Here is a list of how-to solutions for problems that we have encountered while doing research in the i-NRG Lab. Note: the … Read More
Socially- and Geographically-Aware Modeling Framework for User Mobility in Wireless Networks
The "Socially- and Geographically-Aware Modeling Framework for User Mobility in Wireless Networks" is a project sponsored by the NSF under the NeTS Program. One of its main goals is to develop novel, socially-inspired human mobility models that also account for geographic diversity of the region of interest. The proposed models consider features observed in real human networks such as differential popularity, transitivity and clustering as well as geographical features and preferences from users.
One of the project’s main goal is to develop novel, socially-inspired human mobility models that also account for geographic diversity of the region of interest. The proposed models consider features observed in real human networks such as differential popularity, transitivity and clustering as well as geographical features and preferences from users. Another important deliverable of our work is a suite of tools that implement the proposed models and that can be used by other researchers and practitioners in the evaluation of mobile systems and protocols.
This project has made contributions to user mobility characterization and modeling in wireless networks, sensor network deployment in outdoor environments, employment of computational intelligence techniques to estimate network conditions, and Disruption Tolerant Network (DTN) congestion control. More specifically, our accomplishments to-date include:
- We have developed socially-inspired human mobility models that also account for geographic diversity of the region of interest. As we continue the development of our socially- and geographically-inspired user mobility models, we have also been exploring applications in other disciplines, e.g., urban planning, smart transit- and transportation systems, etc.
- Under the research activity focusing on sensor network deployment, we have developed a terrain model, called TerrainLOS, for the Cooja/Contiki wireless sensor network simulator/emulator. We have contributed our TerrainLOS implementation to the Cooja/Contiki software distribution.
- Another research trust under this project focuses on the use of machine learning techniques to estimate network performance. We have proposed a technique called Smart Experts for Network State Estimation, or SENSE, which uses a simple, yet effective algorithm
combining a machine-learning method known as FixedShare Experts and Exponentially Weighted Moving Average (EWMA). SENSE can be used to improve the performance of a wide range of network protocols and applications. We are currently exploring how SENSE can improve the performance of the IEEE 802.11 protocol.
- Under the DTN congestion control research thrust, we have introduced the first reinforcement learning based congestion control algorithm which is able to adapt a wide range of DTN scenarios and conditions.
To-date, this project has produced a number of publications in peer-reviewed conferences and journals. Some of our key results to-date include: the development of the first framework to model “waypointbased” mobility: development of the first sensor network deployment algorithm for outdoor applications that considers terrain; development of the first terrain-based communication model for sensor network simulation platforms; and development of the first computational-intelligence based congestion control framework for Disruption-Tolerant Networks (DTNs), which can deliver adequate performance in a wide range of DTN scenarios and applications.
For further information about this project, please follow the links below.