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Detecting Colluding Sybil Attackers in Robotic Networks using Backscatters
Due to the openness of wireless medium, robotic networks that consist of...
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ShieldScatter: Improving IoT Security with Backscatter Assistance
The lightweight protocols and low-power radio technologies open up many ...
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Analysis of Safe Ultrawideband Human-Robot Communication in Automated Collaborative Warehouse
The paper presents the propagation analysis of ultrawideband Gaussian si...
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Authenticating On-Body Backscatter by Exploiting Propagation Signatures
The vision of battery-free communication has made backscatter a compelli...
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Avalon: Building an Operating System for Robotcenter
This paper envisions a scenario that hundreds of heterogeneous robots fo...
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UWB Propagation Characteristics of Human-to-Robot Communication in Automated Collaborative Warehouse
Propagation of UWB Gaussian signal in a model of automated collaborative...
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Lightweight Sybil-Resilient Multi-Robot Networks by Multipath Manipulation
Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively “observing” signatures, ScatterID actively “manipulates” multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4 verification.
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