Redundant data transmission in control/estimation over lossy networks

Mesquita, Alexandre R.; Hespanha, João P.; Nair, Girish N.

doi:10.1016/j.automatica.2012.06.009

Cited by 57 publications

(28 citation statements)

References 16 publications

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“…A similar model has been considered in [17]: our problem formulation is different since we perform network coding and we wish to optimize the network coding weights. Moreover, our model allows that each path can be associated to a different delay: as a direct consequence it is not trivial to decide what is the best strategy when receiveing packets.…”

Section: Introductionmentioning

confidence: 99%

Approximation methods for optimal network coding in a multi-hop control network with packet losses

Smarra

D’Innocenzo

Benedetto

2015

2015 European Control Conference (ECC)

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A Multi-hop Control Network consists of a plant where the communication between sensors, actuators and computational units is supported by a (wireless) multi-hop communication network, and data flow is performed using scheduling and routing of sensing and actuation data. With the aim of rendering the system robust with respect to packet losses, we exploit network coding and redundancy in data communication (i.e. sending multiple copies of sensing and actuation data via multiple routing paths associated to possibly different delays) and assume that such data is re-combined as a weighed linear combination. The main contribution of this paper is to provide efficient computational methods to find the optimal choice of such weights either to maximize or to constrain a robustness metric based on the notion of Asymptotic Mean Square Stability. Since we observe that such metric induces an objective or constraint function that is highly non-linear, we propose an approximation that takes into account both the network parameters and the system dynamics: it is based on the assumption that packet loss probabilities are much smaller than correct transmission probabilities and leverages the well known Gerschgorin disks. We then compare our approximation with the optimal choice from the communication designer point of view, which neglects the system dynamics and is based on the idea of minimizing the quadratic error induced by the network on the actuation signal. The comparison of the above approximations quantitatively shows that the optimal choices from the points of view of control and communication theory are often at odds.

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Section: Introductionmentioning

confidence: 99%

Approximation methods for optimal network coding in a multi-hop control network with packet losses

Smarra

D’Innocenzo

Benedetto

2015

2015 European Control Conference (ECC)

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“…First assume a single channel system where only channel m * is available. Consider a suboptimal policy where at each time instant the sensor with the largest holding time is chosen to transmit, provided that this holding time is greater than some L > 2N [26]. Using an argument similar to the proof of Theorem 3 in [26], we can show that this policy has bounded average cost if…”

Section: B Stability Conditionmentioning

confidence: 99%

Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

et al. 2020

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This paper studies how to schedule wireless transmissions from sensors to estimate the states of multiple remote, dynamic processes. Sensors make observations of each of the processes. Information from the different sensors have to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. Such estimation problems routinely occur in large-scale Cyber-Physical Systems, especially when the dynamic systems (processes) involved are geographically separated. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant to decide which sensors have network access and which ones do not. To solve this scheduling problem, we formulate an associated Markov decision process (MDP). Further, we solve this MDP using a Deep Q-Network, a deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waitingtime, among others. Our algorithm is shown to significantly outperform these algorithms for randomly generated example scenarios.arXiv:1809.05149v1 [cs.SY]

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“…In [20], the sampling time is kept constant and the number of transmitted packets is increased after a loss. Unfortunately, this might temporarily overload the communication system, resulting in even more loss, see, e.g., [21], [22].…”

Section: Trading Loss Against Sampling Timementioning

confidence: 99%

Is it worth to retransmit lost packets in Networked Control Systems?

Blind

Allgöwer

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Recent publications showed that packet loss significantly reduces the performance of Networked Control Systems (NCS). One approach to increase the reliability of the communication system is to retransmit lost packets. Obviously, such retransmissions take time and thus require to increase the sampling time. Increasing the sampling time generally degrades the performance of a control system. Thus, we raise the question whether it is worth to increase the sampling time to allow more packet transmissions per sampling interval.

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Redundant data transmission in control/estimation over lossy networks

Cited by 57 publications

References 16 publications

Approximation methods for optimal network coding in a multi-hop control network with packet losses

Approximation methods for optimal network coding in a multi-hop control network with packet losses

Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

Is it worth to retransmit lost packets in Networked Control Systems?

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