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: (1) user mobility characterization and modeling in wireless networks, (2) IoT and sensor network deployment in outdoor environments, (3) development of computational intelligence techniques to estimate network conditions, and (4) development of congestion control for disruption-tolerant networks (DTNs). 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. Besides applying our models to core network services (e.g., routing), we have also explored applications in other disciplines, e.g., urban planning, smart transit- and transportation systems, etc.
- Under the research activity focusing on IoT and sensor network deployment, we have introduced a novel distributed greedy deployment algorithm, we call grid partition, as well as a distributed simulated annealing algorithm and an evolutionary strategy algorithm. We have also 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 have explored 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.
This project has produced a number of publications in peer-reviewed conferences and journals. Some of our key results to-date include: development of the first framework to model “way-point based” mobility model; 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; development of the first computational-intelligence based congestion control framework for delay- and disruption-tolerant networks (DTNs), which can deliver adequate performance in a wide range of DTN scenarios and applications; and development of a simple, yet efficient machine learning mechanism to improve network protocol performance. Our work was the first to employ a machine learning based approach to dynamically control the use of the IEEE 802.11’s RTS/CTS handshake as well as dynamically adjust 802.11’s contention window based on current network conditions.
For further information about this project, please follow the links below.