Scott Pudlewski, Ph.D.
Ph.D. in Electrical Engineering (2012)
Education
- Ph.D. in Electrical Engineering - University at Buffalo, SUNY (2012)
- M.S. in Electrical Engineering - University at Buffalo, SUNY (2010)
- B.S. in Electrical Engineering - Rochester Institute of Technology (2008)
Scott Pudlewski received his Ph.D. in Electrical Engineering from the University at Buffalo, The State University of New York (SUNY) in April 2012, working in the Wireless Networks and Embedded Systems Laboratory under Professor Tommaso Melodia. His doctoral research focused on compressed sensing for video encoding and transmission, cooperative communications in wireless multimedia sensor networks using compressed sensing, video distortion based networking, and wireless multimedia sensor networks in general. He successfully defended his Ph.D. thesis titled “Compressed-sensing-based Video Streaming in Wireless Multimedia Sensor Networks” on April 20th, 2012. Scott also received his M.S. in Electrical Engineering from the University at Buffalo in 2010, and his B.S. in Electrical Engineering from the Rochester Institute of Technology in 2008. He is the recipient of the SAP America Scholarship in 2008.
Publications
2020
Journals & Magazines
“Distributed Joint Power, Association and Flight Control for Massive-MIMO Self-Organizing Flying Drones.”
IEEE/ACM Transactions on Networking
(2020)Journal
Conference Papers
Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.
2018
Journals & Magazines
“Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach.”
IEEE Transactions on Mobile Computing
(2018)Journal
Conference Papers
“Self-Organizing Flying Drones with Massive MIMO Networking.”
Proc. of Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)
(2018)Conference
2017
Journals & Magazines
“Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications.”
Proc. of IEEE Conf. on Communications and Network Security
(2017)Journal
2015
Journals & Magazines
“Video transmission over lossy wireless networks: A cross-layer perspective.”
IEEE Journal of Selected Topics in Signal Processing
(2015)Journal
2013
Journals & Magazines
“A Tutorial on Encoding and Wireless Transmission of Compressively Sampled Videos.”
IEEE Communications Surveys and Tutorials
(2013)Journal
Conference Papers
“RA-CVS: Cooperating at low power to stream compressively sampled videos.”
2013 IEEE International Conference on Communications (ICC)
(2013)Conference
2012
Conference Papers
“Network Simulation for Advanced HF Communications Engineering.”
IET Intl. Conf. on Ionospheric Radio Systems and Techniques (IRST)
(2012)Conference
2011
Journals & Magazines
“Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks.”
IEEE Transactions on Mobile Computing
(2011)Journal
Conference Papers
“A Rate-Energy-Distortion Analysis for Compressed-Sensing-Enabled Wireless Video Streaming on Multimedia Sensors.”
Proc. of IEEE Global Communications Conference (GLOBECOM)
(2011)Conference
2010
Journals & Magazines
The availability of inexpensive CMOS cameras and microphones that can ubiquitously capture multimedia content from the environment is fostering the development of wireless multimedia sensor networks (WMSNs), i.e., distributed systems of wirelessly networked devices that can retrieve video and audio streams, still images, and scalar sensor data. A new cross-layer rate control scheme for WMSNs is introduced in this paper with a twofold objective: (i) maximize the video quality of each individual video stream; (ii) maintain fairness in the domain of video quality between different video streams. The rate control scheme is based on analytical and empirical models of video distortion and consists of a new cross-layer control algorithm that jointly regulates the end-to-end data rate, the video quality, and the strength of the channel coding at the physical layer. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. Once the end-to-end data rate has been determined, the sender adjusts the video encoder rate and the channel encoder rate based on the overall rate and the current channel quality, with the objective of minimizing the distortion of the received video. Simulations show that the proposed algorithm considerably improves the received video quality with respect to state-of-the art rate control algorithms, without sacrificing on fairness.
Conference Papers
“C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks.”
Proc. of IEEE Intl. Conf. on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)
(2010)Conference
“On the Performance of Compressive Video Streaming for Wireless Multimedia Sensor Networks.”
Proc. of IEEE Intl. Conf. on Communications (ICC)
(2010)Conference
“Resilient Image Sensor Networks in Lossy Channels Using Compressed Sensing.”
Proc. of IEEE Intl. Workshop on Information Quality and Quality of Service for Pervasive Computing (IQ2S)
(2010)Conference
2009
Journals & Magazines
“A Case for Compressive Video Streaming in Wireless Multimedia Sensor Networks.”
IEEE COMSOC MMTC E-Letter
(2009)Journal
Conference Papers
“DMRC: Distortion-minimizing Rate Control for Wireless Multimedia Sensor Networks.”
Proc. of IEEE Intl. Conf. on Mobile Ad Hoc And Sensor Systems (MASS)
(2009)Conference
“Distributed Spectrum Sharing for Video Streaming in Cognitive Radio Ad Hoc Networks.”
Proc. of Intl. Workshop on Cross-layer Design in Wireless Mobile Ad Hoc Networks
(2009)Conference