Using high-performance computing to simulate the vulnerability of wireless communication networks to jamming attacks

Agency: Engineer Research and Development Center (U.S. Army)
Researcher: Medal, H.R. (PI)
Amount: $106,310

The main goal of this research is to design new algorithms for use on HPC clusters that will enable stakeholders to rapidly and accurately simulate the effect that jamming attacks will have on the performance of a given wireless network design. This goal will be pursued through the following two objectives: 1) design new algorithms for rapidly and accurately computing the throughput of a wireless network under normal conditions (no jamming) and 2) design new algorithms for rapidly and accurately computing the throughput of a wireless network after a jamming attack. These two objectives will contribute to the public good by 1) providing network designers with tools for simulating the throughput of a hypothetical wireless network design and 2) providing designers with tools for simulating the security of a hypothetical design.

WD62 (HPC-based Sensor Analytics) Task 5: Modeling and Simulation of Wireless Sensor Networks

Agency: United States Army Tank Automotive and Armaments Command
Researcher: Medal, H.R. (PI)
Amount: $254,384

We propose to develop new tools for modeling the performance of sensor networks under a variety of environmental conditions (e.g., dust, sand, etc.). Our tools will model the following performance metrics for a given network: vulnerability, reliability, and power consumption / lifetime. Modeling these metrics will provide engineers with a tool for virtual prototyping that can quickly evaluate the performance of candidate network designs and new technologies. In addition, having models for all of these performance metrics will allow engineers to trade off these metrics when evaluating alternative designs. Our modeling approach will model the sensor network as a directed graph, allowing us to use state-of-the-art graph algorithms to model the routing and scheduling of packets through the network. We will also use geospatial data to represent environmental conditions, which will provide needed fidelity to our model. Our model with include functions for intelligent routing and scheduling, in which packets are routed and scheduled through the network in order to both avoid adverse conditions and maximize throughput.