Sensor Placement Problem Research Articles

Optimizing spatiotemporal sensors placement for nutrient monitoring: a stochastic optimization framework

Nutrient monitoring is very important for the area of food–energy–water nexus. The sensor network for nutrient monitoring requires dynamic sensing where the positions of the sensors change with time. In this work, we have proposed a methodology to optimize a dynamic sensor network which can address the spatiotemporal aspect of nutrient movement in a watershed. This is a first paper in the series where an algorithmic and methodological framework for spatiotemporal sensor placement problem is proposed. Dynamic sensing is widely used in wireless sensors, and the current approaches to solving this problem are data intensive. This is the first time we are introducing a stochastic optimization approach to dynamic sensing which is efficient. This framework is based on a novel stochastic optimization algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). A small case study of the dynamic sensor placement problem is presented to illustrate the approach. In the second paper of this series, we will present a detailed case study of nutrient monitoring in a watershed.

A global stochastic programming approach for the optimal placement of gas detectors with nonuniform unavailabilities

Optimal design of a gas detection systems is challenging because of the numerous sources of uncertainty, including weather and environmental conditions, leak location and characteristics, and process conditions. Rigorous CFD simulations of dispersion scenarios combined with stochastic programming techniques have been successfully applied to the problem of optimal gas detector placement; however, rigorous treatment of sensor failure and nonuniform unavailability has received less attention. To improve reliability of the design, this paper proposes a problem formulation that explicitly considers nonuniform unavailabilities and all backup detection levels. The resulting sensor placement problem is a large-scale mixed-integer nonlinear programming (MINLP) problem that requires a tailored solution approach for efficient solution. We have developed a multitree method which depends on iteratively solving a sequence of upper-bounding master problems and lower-bounding subproblems. The tailored global solution strategy is tested on a real data problem and the encouraging numerical results indicate that our solution framework is promising in solving sensor placement problems. This paper was selected for the special issue in JLPPI from the 2016 International Symposium of the MKO Process Safety Center.

Compressed sensing based optimal sensor placement for leak localization in water distribution networks

Abstract The performance of model-based leak detection and localization techniques heavily depends on the configuration of a limited number of sensors. This paper presents a sensor placement optimization strategy that guarantees sufficient diagnosability while satisfying the budget constraint. Based on the theory of compressed sensing, the leak localization problem could be transformed into acquiring the sparse leak-induced demands from the available measurements, and the average mutual coherence is devised as a diagnosability criterion for evaluating whether the measurements contain enough information for identifying the potential leaks. The optimal sensor placement problem is then reformulated as a {0, 1} quadratic knapsack problem, seeking an optimal sensor placement scheme by minimizing the average mutual coherence to maximize the degree of diagnosability. To effectively handle the complicated real-life water distribution networks, a validated binary version of artificial bee colony algorithm enhanced by genetic operators, including crossover and swap, is introduced to solve the binary knapsack problem. The proposed strategy is illustrated and validated through a real-life water distribution network with synthetically generated field data.

Sensor placement in active multistatic sonar networks

The idea of deploying noncollocated sources and receivers in multistatic sonar networks (MSNs) has emerged as a promising area of opportunity in sonar systems. This article is one of the first to address point coverage problems in MSNs, where a number of points of interest have to be monitored in order to protect them from hostile underwater assets. We consider discrete “definite range” sensors as well as various diffuse sensor models. We make several new contributions. By showing that the convex hull spanned by the targets is guaranteed to contain optimal sensor positions, we are able to limit the solution space. Under a definite range sensor model, we are able to exclude even more suboptimal solutions. We then formulate a nonlinear program and an integer nonlinear program to express the sensor placement problem. To address the nonconvex single-source placement problem, we develop the Divide Best Sector (DiBS) algorithm, which quickly provides an optimal source position assuming fixed receivers. Starting with a basic implementation of DiBS, we show how incorporating advanced sector splitting methods and termination conditions further improve the algorithm. We also discuss two ways to use DiBS to find multiple source positions by placing sensors iteratively or simultaneously. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 287–304, 2017

Decentralized Message Passing Algorithm for Distributed Minimum Sensor Cover

This paper addresses a decentralized message passing approach for a sensor placement problem in a continuous plane, named minimum sensor cover problem: given pointwise tasks and omnidirectional coverage of sensors deployable by distributed local planners, find the minimum set of sensors that cover all the tasks. Because the solution is required to be derived in the local planners’ network, a decentralized message passing algorithm is proposed with a problem conversion process into a discrete domain for applying the algorithm. By using discretization of the solution space, the minimum sensor cover problem can be converted to a combinatorial optimization (i.e., the geometric set cover), and then it is formulated as a maximum a posteriori state assignment problem. Belief propagation is a decentralized algorithm based on local iterative message passing to solve the maximum a posteriori state assignment problem, but it has convergence and feasibility issues. Therefore, the proposed algorithm is modified from the belief propagation to obtain stable and guaranteed feasible solutions, and its time complexity in a decentralized computation is analyzed. Numerical simulations validate the convergence, feasibility, and preferable solution quality of the proposed algorithm against existing variants of belief propagation and the centralized greedy algorithm for the set cover.

Accurate and Optimal Sensor Placement for Source Identification of Water Distribution Networks

AbstractThe problem of sensor placement for early-warning detection systems is a topical issue for industry and utilities who want to equip their networks with such technology. Solving the problem ...

A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems

Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach.

Acceleration sensor placement technique for vibration test in structural health monitoring using microhabitat frog-leaping algorithm

In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In addition, sensor configuration should be calculated automatically to facilitate engineers. However, most of the previous methods focus on the sensor placement of 1-axis sensors. Then, the 3-axis acceleration sensors are calculated by the method of 1-axis sensors, which results in non-optimal placement of many 3-axis acceleration sensors. Moreover, the calculation precisions and efficiencies of most of the previous methods cannot meet the requirement of practical engineering. In this work, the microhabitat frog-leaping algorithm is proposed to solve the optimal sensor placement problems of 3-axis acceleration sensors. The computation precision and efficiency are improved by microhabitat frog-leaping algorithm. Finally, microhabitat frog-leaping algorithm is applied and compared with other algorithms using Dalian South Bay Cross-sea Bridge.

Joint Placement of Transmitters and Receivers for Distributed MIMO Radars

Geometry configuration is crucial to the system performance in distributed multiple-input–multiple-output (MIMO) radars. Sensor placement in distributed MIMO radars is different from the conventional sensor placement problem as transmitters and receivers need to cooperate with each other to perform sensing. In this paper, we establish a combinatorial optimization model for the joint placement of transmitters and receivers. The proposed algorithm first transforms the original model into an equivalent model with convex constraints via convex relaxation. Then, the nonconvex objective function is further replaced with a new convex surrogate. The surrogate is shown to converge to a good approximation of the original optimal solution. The performance of the proposed algorithm is validated using numerical simulations.

Optimisation of numbers, types, and locations of wireless sensors with communication, finite supply, and multiple-sensor coverage constraints

The sensor placement problem considered in this paper is to determine optimal sensor configurations satisfying coverage preferences at a minimal cost. This problem is formulated as the binary linear programming problem. The generic formulation is generalised to include several realistic demands, such as a finite sensor supply, single hop wireless sensor communication, and multiple-sensor overlapping coverage. Communication and detection are considered in a probabilistic framework. An exact solution to this problem is computationally demanding (NP-complete) and practically impossible for realistic scenarios. Therefore, a fast algorithm for approximate solutions is used. To evaluate the quality of the approximate solutions, they are compared with exact solutions for cases when the exact solutions are obtainable. The results demonstrate that the approximate solutions are highly satisfactory regardless of the complexity of problem constraints, while an exact algorithm can be impractically slow.

Influence of multi-valued diagnostic signals on optimal sensor placement

In this paper the comparison of results of optimal sensor placement with the use of binary and multi-valued diagnostic signals is given. The exoneration assumption was introduced and its effects were discussed. The influence of multi-valued diagnostic signals on several fault isolability metrics were discussed. The optimal sensor placement problem under budgetary constraints is formulated. A branch-and-bound algorithm solving this problem is described. It is used in context of three tanks system with 25 possible diagnostic signals.

Efficient Sensor Placement for Leak Localization Considering Uncertainties

Losses in water distribution systems can be between several percent in well maintained systems up to more than 50 percent in developing countries. Most of the losses originate from leaks. Therefore, a fast detection and localization of leaks is crucial for effectively reducing this losses in water distribution networks. Model-based leak localization has become increasingly popular in recent years. Certainly, the performance of these methods is linked to 1) the measurement locations in the system and 2) uncertainties at these locations. This paper provides a methodology that incorporates uncertainties of different types and sources in the optimal sensor placement problem for leak localization shown by the example of the effect of demand uncertainties on potential pressure measurement points. The problem is solved for different numbers of sensors and different strengths of uncertainties are taken into account. Additionally, to describe the relation between the number of sensors and the leak localization quality, a cost-benefit function is derived based on the different sensor placement results and GoF statistics. It was found that the function follows a power law. Results show that incorporating uncertainties leads to other optimal positions than without uncertainties, but the power law behavior still stays true. Additionally, more sensors are needed than without uncertainties.

Sensor Configuration Optimizing in Modal Identification by Siege ant Colony Algorithm

AbstractProper monitor planning is a vital component of structural health monitoring (SHM) project. An extremely important part of the monitor planning is the placement of sensors, usually in the form of acceleration sensors. For the placement of three-dimensional acceleration sensors, the state of practice is to select the sensor configuration by previous experiences. However, this results in a waste of many sensors. A novel method called siege ant colony algorithm (SAC) is proposed in this paper. This method is built on the previous ant colony optimization (ACO) in the direction of improving efficiency and accuracy when applied to optimal sensor placement (OSP) problems in large-scale structure monitoring. This method is applied and compared with standard approaches using the Hanjiang transmission tower.

A survey on sensor placement for contamination detection in water distribution systems

Frequent water pollution incidents have occurred recently, leading to severe damages, economic loss, and long-lasting society impact. Therefore, installation of water quality monitoring sensors in water distribution system (WDS) has been advocated as a viable solution to enable real-time pollution detection and thus the mitigation of the risks associated with catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy the sensors within the distribution system while the network still gets covered. Although there exist a significant number of articles on sensor placement, WDS for contamination detection is unique comparing to other networks such as power grids, road networks, structural networks and microwave radio networks. In this paper, we conduct a comprehensive literature survey on the sensor placement problem for contamination detection in WDS, with a special focus on optimization strategies and framework. Current challenges, issues, and research directions are also identified and discussed.

Direction-finding arrays of directional sensors for randomly located sources

The problem of directional sensor placement and orientation is considered when statistical information about the source direction of arrival is available. We focus on two-sensor arrays and form a cost function based on the Cramer–Rao bound that depends on the probability distribution of the coplanar source direction. Proper positioning and orientation of the sensors enable the two-sensor array to have an accuracy comparable to that of a three- or four-sensor uniform circular array.

Sensor placement for fault location identification in water networks: A minimum test cover approach

This paper focuses on the optimal sensor placement problem for the identification of pipe failure locations in large-scale urban water systems. The problem involves selecting the minimum number of sensors such that every pipe failure can be uniquely localized. This problem can be viewed as a minimum test cover (MTC) problem, which is NP-hard. We consider two approaches to obtain approximate solutions to this problem. In the first approach, we transform the MTC problem to a minimum set cover (MSC) problem and use the greedy algorithm that exploits the submodularity property of the MSC problem to compute the solution to the MTC problem. In the second approach, we develop a new augmented greedy algorithm for solving the MTC problem. This approach does not require the transformation of the MTC to MSC. Our augmented greedy algorithm provides in a significant computational improvement while guaranteeing the same approximation ratio as the first approach. We propose several metrics to evaluate the performance of the sensor placement designs. Finally, we present detailed computational experiments for a number of real water distribution networks.

Polygon guarding with orientation

The art gallery problem is a classical sensor placement problem that asks for the minimum number of guards required to see every point in an environment. The standard formulation does not take into account self-occlusions caused by a person or an object within the environment. Obtaining good views of an object from all orientations despite self-occlusions is an important requirement for surveillance and visual inspection applications. We study the art gallery problem under a constraint, termed △-guarding, that ensures that all sides of any convex object are always visible in spite of self-occlusion.Our contributions in this paper are three-fold: We first prove that Ω(n) guards are always necessary for △-guarding the interior of a simple polygon having n vertices. Second, we present a O(log⁡copt) factor approximation algorithm for △-guarding polygons with or without holes, when the guards are restricted to vertices of the polygon. Here, copt is the optimal number of guards. Third, we study the problem of △-guarding a set of line segments connecting points on the boundary of the polygon. This is motivated by applications where an object or person of interest can only move along certain paths in the polygon. We present a constant factor approximation algorithm for this problem – one of the few such results for art gallery problems.

Optimal Sensor Placement for Acoustic Underwater Target Positioning With Range-Only Measurements

This paper addresses the problem of optimal acoustic sensor placement for underwater target localization in 3-D using range measurements only. By adopting an estimation theoretical framework, the optimal geometric sensor formation that will yield the best achievable performance in terms of target positioning accuracy is computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM). For mathematical tractability, it is assumed that the measurements of the ranges between the target and a set of acoustic sensors are corrupted with white Gaussian noise. For the sake of completeness, an explicit analytical result for a generic $n$ -sensor network is first obtained for the case when there is no uncertainty in the prior knowledge about the target position. The result is then extended to the practical case where the target is known to lie inside a region of uncertainty. The optimal sensor configuration thus obtained lends itself to an interesting and useful geometrical interpretation. In addition, the “spreading” of the configuration is shown to depend on the number of range measurements, target depth, and the probability distribution function that characterizes the prior knowledge about the target position. Results are also obtained for the problem of optimal sensor placement with constraints, namely, by considering that the sensors can be either located at the sea surface or distributed between the surface and the seabed. The connection between 2-D and 3-D scenarios is clarified. Simulation examples illustrate the key results derived.

On optimal sensing and actuation design for an industrial scale steam methane reformer furnace

The spatial temperature distribution in the highly energy‐intensive furnace unit in a steam reforming‐based hydrogen manufacturing plant determines the energy efficiency of the plant. While the fuel distribution among the burners can be manipulated to control the furnace temperature distribution, adequate temperature measurements is a prerequisite. Typical furnaces have hundreds of tubes and burners, and economic considerations dictate that the number of temperature sensors and flow actuators required for automatic temperature optimization be minimized. In this article, we investigate several formulations for the design of the optimal sensor and actuation configurations for an industrial furnace. We initially formulate the optimal sensor placement problem as a bi‐level optimization problem, and exploit the problem structure to obtain an equivalent mixed‐integer linear program formulation. We then provide an extension to the combined sensor and actuator placement. We demonstrate the efficacy of our approach through simulation case studies based on industrial data. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3225–3237, 2016

Travel time estimation oriented freeway sensor placement problem considering sensor failure

ABSTRACTOptimal sensor placement on freeway corridor is of great interest to transportation authorities. However, current traffic sensors are easily subject to various failures. Therefore, it is necessary to incorporate sensor failure into the optimal sensor placement model. In this article, a two-stage stochastic model is proposed for the purpose of travel time estimation on freeway corridor. To balance the effectiveness and reliability, a stochastic conditional value at risk (CVaR) model is also proposed. Since both models are too complicated, a customized genetic algorithm is developed. Numerical experiments show that considering sensor failure makes a significant performance improvement in the sensor placement pattern. Sensitivity analysis is also applied to investigate the impact of a number of allowable sensors and different traffic sensor failure probability.