access control

. Routing (unicast and multicast)

. Wireless internetworking (Mobile IP)

. End-to-end protocols

. Other:

. Networking paradigms: disruption-tolerant networking, software-defined networking

. Edge computing/networking

. Mobility characterization and modeling

. Power and topology management

. Security

. Internet of Things and Sensor Networks

Resources:   No textbook is required since most of the required readings will be from research papers. The book "Ad Hoc Wireless Networks: Architectures and Protocols" by C. Siva Ram Murthy and B.S. Manoj can be used as a reference. Students can check with the instructor on additional reference sources. All material for the class will be distributed and managed online using the class Web page.


. In-class presentation: 20%

. Reading reports: 10%

. Project: 40%

. Exam: 30%

IMPORTANT NOTE: Grades of C and below will be assigned to students who do not perform satisfactorily. Students should not assume that a passing grade will be assigned simply because this is a graduate class.

Student Responsibilities:

Students enrolled in this class are agreeing to the following:

ACADEMIC INTEGRITY: All work turned in as reports, project, and exam MUST be individual. If any work claimed by a student to be their own is found to not to be their own and/or is shared with others, that will be considered a violation of academic integrity and will NOT BE TOLERATED. Academic integrity violations may result in automatically failing the class. For more information on UCSC's academic integrity policies, visit are encouraged to discuss with the instructor any questions they may have regarding academic integrity.

. Students are responsible for reading the papers that will be covered in a specific lecture BEFORE the lecture. All papers must be read in detail even though not all details will be covered in class. A reading report on the papers read needs to be submitted before the class meeting in which the papers will be discussed. More information on reading reports are provided below.

. Students are also responsible for checking the class Web page frequently for updates, schedule changes, etc.

. The course pre-requisite is CMPE 252A or equivalent. Prospective students can talk to the instructor if they do not have the required background. If a student has not taken CMPE 252A (or equivalent) and still wants to take the class, it is the student's responsibility to acquire the corresponding background material.

. As mentioned in the description of the course, students must be proficient (C, C++, Java, Python) programmers as a programming project will account for a considerable portion of the grade.

Class attendance is mandatory. Because this is a graduate class, students are expected to participate actively in class, and that's hard to do if they do not attend class regularly. Attendance will not be recorded, but students cannot miss more than two classes. If  a student needs to miss a class, they must let the instructor know (in advance if possible).

. Much of the course material, including reading assignments and lecture notes, will be posted on the class Web page. However, students are responsible for all material covered in class, whether or not it appeared on the class Web site.

Project: Students need to submit a class project on a topic related to wireless networking. The project will involve implementation or simulation. Some project ideas are listed below. Students are required to submit a project proposal (by the end of the 3rd week of class) containing the following: (1) Project title, (2) Motivation and goals, (3) Brief description of proposed approach, (4) Basic design, (5) Evaluation and testing methodology, (6) Demo/results.

Project proposals will not be graded but are mandatory.

Project deliveralbles: As part of the project, students need to submit a written report, source code, and a presentation. Students will present their projects at the end of the quarter.

Reading Reports:

Every student is expected to write a one-page report that contains a brief summary of the papers to be read for each class. Reading reports are due before the start of the class when the papers will be covered. Reading reports are to be submitted as an e-mail attachment (plain text or pdf). Use “CSE 257 Spring22 Reading Report #” as the subject line.

Each project report should contain: a brief summary of each paper which should include short answers to the following questions: (1) what is the problem the authors are trying to solve? (2) why is the problem interesting, relevant, and/or important? (3) what other approaches or solutions existed at the time that this work was done? (4) how did the proposed approach contribute to the state-of-the-art, i.e., why existing approaches were not adequate? (5) what is the proposed approach and how does it compare to earlier approaches, in other words, what are the contributions of the proposed approach? (6) what are the main strengths and weaknesses of the paper/proposed approach? After summarizing all papers to be covered in lecture, the report should also include a paragraph with brief compare and contrast commentary of the papers read. It should also include at least 2 questions to be asked during lecture as a way to foster discussion. Make sure to have a copy of your reports handy during class to help guide class discussion and your participation. 

Student Presentations:

Students will present on a topic of their choice as they relate to wireless and mobile networking. Possible topics include: security in wireless networks, networking architectures and paradigms (DTN, SDN, edge computing. hybrid networks, IoT, etc). Student presenters need to pick a topic of their interest among the ones suggested. Students can also propose a new topic (need instructor's approval). Once the topic is selected, students will choose 3 papers on the topic to be covered. Selected papers need approval from instructor.

Student presentations must provide adequate overview of the topic through the papers selected. Presentations should avoid describing the papers exactly and instead provide an overview of the main contributions, approach, methods described in the papers as well as a the presenter's own observations/perspective. Presentations should provide insight and critical perspective on the state-of-the-art related to the topic being presented. Class discussion should be encouraged. On the day of their presentation, the presenter does not need to submit a reading report. They should send their presentation ahead of time so it can be posted on the class Web page. The presentation should include discussion points/questions to encourage in-class discussion/participation.

Schedule (Tentative):

 Date  Topic Lecture Notes
March 27 Class overview Lecture 1
March 29 Introduction: Motivation, background, basic concepts, terminology Lecture 2
April 5 Introduction: Motivation, background, basic concepts, terminology Lecture 3
April 7 Medium access control & OMNET++ Tutorial Lecture 4
April 12 Medium access control & ns-3 tutorial Lecture 5
April 14 Medium access control & Cooja/Contiki tutorial Lecture 6
April 19 Medium access control Lecture 7
April 21 Routing: Unicats routing Lecture 8
April 26 Routing: Unicast routing Lecture 9
April 28 Routing: Multicast routing Lecture 10
May 3 Wireless internetworking Lecture 11
May 5 End-to-end protocols Lecture 12
May 10 End-to-end protocols Lecture 13
May 12 End-to-end protocols Lecture 14
May 17 Exam  
May 19 Edge Intelligence (Hari) [Lecture slides posted on the shared drive]
May 24 Security in Wireless Networks (Sloan) Sloan's presentation
May 26 IoT (Theo and Shayan)  
May 31 DTN (Charitha) & Security in Wireless Networks (Tamir)  
June 2 Project presentations  
June 7 Project deliverables due (11:59pm)  

Reading List:

Timeless readings:

. Saltzer et al., End-to-end Arguments in System Design

. Lampson, Hints for Computer System Design

Introduction (March 29)

. L. Kleinrock, "Nomadicity: Anytime, Anywhere In A Disconnected World", Invited paper, Mobile Networks and Applications, Vol. 1, No. 4, January 1996, pp. 351-357. 

. L. Kleinrock, "An Internet Vision: The Invisible Global Infrastructure", Ad Hoc Networks Journal, Vol. 1, No. 1, pp. 3-11, July 2003.

. M Weiser, "The Computer for the 21st Century", 1991. 

. M. Weiser, "Some Computer Science Problems in Ubiquitous Computing", Communications of the ACM, July 1993. 

 Medium Access Control I - Contention-Based MAC (April 07 and 12)

B. P. Crow and I. Widjaja and L. G. Kim and P. T. Sakai, "IEEE 802.11 Wireless Local Area Networks", 1997. IEEE Communications Magazine, 35(9):116-126. 

. Vaduvur Bharghavan, Alan Demers, Scott Shenker, Lixia Zhang, "MACAW: A Media Access Protocol for Wireless for Wireless LANs", ACM Sigcomm 94. 

. J. J. Garcia-Luna-Aceves and C. L. Fullmer, "Floor Acquisition Multiple Access in Single-Channel Wireless Networks," ACM MONET Journal, Special Issue on Ad Hoc Networks, Vol. 4, 1999, pp. 157-174. 

Medium Access Control II - Scheduled-Access MAC (April 19)

. Venkatesh Rajendran, Katia Obraczka, J.J. Garcia-Luna-Aceves. "DYNAMMA: A DYNAmic Multi-channel Medium Access Framework for Wireless Ad Hoc Networks", Proceedings of the 4th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS). Oct 2007. Nominated for the best paper award. 

. V. Rajendran, Katia Obraczka, and J.J. Garcia-Luna-Aceves, "Energy-Efficient, Collision-Free Medium Access Control for Wireless Sensor Networks", ACM/Kluwer Wireless Networks (WINET), 2006.  

. Djukic, P. and Mohapatra, P., "Soft-TDMAC: A Software TDMA-Based MAC over Commodity 802.11 Hardware", Proceedings of the INFOCOM 2009, 2009.

MANET Unicast Routing I (April 21) 

. Dynamic source routing in ad hoc wireless networks, David B. Johnson, David A. Maltz, in Mobile Computing, editor T. Imielinski and Hank Korth, Kluwer, 1996. 

. An Implementation Study of the AODV Routing Protocol, Elizabeth M. Royer and Charles E. Perkins, Proceedings of the IEEE Wireless Communications and Networking Conference, Chicago, IL, September 2000.

MANET Unicast Routing II (April 26)  

. Optimized Link State Routing Protocol (OLSR), RFC 3626.

Samir R. Das, Charles E. Perkins, Elizabeth M. Royer and Mahesh K. Marina. Performance Comparison of Two On-demand Routing Protocols for Ad hoc Networks. IEEE Personal Communications Magazine Special Issue on Ad hoc Networking, February 2001, pp. 16-28.

MANET Multicast Routing (April 28) 

Multicast Operation of the Ad hoc On-Demand Distance Vector Routing Protocol. Royer and Perkins, Proceedings of Mobicom, August 1999. 

. On-Demand Multicast Routing Protocol. Lee, Gerla and Chiang, Proceedings of WCNC, September 1999. 

Wireless Internetworking (May 3) 

. A Mobile Host Protocol Supporting Route Optimization and Authentication, Andrew Myles, David B. Johnson, Charles Perkins, IEEE Journal on Selected Areas in Communications, special issue on Mobile and Wireless Computing Networks, 13(5):839-849, June 1995.

. FLIP: A Flexible Interconnection Protocol for Heterogeneous Internetworking, Ignacio Solis and Katia Obraczka, in ACM/Kluwer Mobile Networking and Applications (MONET) Special on Integration of Heterogeneous Wireless Technologies.

Optional Reading:

. Mobility Support in IPv6 , Charles E. Perkins and David B. Johnson. Proceedings of the Second Annual International Conference on Mobile Computing and Networking (MobiCom'96), November 1996.

. TCP Performance in Mobile-IP, Foo Chun Choong.

End-to-End Protocols I: Infrastructure-Based Wireless Networks (May 5 and May 10) 

. Improving TCP/IP Performance over Wireless Networks, Hari Balakrishnan, Srinivasan Seshan, Elan Amir, Randy H. Katz. Proc. 1st ACM Conf. on Mobile Computing and Networking, Berkeley, CA, November 1995. 

. Delayed duplicate acknowledgements: a TCP-Unaware approach to improve performance of TCP over wireless, Nitin H. Vaidya, Milten N. Mehta, Charles E. Perkins, Gabriel Montenegro. 

. I-TCP: indirect TCP for mobile hosts, 15th Int'l Conf. on Distributed Computing Systems (ICDCS), May 1995.

End-to-End Protocols II: MANETs (May 10 and 12) 

. Analysis of TCP Performance over Mobile Ad Hoc Networks, G. Holland and N. H. Vaidya, Fifth Annual International Conference on Mobile Computing and Networking (MOBICOM), Seattle, August 1999.

. A Comparison of TCP Performance over Three Routing Protocols for Mobile Ad Hoc Networks, Thomas Dyer, Rajendra Boppana, Mobihoc 2001.

. Improving TCP Performance over Mobile Ad-Hoc Networks with Out-of-Order Detection and Response, F. Wand and Y. Zhang, Mobihoc 2002.

 Reliable Multipoint Communication (Optional Reading) 

. Combining Source- and Localized Recovery to Achieve Reliable Multicast in Multi-Hop Ad Hoc Networks, Venkatesh Rajendran, Katia Obraczka, Yunjung Yi, Sung-Ju Lee, Ken Tang and Mario Gerla, Proceedings of the IFIP Networking' 04, May 2004.

. Reliable Adaptive Lightweight Multicast Protocol, Ken Tang, Katia Obraczka, Sung-Ju Lee and Mario Gerla, Proceedings of IEEE ICC 2003, May 2003.

. Ken Tang, Katia Obraczka, Sung-Ju Lee and Mario Gerla, “Congestion Controlled Adaptive Lightweight Multicast in Wireless Mobile Ad Hoc Networks”, Proceedings of IEEE ISCC, July 2002.

Exam: (May 17th)

Edge Intelligence: (May 19th): Hari

. Satyanarayanan, M. “The Emergence of Edge Computing.” Computer 50, no. 1 (January 2017): 30–39.

. Zhou, Z., X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang. “Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing.” Proceedings of the IEEE 107, no. 8 (August 2019): 1738–62.

. McMahan, H Brendan, Eider Moore, Daniel Ramage, and Seth Hampson. “Communication-Efficient Learning of Deep Networks from Decentralized Data,” n.d., 10.

Security in Wireless Networks (May 24)

- AI/ML-Based Security for Wireless Networks: (Sloan)

. Maxim Kalinin,  Peter Zegzhda, "AI-based Security for the Smart Networks", SIN 2020: 13th International Conference on Security of Information and Networks, Article No. 22, Pages 1-4, Nov. 2020,

. Elisa Bertino, Imtiaz Karim, "AI-powered Network Security: Approaches and Research Directions", 8th NSysS 202: 8th International Conference on Networking, Systems and Security, Pages 97-105, December 2021. 

. Amir Afaq, Noman Haider, Muhammad Zeeshan Baig, Komal S. Khan, Muhammad Imran, Imran Razzak, "Machine learning for 5G security: Architecture, recent advances, and challenges", Ad Hoc Networks, Volume 123, Issue C, Dec. 2021.

IoT (May 26)

- Power-Awareness in IoT: (Theo)

. W. Gao, W. Du, Z. Zhao, G. Min and M. Singhal, "Towards Energy-Fairness in LoRa Networks," 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019, pp. 788-798, doi: 10.1109/ICDCS.2019.00083. pdf:

. C. Gomez, J. C. Veras, R. Vidal, L. Casals, and J. Paradells, “A Sigfox Energy Consumption Model,” Sensors, vol. 19, no. 3, p. 681, Feb. 2019, doi: 10.3390/s19030681. pdf:

M. Chen, Y. Miao, Y. Hao and K. Hwang, "Narrow Band Internet of Things," in IEEE Access, vol. 5, pp. 20557-20577, 2017, doi: 10.1109/ACCESS.2017.2751586. pdf:

- IoT Security: (Shayan)

. A. Mosenia and N. K. Jha, "A Comprehensive Study of Security of Internet-of-Things," in IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 4, pp. 586-602, 1 Oct.-Dec. 2017, doi: 10.1109/TETC.2016.2606384.

. M. Antonakakis, "Understanding the Mirai Botnet," in USENIX Association in USENIX Security Symposium, Aug. 2017, isbn:978-1-931971-40-9.

. E. Fazeldehkordi, O. Owe and J. Noll, "Security and Privacy in IoT Systems: A Case Study of Healthcare Products," 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT), 2019, pp. 1-8, doi: 10.1109/ISMICT.2019.8743971.

Disruption-Tolerant Networks (May 31): Charitha

 . Silva, A. P., Burleigh, S., Hirata, C. M., & Obraczka, K. (2015). A survey on congestion control for delay and disruption tolerant networks. Ad Hoc Networks, 25, 480-494.

. Jones, E. P., & Ward, P. A. (2006). Routing strategies for delay-tolerant networks. Submitted to ACM Computer Communication Review (CCR), 1.

. Soelistijanto, B., & Howarth, M. P. (2013). Transfer reliability and congestion control strategies in opportunistic networks: A survey. IEEE communications surveys & tutorials, 16(1), 538-555.

. RFC 4838: Delay-Tolerant Networking Architecture

Security in Wireless Networks (May 31): Tamir

- Cross-Layer Device Fingerprinting: (Tamir)

. Q. Xu, R. Zheng, W. Saad and Z. Han, "Device Fingerprinting in Wireless Networks: Challenges and Opportunities," in IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 94-104, Firstquarter 2016, doi: 10.1109/COMST.2015.2476338. [source]

K. Bonne Rasmussen and S. Capkun, "Implications of radio fingerprinting on the security of sensor networks," 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops - SecureComm 2007, 2007, pp. 331-340, doi: 10.1109/SECCOM.2007.4550352 [source]


An, G. & Kim, S.-H. (2013). MAC spoofing attack detection based on EVM in 802.11 WLAN. UBICOMM 2013 - 7th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. 163-167. [source


Project Ideas:

1. According to the U.S. Department of Transportation (USDOT), platooning is a coordinated operation of two or more vehicles via cooperative adaptive cruise control (CACC). Platooning provides a number of benefits, including: fuel efficiency, road capacity and road safety.

Platooning systems can be classified broadly into two types, 1. Centralized and 2. Decentralized. Centralized approach, which is characterized with the presence of a leader suffers from few well known drawbacks such as single point of failure and performance bottleneck (the leader), as well as shorter platoon-length and data loss due to longer range communication from the leader. 

Decentralized platooning systems were proposed to mitigate the drawbacks of centralized platooning mentioned above. There is no platoon leader and vehicles interact only with neighboring vehicles to complete the maneuvers. Localized interactions allow decentralized platooning systems handle longer platoons

Generally, platooning systems adopt top-down approaches where high-level objectives, in this case, the platoon maneuvers such as join, exit and lane change, are defined and then workflows, including message exchange, specific for each maneuver are defined. This kind of approach is termed as Deliberate Decentralized Systems. In our research, we have proposed a new type of decentralized platooning that adopts the bottom-up approach which is inspired by biological systems. We call our approach Emergent-Behavior Based Platooning reported in:

S. Sreenivasamurthy and K. Obraczka, “Towards Biologically Inspired Decentralized Platooning for Autonomous Vehicles,” 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-7, doi: 10.1109/VTC2021-Spring51267.2021.9448731.pdf

The goal of the project is to implement a Deliberate Decentralized Platooning Systems. The implementation will be used to compare it against existing Centralized Platooning System and a novel Emergent Decentralized Platooning System. Therefore, the infrastructure on which this system will be implemented is rather fixed. We would like to use PLEXE-3.0 simulator for this work. This uses additional software and the versions of those should be as follows - omnetpp-5.6.2,  sumo-1.9.2  and  veins-5.1.

The choice of actual implementation of the Deliberate Decentralized Platooning Systems is left to the implementer. However, it should support at least platoon joining at the tail end and multi vehicle platoon exit maneuvers.

Some examples of Deliberate Decentralized Platooning Systems:

Heinovski, Julian, and Falko Dressler. "Platoon formation: Optimized car to platoon assignment strategies and protocols." 2018 IEEE Vehicular Networking Conference (VNC). IEEE, 2018.

Michaud, Franois, et al. "Coordinated maneuvering of automated vehicles in platoons." IEEE Transactions on Intelligent Transportation Systems 7.4 (2006): 437-447.

Renzler, Tobias, Michael Stolz, and Daniel Watzenig. "Decentralized dynamic platooning architecture with v2v communication tested in omnet++." 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE). IEEE, 2019.

Some useful links:


2Based on the paper: S. Mansfield, K. Veenstra and K. Obraczka, “TerrainLOS: An Outdoor Propagation Model for Realistic Sensor Network Simulation”, In Proceedings of IEEE Computer Society’s MASCOTS, 2016, extend TerrainLOS to incorporate more realistic channel propagation models.

3. Based on the paper: S. Mansfield, K. Veenstra and K. Obraczka, “TerrainLOS: An Outdoor Propagation Model for Realistic Sensor Network Simulation”, In Proceedings of IEEE Computer Society’s MASCOTS, 2016, port TerrainLOS to the ns3 network simulator.

4. Propose a mechanism (e.g., using machine learning) to automatically adjust the route cache TTL for on-demand MANET routing protocols.

5. In Yalda Edalat, Katia Obraczka, and Bahadur Amiri, “A Machine Learning Approach for Dynamic Control of RTS/CTS in WLANs”, in IEEE Mobiquitous 2018, a machine learning approach was proposed to enable/disable IEEE 802.11's RTS/CTS. The proposed mechanism uses congestion as well as packet airtime to decide whether turn RTS/CTS on/off. Different metrics can be used to measure channel congestion such as mean access delay, packet delivery ratio, collision probability, status of sender's queue, average length of idle periods, etc.  In this project, you will use SENSE [1] to estimate contention using any of the metrics suggested above, other than collision probability. Based on this estimate, RTS/CTS will be enabled/disabled. As basis of comparison, you will use the standard approach to setting the value of the RT.

[1] Y. Edalat, J. S. Ahn, and K. Obraczka. “Smart Experts for Network State Estimation.” IEEE Transactions on Network and Service Man- agement 13, no. 3 (2016): 622–635.  

6. Estimating the performance of multimedia traffic is important in numerous contexts, including routing and forwarding, QoS provisioning, and adaptive video streaming[1]. In our research, we are proposing a network performance estimator, called MAPE, which aims at providing, in quasi real-time, network performance estimates for IoT multimedia traffic in IEEE 802.11 multihop wireless networks. MAPE is a deterministic simulation-based estimator that provides real-time per-flow throughput, packet loss and delay estimates while considering inter-flow interference and multi-rate flows, typical of multimedia traffic[2].

MAPE is able to provide network performance estimates that can be used by IoT multimedia services, notably to inform real-time route selection in IoT video transmission. The routing protocol would invoke MAPE with an up-to-date network snapshot as input. Then, based on MAPE's performance estimates, it would perform route selection accordingly. For instance, a proactive link-state algorithm (e.g., OLSR can periodically discover topology changes and disseminate this information through link state  updates that MAPE can use to adjust its estimates. Network topology information would be updated whenever a node identifies “significant” changes in the network topology, e.g., link failures, new nodes/links or changes in link quality.

The goal of the project is to integrate MAPE with the OLSR routing protocol[3], using NS-3 simulator, to provide an up-to-date network snapshot periodically according to link quality between nodes and then perform a heuristic-based multipath algorithm based on MAPE's performance estimates—instead of hop count—to calculate multiple shortest paths. The implementation will be used to evaluate the performance of the routing protocol considering the network dynamic (changes in the network topology) and the source node synchronization (updates the network snapshot).


[1] F. Bhering, K. Obrackza, et al. Wireless multipath video transmission: when IoT video applications meet networking—a survey. Multimedia Systems (2022). 

[2] F. Bhering, K. Obrackza, et al. Network Performance Estimator with Applications to Route Selection for IoT Multimedia Applications. Available in:

[3] NS-3: Optimized Link State Routing (OLSR). Available in:

7. Advances in video cameras and wireless communication technology have contributed to the unprecedented growth of a variety of IoT video applications. However, meeting these applications' quality-of-service requirements pose significant challenges to the underlying network [1]. In addition to their high bandwidth demands, video applications also require more stringent quality-of-service guarantees from the network, such as bounded delay and delay jitter in order to deliver adequate quality-of-experience to users. Meeting such requirements becomes even more challenging in wireless networking environments due to their more limited capacity and higher data loss and link failure probability. It is thus increasingly important to understand the resource requirements of different video traffic and how they perform under different network conditions.

The goal of this project is to evaluate wireless video transmission quality under a variety of network conditions, The experiments will use ns-3's Evalvid simulator which is able to assess user-perceived quality of a wireless video transmission [2] [3]. The main contribution of the project is to propose metrics that will be representative of the underlying netwotk, e.g., network utilization. Based on these metrics to classify the underlying network, evaluate video quality (using metrics like SSIM and PSNR) under a variety of video application scenarios (e.g., number of video sources and their location, data rates, etc).


[1] F. Bhering, K. Obrackza, et al. Wireless multipath video transmission: when IoT video applications meet networking—a survey. Multimedia Systems (2022). 

[2] J. Klaue, B. Rathke, A. Wolisz. Evalvid–A framework for video transmission and quality evaluation. In International conference on modelling techniques and tools for computer performance evaluation, Springer (2003).

[3] NS-3 Simulator, Available in: 

8. Cross-layer Device Fingerprints

Suggested Resources: (Need to ask for access permission)

Motivation and Background:

Todays’ data networks are becoming increasingly complex, with millions of devices ranging from small sensors, smartphones, vehicles, to computing servers that communicate using a variety of network technologies, e.g., IoT, WLAN, cellular. In addition to their heterogeneity, today’s networks are highly dynamic with nodes joining and leaving the network continuously. Moreover, today's networks ever increasing use of wirelss communication make them quite vulnerable to security attacks, for example through device impersonation where malicious actors capture legitimate cryptographic credentials. Consequently, devising low-complexity methods for efficiently identifying legitimate users and preventing malicious attacks is of great interest. Recently, device fingerprinting (DF) has emerged as a promising technique against impersonation or insider attacks. DF can considerably enhance device identification accuracy and robustness, thus significantly reducing the risk of device impersonation attacks by leveraging unique characteristics of devices at the physical and MAC layers, termed hereafter as Cross-Layer (CL)-device fingerprinting (DF). 


The project can focus on one of the following goals:

1) Device feature extraction: Identify device features to be used as fingerprints showing how they can uniquely identify devices. For this aspect of the project, Matlab will be used to identify and extract device features to be used as their fingerprints.

2) Using device features such as the EVM and MAC address, use ML techniques to decide whether devices are authorized or not.