Research Interests

“Challenged” wireless networks where connectivity is intermittent, resources (e.g. battery, bandwidth) are scarce, and applications of interest have non-stringent real time constraints (e.g. data sensing/collecting, social networking, micro-blogging, online flea markets, etc.). Example networks include sensor networks, vehicular networks, pocket-switched networks, etc. In such networks, one needs to exploit node mobility to bridge disconnected parts of the networks. To this end, controlled redundancy (e.g. replication or coding) along with learning algorithms that infer mobility pattern properties and use them to predict future node contacts have been proven quite useful.

Delay Tolerant & Opportunistic Networking (more details)

I’m generally interested in the application of different mathematical tools and/or cross-disciplinary approaches to solve or better understand difficult networking problems. Some of these tools include:

· Stochastic Processes and Queueing Theory

· Transient Analysis of Random Walks

· Fluid Approximations and Mean-field Analysis

· Optimization Theory

· Complex Network Analysis

· Statistical Learning Theory

· Graph Theory

 

A summary of particular areas of interest is shown below. Please follow the links for more details on each topic.

Analytical Tools for Networks

Complex Network Analysis has emerged as a method to improve the performance of large networks arising in computer science. Online Social Networks and the need to scalably analyze and search their connectivity graphs has been a major driving force. Aggregating mobility patterns of nodes to social graphs has also been used successfully to study the growing number of available mobility traces. It enables learning the underlying structure governing human mobility, so as to use it to efficiently "navigate" the sparse connectivity graph.

Social / Complex Network Analysis (more details)

An important gap exists between mobility models used in theory and simulations, and real-life mobility characteristics revealed by a number of trace-based analyses. Although some initial efforts have been performed towards bridging this gap, what is really needed is a mobility model that is rich enough to better resemble real-life mobility, yet is analytically tractable. I’m interested in analyzing existing mobility traces from different applications as well as collaborating with researchers who collect new traces, in order to identify the building components for such a model.

It is now becoming too expensive to collect and analyze packet level data, especially closer to the core. Instead, flow data have been proposed to classify traffic, detect anomalies, etc. Yet, new applications and the hiding of them on known (e.g. port 80) or random ports makes these tasks extremely challenging. Cross-correlation between different traffic types, subnet aggregation, and analysis of the communication graphs are some of the methods that can be used to detect anomalies, monitor servers, etc. from a vantage point (e.g. gateway routers).

Traffic Analysis and Host Profiling using Netflow Data

Mobility Modeling (more details)

Operators are having a difficult time keeping up with the exponential increase in data traffic demand. Upgrading to new technologies (e.g. 4G) is expensive, and not profitable due to flat rate plans. For these reasons, operators are investigating ways to offload as much data as possible through alternative means, such as WiFi networks and D2D communication. We have attempted to model both problems analytically, in order to better understand the impact of different network parameters on performance, as well as the feasibility of each approach. We have also used these models to optimize various aspects of offloading:

Mobile Data Offloading (more details)

Operators struggling to continuously add capacity and upgrade their architecture to keep up with traffic demand. Small cell networks (SCNs) are widely considered as a promising solution for future cellular deployments. By increasing the number of cells a user can associate with, (i) user quality of experience (QoE) can be improved, and (ii) traffic can be offloaded from congested base stations, to achieve better load balancing. In addition, the power consumption of current deployments, for instance due to idle power and cooling equipment, is a major concern for operators. Small cells further offer the opportunity for more dynamic power management of base stations, due to coverage overlaps and larger spatio-temporal load fluctuations. Yet, such power management decisions (e.g. turning off a base station) should not lead to excessive performance degradation for users associated with it or additional power consumption. This tradeoff becomes significantly more challenging to evaluate in future networks, due to the diversity of services offered to users beyond the traditional voice calls, as well as the complexity of traffic scheduling algorithms.

Small Cell Networks and HetNets (more details)