169 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Recent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio
Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling with Minimal Knowledge
It is becoming increasingly clear that an important task for wireless
networks is to minimize the age of information (AoI), i.e., the timeliness of
information delivery. While mainstream approaches generally rely on the
real-time observation of user AoI and channel state, there has been little
attention to solve the problem in a complete (or partial) absence of such
knowledge. In this article, we present a novel study to address the optimal
blind radio resource scheduling problem in orthogonal frequency division
multiplexing access (OFDMA) systems towards minimizing long-term average AoI,
which is proven to be the composition of time-domain-fair clustered round-robin
and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic
solutions that are near-optimal as shown by simulation results are also
proposed to effectively improve the performance upon presence of various
degrees of extra knowledge, e.g., channel state and AoI.Comment: Accepted on 05.06.2021 by the IEEE Transactions on Wireless
Communications for publicatio
Estimation and stability of nonlinear control systems under intermittent information with applications to multi-agent robotics
This dissertation investigates the role of intermittent information in estimation and control problems and applies the obtained results to multi-agent tasks in robotics. First, we develop a stochastic hybrid model of mobile networks able to capture a large variety of heterogeneous multi-agent problems and phenomena. This model is applied to a case study where a heterogeneous mobile sensor network cooperatively detects and tracks mobile targets based on intermittent observations. When these observations form a satisfactory target trajectory, a mobile sensor is switched to the pursuit mode and deployed to capture the target. The cost of operating the sensors is determined from the geometric properties of the network, environment and probability of target detection. The above case study is motivated by the Marco Polo game played by children in swimming pools. Second, we develop adaptive sampling of targets positions in order to minimize energy consumption, while satisfying performance guarantees such as increased probability of detection over time, and no-escape conditions. A parsimonious predictor-corrector tracking filter, that uses geometrical properties of targets\u27 tracks to estimate their positions using imperfect and intermittent measurements, is presented. It is shown that this filter requires substantially less information and processing power than the Unscented Kalman Filter and Sampling Importance Resampling Particle Filter, while providing comparable estimation performance in the presence of intermittent information. Third, we investigate stability of nonlinear control systems under intermittent information. We replace the traditional periodic paradigm, where the up-to-date information is transmitted and control laws are executed in a periodic fashion, with the event-triggered paradigm. Building on the small gain theorem, we develop input-output triggered control algorithms yielding stable closed-loop systems. In other words, based on the currently available (but outdated) measurements of the outputs and external inputs of a plant, a mechanism triggering when to obtain new measurements and update the control inputs is provided. Depending on the noise environment, the developed algorithm yields stable, asymptotically stable, and Lp-stable (with bias) closed-loop systems. Control loops are modeled as interconnections of hybrid systems for which novel results on Lp-stability are presented. Prediction of a triggering event is achieved by employing Lp-gains over a finite horizon in the small gain theorem. By resorting to convex programming, a method to compute Lp-gains over a finite horizon is devised. Next, we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the control law with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, we investigate a decentralized output synchronization problem of heterogeneous linear systems. We develop a self-triggered output broadcasting policy for the interconnected systems. Broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell time condition that yields disturbance-to-state stable output synchronization in case of switching topology. Our approach is applicable to directed and unbalanced communication topologies.\u2
Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling
In this paper, we consider a remote inference system, where a neural network
is used to infer a time-varying target (e.g., robot movement), based on
features (e.g., video clips) that are progressively received from a sensing
node (e.g., a camera). Each feature is a temporal sequence of sensory data. The
learning performance of the system is determined by (i) the timeliness and (ii)
the temporal sequence length of the features, where we use Age of Information
(AoI) as a metric for timeliness. While a longer feature can typically provide
better learning performance, it often requires more channel resources for
sending the feature. To minimize the time-averaged inference error, we study a
learning and communication co-design problem that jointly optimizes feature
length selection and transmission scheduling. When there is a single
sensor-predictor pair and a single channel, we develop low-complexity optimal
co-designs for both the cases of time-invariant and time-variant feature
length. When there are multiple sensor-predictor pairs and multiple channels,
the co-design problem becomes a restless multi-arm multi-action bandit problem
that is PSPACE-hard. For this setting, we design a low-complexity algorithm to
solve the problem. Trace-driven evaluations suggest that the proposed
co-designs can significantly reduce the time-averaged inference error of remote
inference systems.Comment: 41 pages, 8 figures. The manuscript has been submitted to IEEE
Journal on Selected Areas in Information Theor
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