I have also used network interdiction modeling to study the vulnerability of wireless networks. Although the field of network interdiction has produced a mature set of modeling and algorithmic tools, these models and algorithms are tailored for “wired” networks, such as supply chains and road networks, and not for wireless ones. Thus, there is a need to extend the concept of interdiction modeling to the wireless domain.

One of my former Ph.D. students has developed a Bender’s decomposition approach for optimally locating jamming devices during a flow jamming attack on a wireless communication network. Another student has studied how to compute the optimal placement of jamming devices within an array of access points as well as how to optimally design such an array that is subject to jamming attacks.

I have also developed a branch-and-cut approach for optimally locating jamming devices given that the wireless network is subject to protocol interference. I am also working on a variation of this approach for studying the version of the problem in which the network is subject to physical interference as well as a study of how directional antennas and limited battery capacity influences vulnerability to jamming. These studies give insight into how to protect wireless networks and how to disable malicious wireless networks. This work is funded by the U.S. Army Engineering Research and Development Center.

Selected Publications

  1. A mixed-integer programming approach for optimizing flow jamming attacks
    Satish Vadlamani, Hugh Medal, David Schweitzer, Apurba Nandi, Burak Ekşioğlu. To appear in Computers and Operations Research.
    Publication Year: 2018
    [PDF]   Abstract
  2. Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain
    Hugh R. Medal, Edward A. Pohl, Manuel D. Rossetti. IIE Transactions 48(3), 220–234.
    Publication Year: 2016
    [PDF]   [Link to Article] Abstract
  3. The wireless network jamming problem subject to protocol interference
    Hugh R. Medal. Networks 67(2), 111–125
    Publication Year: 2016
    [PDF]   [Link to Article] Abstract

Funding

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
Abstract:

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
Abstract:

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.