Positioning Helper Nodes to Improve Robustness of Wireless Mesh Networks to Jamming Attacks

Feng, Jixin; Dixon, Warren E.; Shea, John M.

doi:10.1109/glocom.2017.8253960

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…A novel connectivity-aware multi-robot redistribution method is proposed in [33], which considers dynamic task allocation and connectivity for a heterogeneous robot team. Considering that the nodes are attacked, the method in [34] optimizes the positions of the helper nodes to maximize the robustness of the network. By iterative UAV positioning, the authors in [35] utilize biological robustness to design a reliable multi-UAV network, which can effectively reduce the impact of UAV failures.…”

Section: Related Workmentioning

confidence: 99%

Robust Multiple Unmanned Aerial Vehicle Network Design in a Dense Obstacle Environment

Zhang

Yao

Zuo

et al. 2023

Drones

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Highly robust networks can resist attacks, as when some UAVs fail, the remaining UAVs can still transmit data to each other. In order to improve the robustness of a multi-UAV network, most methods construct the network by adjusting the positions of the UAVs and adding a large number of links. However, having a large number of links greatly consumes communication resources and increases serious signal interference. Therefore, it is necessary to study a method that can improve robustness and reduce the number of links. In this paper, we propose a method that consists of combining formation control and link selection, which can work in a distributed manner. For formation control, our method keeps the UAVs compact in the obstacle environment through an improved artificial potential field. The compact formation enables UAVs to have a large number of neighbors. For link selection, reinforcement learning is used to improve the robustness of the network and reduce the number of network edges. In the simulation of the 3D urban environment, three failure modes are used to verify the robustness of the network. The experimental results show that even if the number of links is reduced by 20%, the networks designed by our method still have strong robustness.

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Section: Related Workmentioning

confidence: 99%

Robust Multiple Unmanned Aerial Vehicle Network Design in a Dense Obstacle Environment

Zhang

Yao

Zuo

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…For instance, the mobile relaying systems as proposed in [4] optimize the UAVs transmit power and trajectory to resist jamming and improve the throughput of cellular systems. The 5G communication scheme in [5] uses UAVs to relay the messages for mobile devices to resist jamming and achieve lower time complexity. The UAV-aided VANETs in [6] presents a hotbooting PHC based relay algorithm and uses UAV to relay the messages for onboard units to resist smart jamming, which can reduce the BER of the messages and thus improves the SINR of the VANET.…”

Section: Related Workmentioning

confidence: 99%

UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming

Xiao

Dai

et al. 2018

Preprint

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Cellular systems are vulnerable to jamming attacks, especially smart jammers that choose their jamming policies such as the jamming channel frequencies and power based on the ongoing communication policies and network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular communication framework against jamming. In this scheme, UAVs use reinforcement learning methods to choose the relay policy for mobile users in cellular systems, if the serving base station is heavily jammed. More specifically, we propose a deep reinforcement learning based UAV relay scheme to help cellular systems resist smart jamming without being aware of the jamming model and the network model in the dynamic game based on the previous anti-jamming relay experiences and the observed current network status. This scheme can achieve the optimal performance after enough interactions with the jammer. Simulation results show that this scheme can reduce the bit error rate of the messages and save energy for the cellular system compared with the existing scheme.

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Enhancing Wi-SUN AMI Network Resilience by using Emergency Gateway with Optimal Placement

Boonkajay

Tan

Goh

et al. 2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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Positioning Helper Nodes to Improve Robustness of Wireless Mesh Networks to Jamming Attacks

Cited by 3 publications

References 16 publications

Robust Multiple Unmanned Aerial Vehicle Network Design in a Dense Obstacle Environment

Robust Multiple Unmanned Aerial Vehicle Network Design in a Dense Obstacle Environment

UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming

Enhancing Wi-SUN AMI Network Resilience by using Emergency Gateway with Optimal Placement

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