Wireless communication systems are susceptible to both unintentional interference and intentional jamming attacks. For mesh and ad-hoc networks, interference affects the network topology and can cause the network to partition, which may completely disrupt the applications or missions that depend on the network. Defensive techniques can be applied to try to prevent such disruptions to the network topology. Most previous research in this area is on improving network resilience by adapting the network topology when a jamming attack occurs. In this paper, we consider making a network more robust to jamming attacks before any such attack has happened. We consider a network in which the positions of most of the radios in the network are not under the control of the network operator, but the network operator can position a few “helper nodes” to add robustness against jamming. For instance, most of the nodes are radios on vehicles participating in a mission, and the helper nodes are mounted on mobile robots or UAVs. We develop techniques to determine where to position the helper nodes to maximize the robustness of the network to certain jamming attacks aimed at disrupting the network topology. Using our recent results for quickly determining how to attack a network, we use the harmony search algorithm to find helper node placements that maximize the number of jammers needed to disrupt the network
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Optimizing Non-Markovian Information Gain under Physics-based Communication Constraints
In many exploration scenarios, it is important for robots to efficiently explore new areas and constantly communicate results. Mobile robots inherently couple motion and network topology due to the effects of position on wireless propagation, e.g., distance or obstacles between network nodes. Information gain is a useful measure of exploration. However, finding paths that maximize information gain while preserving communication is challenging due to the non-Markovian nature of information gain, discontinuities in network topology, and zero-reward local optima. We address these challenges through an optimization and sampling-based algorithm. Our algorithm scales to 50% more robots and obtains 2-5 times more information relative to path cost compared to baseline planning approaches.
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- NSF-PAR ID:
- 10219013
- Date Published:
- Journal Name:
- IEEE Robotics and Automation Letters
- ISSN:
- 2377-3774
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LRA.2021.3068935
