A complete greedy algorithm for infinite-horizon sensor scheduling

Asghar, Ahmad Bilal; Jawaid, Syed Talha; Smith, Stephen L.

doi:10.1016/j.automatica.2017.04.018

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…Early detection of dangerous conditions is a critical factor in mitigating damages to ecological systems and human infrastructure [5]. Persistent monitoring methods have utilized satellite data, human-piloted aircraft, and networks of stationary sensors, optimizing their placement for a variety of resource constraints [6][7][8][9]. Satellite data is often at too low resolution (e.g.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications

Ostertag

Atanasov

Rosing

2022

J Intell Robot Syst

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This paper considers persistent monitoring of environmental phenomena using unmanned aerial vehicles (UAVs). The objective is to generate periodic dynamically feasible UAV trajectories that minimize the estimation uncertainty at a set of points of interest in the environment. We develop an optimization algorithm that iterates between determining the observation periods for a set of ordered points of interest and optimizing a continuous UAV trajectory to meet the required observation periods and UAV dynamics constraints. The interest-point visitation order is determined using a Traveling Salesman Problem (TSP), followed by a greedy optimization algorithm to determine the number of observations that minimizes the maximum steady-state eigenvalue of a Kalman filter estimator. Given the interest-point observation periods and visitation order, a minimum-jerk trajectory is generated from a bi-level optimization, formulated as a convex quadratically constrained quadratic program. The resulting B-spline trajectory is guaranteed to be feasible, meeting the observation duration, maximum velocity and acceleration, region enter and exit constraints. The feasible trajectories outperform existing methods by achieving comparable observability at up to 47% higher travel speeds, resulting in lower maximum estimation uncertainty.

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Section: Introductionmentioning

confidence: 99%

Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications

Ostertag

Atanasov

Rosing

2022

J Intell Robot Syst

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“…Some other works modeled the sensor scheduling problem as static sensor selection problems, resulting in an optimization problem in an Euclidean space with integer constraints. They either found a convex approximation of the original problem [18] or used some greedy based heuristics to find a suboptimal policy with theoretical performance bound [19]. Although efficient algorithms can be developed from approximated models, the gap between the approximated policy and the optimal policy can be significant.…”

Section: Introductionmentioning

confidence: 99%

“…The sensors transmit data based on system clock and predetermined timing. The periodic policy [16], [17] and static sensor selection [18], [19] aforementioned are in this category. Besides offline scheduling, a large number of works were devoted to optimal online scheduling.…”

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confidence: 99%

Optimal Scheduling of Multiple Sensors Over Lossy and Bandwidth Limited Channels

Ding

Cheng

et al. 2020

IEEE Trans. Control Netw. Syst.

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This work considers the sensor scheduling for multiple dynamic processes. We consider n linear dynamic processes. The state of each process is measured by a sensor, which transmits its local state estimate over one wireless channel to a remote estimator with certain communication costs. At each time step, only a portion of the sensors are allowed to transmit data to the remote estimator and the packet might be lost due to unreliability of the wireless channels. Our goal is to find a scheduling policy which coordinates the sensors in a centralized manner to minimize the total expected estimation error of the remote estimator and the communication costs. We formulate the problem as a Markov decision process. We develop an algorithm to check whether there exists a deterministic stationary optimal policy. We show the optimality of monotone policies, which saves the computational effort of finding an optimal policy and facilitates practical implementation. Nevertheless, obtaining an exact optimal policy still suffers from curse of dimensionality when the number of processes is large. We further provide an index-based heuristic to avoid brute force computation. We derive analytic expressions of the indices and show that this heuristic is asymptotically optimal. Numerical examples are presented to illustrate the theoretical results.Index Terms-Kalman filtering; Sensor scheduling; lossy network; monotone policy; Markov decision process; index policy I. INTRODUCTIONThe development of device, sensing and communication technologies enables wide range of applications of wireless sensor networks. After the pioneering work on event-based sensor data scheduling proposed in [1], a variety of studies has been done to balance the estimation performance and the communication overhead in [2]- [4].A large number of works on sensor scheduling focused on remote estimation of a linear time-invariant (LTI) dynamic process. There are also some other works addressing static processes and nonlinear models. However, the static models [5], [6] are special cases of LTI systems and nonlinear models either involve approximation of a linear system [7], [8] or the solution method requires numerically solving a partially observable Markov decision process, which is computationally inefficient [9]-[11]. A few works [12], [13] considered control

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“…It has been shown in [5] that for linear state-space models, one can prune branches for the search tree and significantly reduce computational time. [20,4,21,22] followed the idea in [5] to develop practical algorithms for environmental monitoring and searching applications. However, these algorithms are implemented in real-time at the cost of falling back to myopic scheduling strategies, and myopic strategy is not desired in city-scale environmental monitoring which would be shown in Section 4.…”

Section: Introductionmentioning

confidence: 99%

Real-time Mobile Sensor Management Framework for city-scale environmental monitoring

Qian,

Claudel

2020

Preprint

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Environmental disasters such as flash floods are becoming more and more prevalent and carry an increasing burden to human civilization. They are usually unpredictable, fast in development and extend across large geographical areas. The consequences of such disasters can be reduced through better monitoring, for example using mobile sensing platforms that can give timely and accurate information to first responders and the public. Given the extended scale of the areas to monitor, and the time-varying nature of the phenomenon, we need fast algorithms to quickly determine the best sequence of locations to be monitored. This problem is very challenging: the present informative mobile sensor routing algorithms are either short-sighted or computationally demanding when applied to large scale systems. In this paper, a real-time sensor task scheduling algorithm that suits the features and needs of city-scale environmental monitoring tasks is proposed. The algorithm is run in forward search and makes use of the predictions of an associated distributed parameter system, modeling flash flood propagation. It partly inherits the causal relation expressed by a search tree, which describes all possible sequential decisions. The computationally heavy data assimilation steps in the forward search tree are replaced by functions dependent on the covariance matrix between observation sets. Taking flood tracking in an urban area as a concrete example, numerical experiments in this paper indicate that this scheduling algorithm can achieve better results than myopic planning algorithms and other heuristics based sensor placement algorithms. Furthermore, this paper relies on a deep learning-based data-driven model to track the system states, and experiments suggest that popular estimation techniques have very good performance when applied to precise data-driven models. The data and code can be freely downloaded from * .

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A complete greedy algorithm for infinite-horizon sensor scheduling

Cited by 19 publications

References 16 publications

Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications

Trajectory Planning and Optimization for Minimizing Uncertainty in Persistent Monitoring Applications

Optimal Scheduling of Multiple Sensors Over Lossy and Bandwidth Limited Channels

Real-time Mobile Sensor Management Framework for city-scale environmental monitoring

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