Adaptive Communication in Multi-robot Systems Using Directionality of Signal Strength

Gil, Stephanie; Kumar, Swarun; Katabi, Dina; Rus, Daniela

doi:10.1007/978-3-319-28872-7_4

Cited by 22 publications

(49 citation statements)

References 18 publications

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“…These blockages occur with positive and non-negligible probability as is modeled in [15]- [17] and they become more frequent as the distance between the transmitter and receiver grows. Connectivity is also a common problem in mobile robotic systems (see [18]- [21]), where robot location affects both the robot connectivity to the FC, and its event-detection probability. To the best of our knowledge, minimizing the expected loss function of cloud-cluster sensor networks where sensors are intermittently connected to the cloud was not previously investigated.…”

Section: A Related Workmentioning

confidence: 99%

Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

Yemini¹,

Gil²,

Goldsmith³

2021

Preprint

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We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors may collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme.Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors' and clusters' detection quality, heterogeneity in their communication opportunities, and nonconvexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture. I. INTRODUCTIONThe next generation of wireless infrastructure enables cloud connectivity, and with it, powerful centralized decision making based on sensor data. However, cloud connectivity of sensors cannot M. Yemini and A. J. Goldsmith are with the Electrical and

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Section: A Related Workmentioning

confidence: 99%

Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

Yemini¹,

Gil²,

Goldsmith³

2021

Preprint

Self Cite

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“…In order to establish reliable wireless communication with a team of client agents who are performing an independent task, Gil et al (2015) deployed an auxiliary team of robot routers. The idea is to find the best positions of the routers that support variable demanded rates to the client's robots.…”

Section: Communicationmentioning

confidence: 99%

Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study

Botelho

Marietto

Mendes

et al. 2020

The Knowledge Engineering Review

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Multi-Robot System (MRS) is composed of a group of robots that work cooperatively. However, Multi-Agent System (MAS) is computational systems consisting of a group of agents that interact with each other to solve a problem. The central difference between MRS and MAS is that in the first case, the agent is a robot, and in the second, it is a software. Analyzing the scientific literature, it is possible to notice that few studies address the integration between MAS and MRS. In order to achieve the interdisciplinary integration, the theoretical background of these areas must be considered in this paper, so that the integration can be applied using a case study of decentralized MRS. The objective of this MRS is to track and surround a stationary target. Also, it has been implemented and validated in the robot simulator called Virtual Robot Experimentation Platform (V-REP). In the validation of the proposed MRS, a scenario with three robots and a stationary target were defined. In the tracking task, the robot can detect the target whose position is not known a priori. When the detection occurs, the V-REP informs the target position to the robot because the environment is discretized into a grid of rectangular cells. After that, all the robots are directed to the target, and the surround task is realized. In this task, a mathematical model with direct communication between the robots was used to keep the robots equidistant therefrom and from each other.

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“…In [32], the authors designed a comparative score, searching for a node message patterns that are dissimilar relative to the messages of other neighbors. In [33] the authors showed analytically and experimentally that it is possible to determine if messages come from the same source at the physical layer, providing a solution for the detection of Sybil-type attacks. In [22], [27] the authors proposed several strategies based on carefully crafted metrics for detecting and localizing attackers.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks-Aided Insider Attack Detection for the Average Consensus Algorithm

Zhang

et al. 2020

IEEE Access

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To support the big-data processing needs of large-scale deployments of smart devices, there is significant interest in moving from cloud-computing to multi-agent (fog-computing) models, given these algorithms scalability and self-healing properties with respect to nodes and link failures. However, these algorithms are often based on the average consensus primitive, which is, unfortunately, vulnerable to data injection attacks. Recognizing this challenge, this work proposes three novel methods for detecting and localizing adversarial nodes using neural network (NN) models. The methods proposed are based on fully distributed algorithms, wherein each node locally updates its local states by exchanging information with its neighbors without supervision. Compared to the state-of-the-art, the proposed approach leverages the automatic learning characteristics of NN to reduce the dependence on pre-designed models and human expertise in complex internal attack scenarios. Simulation results show that the NN-based methods can significantly improve the attacker detection and localization performance. INDEX TERMS Gossip algorithm, average consensus, neural network (NN), insider attack, detection and localization.

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Adaptive Communication in Multi-robot Systems Using Directionality of Signal Strength

Cited by 22 publications

References 18 publications

Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study

Neural Networks-Aided Insider Attack Detection for the Average Consensus Algorithm

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