18 research outputs found
Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
In many Cyber-Physical Systems, we encounter the problem of remote state
estimation of geographically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic 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. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to
decide which sensors have network access and which ones do not. To address this
scheduling problem, we formulate an associated Markov decision process (MDP).
This MDP is then solved using a Deep Q-Network, a recent 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-waiting-time, among others. Our algorithm is shown to significantly
outperform these algorithms for many example scenarios
Stochastic Sensor Scheduling via Distributed Convex Optimization
In this paper, we propose a stochastic scheduling strategy for estimating the
states of N discrete-time linear time invariant (DTLTI) dynamic systems, where
only one system can be observed by the sensor at each time instant due to
practical resource constraints. The idea of our stochastic strategy is that a
system is randomly selected for observation at each time instant according to a
pre-assigned probability distribution. We aim to find the optimal pre-assigned
probability in order to minimize the maximal estimate error covariance among
dynamic systems. We first show that under mild conditions, the stochastic
scheduling problem gives an upper bound on the performance of the optimal
sensor selection problem, notoriously difficult to solve. We next relax the
stochastic scheduling problem into a tractable suboptimal quasi-convex form. We
then show that the new problem can be decomposed into coupled small convex
optimization problems, and it can be solved in a distributed fashion. Finally,
for scheduling implementation, we propose centralized and distributed
deterministic scheduling strategies based on the optimal stochastic solution
and provide simulation examples.Comment: Proof errors and typos are fixed. One section is removed from last
versio
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
Real-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond
Efficient sampling and remote estimation are critical for a plethora of
wireless-empowered applications in the Internet of Things and cyber-physical
systems. Motivated by such applications, this work proposes decentralized
policies for the real-time monitoring and estimation of autoregressive
processes over random access channels. Two classes of policies are
investigated: (i) oblivious schemes in which sampling and transmission policies
are independent of the processes that are monitored, and (ii) non-oblivious
schemes in which transmitters causally observe their corresponding processes
for decision making. In the class of oblivious policies, we show that
minimizing the expected time-average estimation error is equivalent to
minimizing the expected age of information. Consequently, we prove lower and
upper bounds on the minimum achievable estimation error in this class. Next, we
consider non-oblivious policies and design a threshold policy, called
error-based thinning, in which each source node becomes active if its
instantaneous error has crossed a fixed threshold (which we optimize). Active
nodes then transmit stochastically following a slotted ALOHA policy. A
closed-form, approximately optimal, solution is found for the threshold as well
as the resulting estimation error. It is shown that non-oblivious policies
offer a multiplicative gain close to compared to oblivious policies.
Moreover, it is shown that oblivious policies that use the age of information
for decision making improve the state-of-the-art at least by the multiplicative
factor . The performance of all discussed policies is compared using
simulations. The numerical comparison shows that the performance of the
proposed decentralized policy is very close to that of centralized greedy
scheduling