Timer-Based Distributed Channel Access in Networked Control Systems over Known and Unknown Gilbert-Elliott Channels

In this paper, we consider a system consisting of multiple (possibly heterogeneous) decoupled control subsystems which aim at communicating with their corresponding controllers via shared (possibly) time-varying wireless channels. To address the resource allocation problem in a distributed fashion, we propose a timer-based channel access mechanism in which the subsystem with the smallest timer value, in a channel, claims the slot for transmission in that specific channel. The value of the timer is inversely proportional to a cost which is a function of the temporal correlation in the channel variation and the subsystem state. This cost can be calculated individually and does not require explicit communication between the subsystems, since it is based on locally available information only. The temporal correlation in the channel variation may be unknown and, in such cases, each subsystem tries to deduce it via machine learning techniques. The performance of our proposed mechanism is demonstrated via simulations

Distributed Channel Access for Control Over Unknown Memoryless Communication Channels

We consider the distributed channel access problem for a system consisting of multiple control subsystems that close their loop over a shared wireless network. We propose a distributed method for providing deterministic channel access without requiring explicit information exchange between the subsystems. This is achieved by utilizing timers for prioritizing channel access with respect to a local cost which we derive by transforming the control objective cost to a form that allows its local computation. This property is then exploited for developing our distributed deterministic channel access scheme. A framework to verify the stability of the system under the resulting scheme is then proposed. Next, we consider a practical scenario in which the channel statistics are unknown. We propose learning algorithms for learning the parameters of imperfect communication links for estimating the channel quality and, hence, define the local cost as a function of this estimation and control performance. We establish that our learning approach results in collision-free channel access. The behavior of the overall system is exemplified via a proof-of-concept illustrative example, and the efficacy of this mechanism is evaluated for large-scale networks via simulations.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Distributed Channel Access for Control Over Known and Unknown Gilbert-Elliott Channels

We consider the distributed channel access problem for a system consisting of multiple control subsystems that close their loop over a shared wireless network with multiple channels subject to Markovian packet dropouts. Provided that an acknowledgement/negative-acknowledgement feedback mechanism is in place, we show that this problem can be formulated as a Markov decision process. We then transform this problem to a form that enables distributed control-aware channel access. More specifically, we show that the control objective can be minimized without requiring information exchange between subsystems as long as the channel parameters are known. The objective is attained by adopting a priority-based deterministic channel access method and the stability of the system under the resulting scheme is analyzed. Next, we consider a practical scenario in which the channel parameters are unknown and adopt a learning method based on Bayesian inference which is compatible with distributed implementation. We propose a heuristic posterior sampling algorithm which is shown to significantly improve performance via simulations

Control-Aware Distributed Channel Access for Networked Control Systems

Modern industrial control environments consist of a multitude of spatially distributed sensors and actuators that form various complex control loops. Networked control systems (NCSs) refer to such systems wherein the communication and information exchange between these distributed components happen over a shared communication network. NCSs offer several advantages over their traditional counterparts, such as higher flexibility and scalability with lower deployment and maintenance costs. However, they also introduce novel challenges that need to be addressed before their full potential can be realized. A major challenge is the limited capacity of the network which necessitates efficient sharing of the available communication resources among control loops based on their real-time needs. In this thesis, we first focus on wired NCSs and advocate using control-aware priority measures by using the concepts of cost of information loss (CoIL) and value of information (VoI). Another challenge is orchestrating channel access without a central network manager which we address by proposing a distributed priority-based channel access method. Next, we turn our attention to wireless NCSs (WNCSs) and consider memoryless erasure channels. Despite the additional complexities that arise due to the unreliability of channels, we prove that the aforementioned priority measures can be utilized for control-aware distributed channel access. We further investigate the stability of the system under the proposed channel access scheme and establish stability conditions. Next, we consider scenarios that necessitate the use of learning methods to evaluate transmission priorities and address how the learning can be done in a distributed way without compromising control performance. Data transmission in certain industrial environments is prone to correlated packet dropouts, which cannot be modeled by the memoryless channel. We investigate such scenarios by considering a more comprehensive channel model, i.e., a two-state Markov chain, and derive the resulting priority measures for control-aware channel access in such settings. The priorities are proved to be a function of the transition probabilities of the underlying channel model which can be unknown in practice. We thus consider practical scenarios without a priori knowledge of these parameters and develop a model-based Bayesian reinforcement learning algorithm to learn them in a distributed control-aware manner

Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels

In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possibly) unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power, and high power. The scheduling policy determines when to use low power or high power for data transmission over a fading channel with temporal correlation while satisfying the energy constraints. Although error-free acknowledgement/negative-acknowledgement (ACK/NACK) signals are provided by the remote estimator, they only provide meaningful information about the underlying channel state when low power is utilized. This leads to a partially observable Markov decision process (POMDP) problem and we derive conditions that preserve the optimality of a stationary schedule derived for its fully observable counterpart. However, implementing this schedule requires knowledge of the parameters of the GE model which are not available in practice. To address this, we adopt a Bayesian framework to learn these parameters online and propose an algorithm that is shown to satisfy the energy constraint while achieving near-optimal performance via simulation.Peer reviewe

Event-Triggered Control in Shared Networks: How the Computational Power of Sensors Affects Transmission Priorities

Publisher Copyright: © 2021 IEEE.In this paper, we study the prioritized transmission schemes for event-triggered wireless networked control systems (WNCSs) with smart (i.e., with computational power) or conventional (i.e., without computational power) sensors. When considering conventional sensors, the estimated state available to the controller is based on the intermittently received raw measurements. We show that the priority measure is associated with the statistical properties of the observations conforming with the cost of information loss (CoIL). Next, we consider the case of smart sensors, and despite the fact that CoIL can also be deployed, we deduce that it is more beneficial to use the available measurements as suggested by the value of information (VoI). The derived VoI incorporates the channel conditions and is compatible with distributed implementation. The impact of adopting each priority measure on the performance is evaluated via simulations.Peer reviewe

A Priority-Based Distributed Channel Access Mechanism for Control over CAN-like Networks

Funding Information: This work was supported by the Academy of Finland under Grant 320043. The work of T. Charalambous was supported by the Academy of Finland under Grant 317726. Publisher Copyright: © 2021 EUCA.In this work, we study the distributed channel access problem for multiple subsystems equipped with smart sensors sharing a capacity-limited communication network to perform their control tasks. We propose a fully distributed scheme to use the limited communication resources efficiently with respect to a quadratic cost which reflects the control performance. More specifically, we adopt a priority assignment scheme which consists of two layers: (i) a dynamic priority that corresponds to the time-varying criticality of transmission for each subsystem, and (ii) a pre-specified static priority which ensures that channel access is collision-free. We first demonstrate how the dynamic priorities can be manipulated to allocate the resources with respect to the chosen cost. Next, we propose a synchronization method which enables fully distribute implementation over controller area network (CAN) hardware. We validate the compatibility of our method with the mature hardware technology of CAN by hardware-in-the-loop simulations. Finally, we demonstrate the efficacy of our proposed scheme and evaluate its performance in large-scale networks via simulation.Peer reviewe

Distributed Channel Access for Control Over Unknown Memoryless Communication Channels

We consider the distributed channel access problem for a system consisting of multiple control subsystems that close their loop over a shared wireless network. We propose a distributed method for providing deterministic channel access without requiring explicit information exchange between the subsystems. This is achieved by utilizing timers for prioritizing channel access with respect to a local cost which we derive by transforming the control objective cost to a form that allows its local computation. This property is then exploited for developing our distributed deterministic channel access scheme. A framework to verify the stability of the system under the resulting scheme is then proposed. Next, we consider a practical scenario in which the channel statistics are unknown. We propose learning algorithms for learning the parameters of imperfect communication links for estimating the channel quality and, hence, define the local cost as a function of this estimation and control performance. We establish thatour learning approach results in collision-free channel access. The behavior of the overall system is exemplified via a proof-of-concept illustrative example, and the efficacyPeer reviewe

New regulatory mechanisms involved in FADD protein subcellular localization

La protéine FADD (Fas associated death domain) est l’adaptateur clé de la voie de signalisation apoptotique dépendante des récepteurs de mort de la famille du TNF (tumor necrosis factor). Au cours des dix dernières années, il est apparu évident qu’au-delà de son rôle majeur dans la mort cellulaire, FADD est impliqué dans d’autres processus biologiques comme le développement embryonnaire, la réponse immunitaire innée ou encore la progression du cycle cellulaire. De même, il est devenu clair que la localisation subcellulaire de FADD est déterminante pour sa fonction. Identifier les voies de régulation de l’expression de la protéine est donc d’une importance capitale. En 2008, notre équipe a mis en évidence, dans un modèle murin de la thyroϊde, un nouveau mécanisme de régulation de l’expression de la protéine via la sécrétion. En parallèle, le laboratoire a rapporté que la présence de FADD dans le sérum de patients cancéreux était corrélée à l’agressivité des tumeurs et l’inflammation. (Tourneur et al, 2012). L’objectif de ce travail de thèse était de comprendre le mécanisme par lequel FADD était sécrété, et de déterminer, le cas échéant, les modalités de sa régulation. Un troisième objectif est apparu au cours de la thèse : identifier de nouveaux modes de régulation de l’expression de FADD. Au moyen d’une lignée modèle humaine, nous avons montré que l’expression de la protéine humaine pouvait être régulée via une voie non-conventionnelle de sécrétion, tout comme dans le modèle murin. En parallèle de la caractérisation de cette sécrétion, nous avons montré que celle-ci pouvait être régulée par la kinase anti-apoptotique CK2 (casein kinase 2). Enfin, nous montrons que la CK2 régule la localisation nucléaire de FADD via une phosphorylation dépendante de la sous-unité régulatrice de la kinase et que la CK2 pourrait interagir directement avec FADD. Ces résultats constituent la première démonstration de la régulation de FADD par sécrétion par des cellules humaines et les premiers à rapporter un nouveau mode de régulation de la localisation subcellulaire de FADD par la CK2. Les conséquences de ces résultats en regard des fonctions connus de FADD sont discutéesThe FADD protein (Fas associated death domain) is the key adaptor molecule of the apoptotic signaling pathway triggered by death receptors of the TNF (Tumor necrosis factor) superfamily. During the last decade, it became obvious that, in addition to its major role in cell death, the protein was also involved in other biological processes like the embryonic development, the immune response or even cell cycle progression. Evidence also showed that the protein sub-cellular localization was a key determinant to its functions. Therefore, the identification of underlying regulatory mechanisms dictating FADD expression was of significant importance. In 2008 our laboratory identified, in a thyroid murine model, a new mechanism controlling FADD expression, namely via secretion. We discovered that the loss of FADD expression from tumor cells, by secretion, could be correlated to cancer aggressiveness as well as inflammation (Tourneur 2012). The goal of this thesis work was to apprehend the mechanism by which FADD was secreted and determine, in that case, the modalities of this regulation. A third objective of this work was to identify new potential regulatory pathways of FADD expression. By means of a human cell line model, we showed that, similarly to the mouse model, the expression of human FADD could be regulated via unconventional secretion. In parallel to the characterization of the secretory process itself, we demonstrated that secretion could be negatively regulated by the anti-apoptotic kinase CK2 (casein kinase 2). Finally, we showed that CK2 could regulate FADD nuclear localization via a regulatory sub-unit-dependent phosphorylation and that FADD and CK2 could directly interact. These results are the first to demonstrate that human FADD expression could be regulated via secretion and that FADD sub-cellular localization could be modulated by CK2. The consequences of such regulation with regards to known FADD functions are discusse

Power Allocation of Sensor Transmission for Remote Estimation Over an Unknown Gilbert-Elliott Channel

In this paper, we consider the problem of scheduling the power of a sensor when transmitting over an unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power and high power, which are to be selected for transmission over the channel in order to minimize a cost on the error covariance, while satisfying the energy constraints. The remote estimator provides error-free acknowledgement/negative-acknowledgement (ACK/NACK) messages to the sensor only when low power is utilized. We first consider the Partially Observable Markov Decision Process (POMDP) problem for the case of known GE channels and derive conditions for optimality of a stationary schedule. Next, a Bayesian inference approach is used through which the channels statistics are approximately learned when they are initially unknown. An algorithm is proposed in which the sensor adjusts its scheduling policy based on the energy constraint.Peer reviewe