Optimal Sensor Selection Research Articles

A Stochastic Sensor Selection Scheme for Sequential Hypothesis Testing With Multiple Sensors

We study the problem of binary sequential hypothesis testing using multiple sensors with associated observation costs. An off-line randomized sensor selection strategy, in which a sensor is chosen at every time step with a given probability, is considered. The objective of this work is to find a sequential detection rule and a sensor selection probability vector such that the expected total observation cost is minimized subject to constraints on reliability and sensor usage. First, the sequential probability ratio test is shown to be the optimal sequential detection rule in this framework as well. Efficient algorithms for obtaining the optimal sensor selection probability vector are then derived. In particular, a special class of problems in which the algorithm has complexity that is linear in the number of sensors is identified. An upper bound for the average sensor usage to estimate the error incurred due to Wald's approximations is also presented. This bound can be used to set a safety margin for guaranteed satisfaction of the constraints on the sensor usage.

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.

Optimal selection of SAW sensors for E-Nose applications

Electronic noses (E-Nose) are devices used to substitute human or canine olfactory systems in detecting gases or chemical substances. The success of an E-Nose in detecting a set of target gases depends on how optimal is the choice of the gas sensors. This paper proposes a novel algorithm for selection of an optimal set of surface acoustic wave (SAW) sensors for an E-Nose from a given set of available gas sensors. The sensor performance is quantified in terms of separability of data obtained from them. A similarity measure specifying how similar the responses of sensors are when exposed to a set of gases, is also defined. The sensor selection algorithm is then specified as an optimization problem in terms of separability of target gases and similarity of sensor responses. The advantage of the proposed method lies in its performance being independent of the choice of the pattern recognition engine.

Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation

In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy constraint. Two types of sensor collaboration problems are studied: minimum energy with an information constraint; and maximum information with an energy constraint. To solve the resulting sensor collaboration problems, we present tractable optimization formulations and propose efficient methods that render near-optimal solutions in numerical experiments. We also explore the situation in which there is a cost associated with the involvement of each sensor in the estimation scheme. In such situations, the participating sensors must be chosen judiciously. We introduce a unified framework to jointly design the optimal sensor selection and collaboration schemes. For a given estimation performance, we empirically show that there exists a trade-off between sensor selection and sensor collaboration.

Optimal Satellite Sensor Selection Utilized to Monitor the Impact of Urban Sprawl on the Thermal Environment in Doha City, Qatar

In the present study, the most appropriate Landsat and ASTER Image spatial and spectral resolutions were selected to extract thermal infrared data in Doha City, Qatar from 1990-2015. Results indicated Urban Heat Island (UHI) calculated from Landsat sensor data was more consistent with ground truthed temperatures. Landsat TM thermal infrared data with low spatial resolution (60-120 m) was more appropriate for large-scale thermal studies, but was not suitable to assess complex urban thermal environments and establish Land Surface Temperatures (LSTs) for individual buildings. However, ASTER sensor, with a Thermal Infrared (TIR) subsystem of 90 m spatial resolution, showed more accurate determination of thermal patterns and LSTs. Landsat data revealed relatively high temperatures, which exhibited increased congruence with ground truthed measures. In Doha City, LST showed an increased trend from 1990 to 2015; low in 1990, moderate in 2000, to very high in 2015. Furthermore, our results confirmed accelerated urban sprawl during 1990-2015. The evaluated areas increased from 80 km2 in 1990 to 179 km2 in 2015. A strong positive relationship was observed between UHI and urban area intensity. Urban planning absence significantly affected the rural environment. Urbanized areas exhibited high thermal conductivity and radiation heat budgets, characterized by increased atmospheric and surface temperatures compared to surrounding rural areas. Therefore, rapid urbanization rates must undergo proper planning and management regimes to insure positive impacts on the local climate and environment.

Optimal sensor selection strategies in the presence of wireless communication links

Abstract The problem of selecting in an optimal way κ sensors from a given set of K sensors providing noisy measurements of some physical variable has received a growing interest in the literature. The problem has been shown to be combinatorial, and several computable relaxations have been presented. In this paper, we consider a particularly interesting variant of the sensor selection problem. Motivated by the increase in the application of wireless sensor networks, i.e. networks of sensors which take remote measurements of the quantity of interest and then communicate their values through a (noisy) wireless communication link, we propose a scheme for optimally selecting the wireless sensors taking into account also the available channel state information. The optimality conditions are formally derived in an information-theoretic context, and specific semi-definite programming relaxations leading to computable techniques for large values of κ and K are presented. Also, we derive specific results for the cases of high and low signal-to-noise ratios. Numerical simulations show how knowledge of the channel state information may lead to an increase of the achievable mutual information, and determine a different choice of sensors.

Optimal sensor selection for noisy binary detection in stochastic pooling networks

Stochastic Pooling Networks (SPNs) are a useful model for understanding and explaining how naturally occurring encoding of stochastic processes can occur in sensor systems ranging from macroscopic social networks to neuron populations and nanoscale electronics. Due to the interaction of nonlinearity, random noise, and redundancy, SPNs support various unexpected emergent features, such as suprathreshold stochastic resonance, but most existing mathematical results are restricted to the simplest case where all sensors in a network are identical. Nevertheless, numerical results on information transmission have shown that in the presence of independent noise, the optimal configuration of a SPN is such that there should be partial heterogeneity in sensor parameters, such that the optimal solution includes clusters of identical sensors, where each cluster has different parameter values. In this paper, we consider a SPN model of a binary hypothesis detection task and show mathematically that the optimal solution for a specific bound on detection performance is also given by clustered heterogeneity, such that measurements made by sensors with identical parameters either should all be excluded from the detection decision or all included. We also derive an algorithm for numerically finding the optimal solution and illustrate its utility with several examples, including a model of parallel sensory neurons with Poisson firing characteristics.

Joint Optimal Sensor Selection and Scheduling in Dynamic Spectrum Access Networks

Spectrum sensing is key to the realization of dynamic spectrum access. To protect primary users' communications from the interference caused by secondary users, spectrum sensing must meet the strict detectability requirements set by regulatory bodies, such as the FCC. Such strict detection requirements, however, can hardly be achieved using PHY-layer sensing techniques alone with one-time sensing by only a single sensor. In this paper, we jointly exploit two MAC-layer sensing methods-cooperative sensing and sensing scheduling- to improve spectrum sensing performance, while incurring minimum sensing overhead. While these sensing methods have been studied individually, little has been done on their combinations and the resulting benefits. Specifically, we propose to construct a profile of the primary signal's RSSs and design a simple, yet near-optimal, incumbent detection rule. Based on this constructed RSS profile, we develop an algorithm to find 1) an optimal set of sensors; 2) an optimal point at which to stop scheduling additional sensing; and 3) an optimal sensing duration for one-time sensing, so as to make a tradeoff between detection performance and sensing overhead. Our evaluation results show that the proposed sensing algorithms reduce the sensing overhead by up to 65 percent, while meeting the requirements of both false-alarm and misdetection probabilities of less than 0.01.

Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.

Optimal sensor selection for ensuring diagnosability in labeled Petri nets

This paper studies the problem of optimal static sensor selection for ensuring diagnosability in labeled bounded and unbounded Petri nets. Starting from a non-diagnosable labeled Petri net system, we present a systematic procedure to design a new labeling function that makes the system diagnosable and optimizes a given objective function. This procedure employs a particular net, called Verifier Net, that is built from the original Petri net and provides necessary and sufficient conditions for diagnosability. We exploit the system structure captured in the verifier net to guide the search for the desired new labeling function. The search is performed over an unfolding of the reachability/coverability tree of the verifier net and follows a set of rules that capture the relabeling strategy. We allow for unobservable transitions that cannot be labeled as well as for multiple fault classes. We formulate an integer linear programming problem that finds an optimal labeling function when numerical costs are associated with transition relabeling.

Fault Diagnosis of Helical Coil Steam Generator Systems of an Integral Pressurized Water Reactor Using Optimal Sensor Selection

Fault diagnosis is an important area in nuclear power industry for effective and continuous operation of power plants. Fault diagnosis approaches depend critically on the sensors that measure important process variables. Allocation of these sensors determines the effectiveness of fault diagnostic methods. However, the emphasis of most approaches is primarily on the procedure to perform fault detection and isolation (FDI) given a set of sensors. Little attention has been given to actual allocation of the sensors for achieving efficient FDI performance. This paper presents a graph-based approach as a solution for optimization of sensor selection to ensure fault observability, as well as fault resolution to a maximum possible extent. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships among the measurements and to characterize by a data hyper-plane. Fault directions for the different fault scenarios are obtained using singular value decomposition of the prediction errors, and fault isolation is then accomplished from new projections on these fault directions. Results of the helical coil steam generator (HCSG) system of the International Reactor Innovative and Secure (IRIS) nuclear reactor demonstrate the proposed FDI approach with optimized sensor selection, and its future application to large industrial systems.

Optimal sensor and path selection for target tracking in wireless sensor networks

ABSTRACTThis paper addresses target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress according to a probabilistic state space model. Thus, we propose to improve the use of the quantized variational filtering by jointly selecting the optimal candidate sensor that participates in target localization and its best communication path to the cluster head. In the current work, firstly, we select the optimal sensor in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor networks. This selection is also based on the local cluster node density and their transmission power. Secondly, we select the best communication path that achieves the highest signal‐to‐noise ratio at the cluster head; then, we estimate the target position using quantized variational filtering algorithm. The best communication path is designed to reduce the communication cost, which leads to a significant reduction of energy consumption and an accurate target tracking. The optimal sensor selection is based on mutual information maximization under energy constraints, which is computed by using the target position predictive distribution provided by the quantized variational filtering algorithm. The simulation results show that the proposed method outperforms the quantized variational filtering under sensing range constraint, binary variational filtering, and the centralized quantized particle filtering. Copyright © 2012 John Wiley & Sons, Ltd.

PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks

Recent years have witnessed the shift of wireless sensor networks (WSNs) from theoretical research to practical applications. Due to their simplicity, range-based sensor networks have been widely used. To track a maneuvering target in range-based sensor networks, first, we derive the relationship between the multiple model posterior Cramér–Rao lower bound (PCRLB) and the distance from the sensor to the target, which forms the basis of choosing the subset of candidate sensors that may attend the incoming tracking event. Second, we design two optimization strategies under the communication constraint, namely the optimal sensor selection and cluster head selection. Third, we can estimate the state of the maneuvering target by making use of the interacting multiple model (IMM) algorithm and predict the model index one time step ahead. Last, simulation results show the effectiveness of the proposed schemes.

Optimal Sensor Selection for Ensuring Diagnosability in Labeled Bounded Petri Nets

This paper studies the problem of optimal static sensor selection for ensuring diagnosability in labeled bounded Petri nets. Starting from a non-diagnosable labeled Petri net system, we present a systematic procedure to design a new labeling function that makes the system diagnosable and optimizes a given objective function. This procedure employs a particular net, called Verifier Net, that is built from the original Petri net and provides necessary and sufficient conditions for diagnosability. We exploit the system structure captured in the verifier net to guide the search for the desired new labeling function. The search is performed over an unfolding of the reachability tree of the verifier net.

Stochastic Sensor Scheduling for Energy Constrained Estimation in Multi-Hop Wireless Sensor Networks

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense a dynamic process and transmit measured information over a shared channel to a central fusion center. The fusion center computes an estimate of the process state by means of a Kalman filter. In this technical note we assume that the WSN admits a tree topology with one fusion center at the root. At each time step only a subset of sensors can be selected to transmit observations to the fusion center due to a limited energy budget. We propose a stochastic sensor selection algorithm that randomly selects a subset of sensors according to a certain probability distribution, which is opportunely designed to minimize the asymptotic expected covariance matrix of the estimation error. We show that the optimal stochastic sensor selection problem can be relaxed into a convex optimization problem and thus efficiently solved. We also provide a possible implementation of our algorithm which does not introduce any communication overhead. The technical note ends with some numerical examples that show the effectiveness of the proposed approach.

Sensor Selection for Structural Observability in Discrete Event Systems Modeled by Petri Nets

This paper studies optimal sensor selection in discrete event systems modeled by partially observed Petri nets. The goal is to place a minimum number of sensors while maintaining structural observability, i.e., the ability to uniquely determine the system state at any given time step based on sensor information up to that time step, knowledge of the system model, and an arbitrary but known initial state. The problem is important because the majority of existing control schemes for Petri nets rely on complete knowledge of the system state at any given time step. To simplify the problem, we consider two subproblems: the optimal place sensor selection (OPSS) problem and the optimal transition sensor selection (OTSS) problem. The OPSS problem is shown to be computationally hard by establishing that the corresponding decision problem is NP -complete. For this reason, we first reduce the problem to the linear integer programming problem, which can be solved optimally using existing linear integer programming solvers (at least for small problem instances), and then propose two heuristic algorithms to approximate its solution with polynomial complexity. Simulations suggest that the two proposed heuristics run faster and can find reasonably good solutions when compared to optimal methods that are based on linear integer programming solvers. Unlike the OPSS problem, the OTSS problem is solvable with polynomial complexity.

Development of a Wireless Sensor Glove for Surgical Skills Assessment

Laparoscopic surgery is a challenging task in minimally invasive surgery, which involves complex instrument control, extensive manual dexterity, and hand-eye coordination. This requires a greater attention to training and skills evaluation. In order to provide a more objective skills assessment method, this paper presents a wireless sensor platform for the capture of laparoscopic hand gesture data and a hidden-Markov-model-based analysis framework for optimal sensor selection and placement. Detailed experimental validation is provided to illustrate how the proposed method can be used to assess surgical performance improvement over repeated training.

Network flow formulation of optimal perimeter sensory coverage problem

In this article, the perimeter detection optimization problem in field surveillance and target tracking are discussed. The detection range of sensors is assumed to be circular or elliptical. Sensors are also assumed to be associated with a cost factor reflecting their operational characteristics and power usage. We show that the problem of optimal sensor selection can be reduced to a network flow problem and can then be solved using any existing classical methodology. This significantly reduces the computational time of sensory selection problem which in many cases needs to be solved in almost real time basis, every time that the dynamics of the field changes. The field dynamics could change due to such events as wind direction change and sensor failures.

Design of multimedia surveillance systems

This article addresses the problem of how to select the optimal combination of sensors and how to determine their optimal placement in a surveillance region in order to meet the given performance requirements at a minimal cost for a multimedia surveillance system. We propose to solve this problem by obtaining a performance vector, with its elements representing the performances of subtasks, for a given input combination of sensors and their placement. Then we show that the optimal sensor selection problem can be converted into the form of Integer Linear Programming problem (ILP) by using a linear model for computing the optimal performance vector corresponding to a sensor combination. Optimal performance vector corresponding to a sensor combination refers to the performance vector corresponding to the optimal placement of a sensor combination. To demonstrate the utility of our technique, we design and build a surveillance system consisting of PTZ (Pan-Tilt-Zoom) cameras and active motion sensors for capturing faces. Finally, we show experimentally that optimal placement of sensors based on the design maximizes the system performance.

Optimal Sensor Selection in Binary Heterogeneous Sensor Networks

We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric Kullback-Leibler divergence, or J -divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is ldquowinner takes allrdquo like. The sensor class with the best performance/cost ratio is selected.