phd thesis on networks

PAWS Princeton Advanced Wireless Systems

Phd thesis: pushing the limits of indoor localization in today's wi-fi networks.

Wireless networks are ubiquitous nowadays and play an increasingly important role in our everyday lives. Many emerging applications including augmented reality, indoor navigation and human tracking, rely heavily on Wi-Fi, thus requiring an even more sophisticated network. One key component for the success of these applications is accurate localization. While we have GPS in the outdoor environment, indoor localization at a sub-meter granularity remains challenging due to a number of factors, including the presence of strong wireless multipath reflections indoors and the burden of deploying and maintaining any additional location service infrastructure. On the other hand, Wi-Fi technology has developed significantly in the last 15 years evolving from 802.11b/a/g to the latest 802.11n and 802.11ac standards. Single user multiple-input, multiple-output (SU-MIMO) technology has been adopted in 802.11n while multi-user MIMO is introduced in 802.11ac to increase throughput. In Wi-Fi’s development, one interesting trend is the increasing number of antennas attached to a single access point (AP). Another trend is the presence of frequency-agile radios and larger bandwidths in the latest 802.11n/ac standards. These opportunities can be leveraged to increase the accuracy of indoor wireless localization significantly in the two systems proposed in this thesis: ArrayTrack employs multi-antenna APs for angle-of-arrival (AoA) information to localize clients accurately indoors. It is the first indoor Wi-Fi localization system able to achieve below half meter median accuracy. Innovative multipath identification scheme is proposed to handle the challenging multipath issue in indoor environment. ArrayTrack is robust in term of signal to noise ratio, collision and device orientation. ArrayTrack does not require any offline training and the computational load is small, making it a great candidate for real-time location services. With six 8-antenna APs, ArrayTrack is able to achieve a median error of 23 cm indoors in the presence of strong multipath reflections in a typical office environment. ToneTrack is a fine-grained indoor localization system employing time difference of arrival scheme (TDoA). ToneTrack uses a novel channel combination algorithm to increase effective bandwidth without increasing the radio’s sampling rate, for higher resolution time of arrival (ToA) information. A new spectrum identification scheme is proposed to retrieve useful information from a ToA profile even when the overall profile is mostly inaccurate. The triangle inequality property is then applied to detect and discard the APs whose direct path is 100% blocked. With a combination of only three 20 MHz channels in the 2.4 GHz band, ToneTrack is able to achieve below one meter median error, outperforming the traditional super-resolution ToA schemes significantly.

• Google Scholar
• EndNote X3 XML

Toward Predictable Networks

Ph.d. dissertation, cornell university. 2021., acm sigcomm doctoral dissertation award, cornell computer science phd dissertation award.

The Internet was designed to provide connectivity with best-effort performance. Four decades later, evolving application demands and technological trends have changed network requirements in fundamental ways. Today, they must deliver stronger guarantees and not just best-effort performance. And they must do so while enabling rich functionality that goes beyond basic connectivity. But most networks still rely on the design principles of the early Internet, which leads to a mismatch between what applications need and what networks provide. To bridge this gap, we need to revisit the fundamental principles and design networks that deliver predictable performance.

This dissertation explores the design and implementation of predictable networks while navigating practical challenges such as uncertain operating conditions, interoperation with existing protocols, and hardware trends. It is relatively easy to provide absolute performance guarantees by over-provisioning or reserving resources, but this leads to inefficient resource utilization. The thesis in this work is that we can effectively navigate the trade-off between utilization and performance to build predictable networks that provide meaningful guarantees to applications and users. To achieve this, we propose a two-pronged approach: design individual networking components that offer well-defined performance guarantees and combine them using mechanisms for building larger, more-predictable systems out of smaller, less-predictable components.

In the context of datacenter networks, this dissertation presents three systems: PicNIC, CoNIC and μP4. PicNIC, an end-to-end system that delivers predictable performance for users of shared public clouds, shows how we can provide performance guarantees atop less-predictable components. It introduces a new networking abstraction for virtual machines that provides quantifiable guarantees. CoNIC and μP4 focus on techniques for building predictable networking components. To meet the competing objectives of performance and flexibility for end-host networking stacks, CoNIC explores hardware-software co-design based on heterogeneous packet-processing architectures. To program emerging domain-specific network processors in a modular and composable manner, μP4 proposes a new language and compiler framework.

In the context of wide-area networks, this dissertation presents two systems: YATES and Smore. These systems focus on traffic engineering to navigate the trade-off between efficiency and robustness in wide-area networks. Using traditional approaches to traffic engineering, operators have been able to achieve very low levels of efficiency while favoring robustness. Smore is a new traffic engineering system based on oblivious routing that achieves high efficiency and robustness simultaneously while meeting practical constraints. Again, Smore shows how we can achieve predictability in a system with inherently unreliable components and operating conditions. YATES enables quick prototyping and evaluation of traffic-engineering systems under diverse operational scenarios.

The Network Science PhD program is a pioneering interdisciplinary program that provides the tools and concepts aimed at understanding the structure and dynamics of networks arising from the interplay of human behavior, socio-technical infrastructures, information diffusion and biological agents.

In The News

Does traditional Chinese medicine work? Network science can help evaluate effectiveness, Northeastern researchers say

Banning Twitter users reduced misinformation and improved discourse after Jan. 6, Northeastern research suggests

Northeastern summit on innovative public health technology is an important first step to fixing systemic flaws

The PhD in Network Science is a pioneering interdisciplinary program that provides the tools and concepts aimed at understanding the structure and dynamics of networks. Network Science research covers a broad range of topics, including: Control of Networks, Biological Networks, Spreading and Influence, Group-Decision Making, Social and Political Networks, Data and Graph Mining, and Network Geometry.

Northeastern University is a world leader in Network Science, and faculty affiliated with the program includes prominent leaders in the field such as Albert-László Barabási, Alessandro Vespignani, Tina Eliasi-Rad, and David Lazer. Graduates will be well-prepared to enter into a number of career paths, including industry research positions, government analyst positions, and post-doctoral or junior faculty positions in academic institutions. Students have the opportunity to work with some of the most prominent network scientists in the world. With frequent guest lecturers and workshop series, students have access to diverse scientists and global leaders in the field.

• Northeastern has several leading laboratories and centers in Network Science, with dozens of faculty, postdoctoral fellows, visiting faculty, and doctoral students
• A select cohort of graduate students from varied academic and research backgrounds learning in the same physical space
• Frequent guest lecturers, workshop series, and faculty-led conferences
• Access to a growing alumni body of successful professionals, an expanding roster of talented research scientists and post-docs, and new learning opportunities across the Northeastern University global network

INTERDISCIPLINARY RESEARCH

Faculty and researchers at the Network Science Institute cover many subject areas in the physical sciences (physics, mathematics, psychology); social sciences (political science, urban networks, communication); health science (epidemiology); and computer and information sciences.

FOUNDATIONAL TRAINING

Foundational training in developing and understanding analytical models and tools, formal methods of network representation, dynamical processes, social structures, and integration of machine learning begin in the first year of the program. This knowledge will help students build an inherently interdisciplinary approach to science, launching the next generation of researchers.

IDEAS + TECHNIQUES

Learning to combine theoretical concepts and substantive questions with the appropriate tools and techniques for data collection and analyses is a key element of the program. Students will be combining ideas, techniques, and collaborations into the novel interdisciplinary approaches that are paramount to Network Science.

Network Science is a major interdisciplinary research area with applications in data science and data analytics methodologies. Network scientists are employed in academia, government, and business. Graduates have obtained industry, government, post-doctoral, and faculty positions at:

• Santa Fe Institute
• Max Planck Institute
• Rutgers University
• Stanford University
• Center for International Security and Cooperation
• National Institutes for Health (USA)
• Center for Disease Control

Application Materials

• Online application
• Application fee
• Unofficial transcripts for all institutions attended (Official transcripts required upon acceptance of admission offer)
• Personal statement
• Three letters of recommendation
• GRE General – recommended, but not required
• Degree earned or in progress at an U.S. institution
• Degree earned or in progress at an institution where English is the only medium of instruction
• Official exam scores from either the TOEFL iBT, IELTS, or  PTE exam

Application

Deadline for completed applications: December 1st

Applicants seeking enrollment prior to the Fall 2024 term should apply at the link under application materials below.

• Program Website

Request Information for PhD in Network Science

• Help & Terms of Use

Software Defined Networking for the Industrial Internet of Things

• Michael Baddeley
• Department of Electrical & Electronic Engineering
• EPSRC Centre for Doctoral Training in Communications

Student thesis : Doctoral Thesis › Doctor of Philosophy (PhD)

• Mesh Networks
• IEEE 802.15.4
• Low-Power Wireless
• Concurrent Transmissions
• Synchronous Flooding
• Programmable Networks
• Adaptive Networking
• Smart Metering

File : application/pdf, 18.6 MB

Type : Thesis

• Persistent link

Related content

Research outputs, poster: atomic-sdn: a synchronous flooding framework for sdn control of low-power wireless.

Research output : Contribution to conference › Conference Poster › peer-review

Atomic-SDN: Is Synchronous Flooding the Solution to Software-Defined Networking in IoT?

Research output : Contribution to journal › Special issue (Academic Journal) › peer-review

Evolving SDN for Low-Power IoT Networks

Research output : Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Competition: CROWN--Concurrent ReceptiOns in Wireless Sensor and Actuator Networks

Research output : Contribution to conference › Conference Paper

Isolating SDN control traffic with layer-2 slicing in 6TiSCH industrial IoT networks

Competition: adaptive software defined scheduling of low power wireless networks.

Research output : Contribution to conference › Conference Paper › peer-review