Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).

Resilience Assessment of Water Quality Sensor Designs under Cyber-Physical Attacks

Water distribution networks (WDNs) are critical infrastructure for the welfare of society. Due to their spatial extent and difficulties in deployment of security measures, they are vulnerable to threat scenarios that include the rising concern of cyber-physical attacks. To protect WDNs against different kinds of water contamination, it is customary to deploy water quality (WQ) monitoring sensors. Cyber-attacks on the monitoring system that employs WQ sensors combined with deliberate contamination events via backflow attacks can lead to severe disruptions to water delivery or even potentially fatal consequences for consumers. As such, the water sector is in immediate need of tools and methodologies that can support cyber-physical quality attack simulation and vulnerability assessment of the WQ monitoring system under such attacks. In this study we demonstrate a novel methodology to assess the resilience of placement schemes generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT) and evaluated under cyber-physical attacks simulated using the stress-testing platform RISKNOUGHT, using multidimensional metrics and resilience profile graphs. The results of this study show that some sensor designs are inherently more resilient than others, and this trait can be exploited in risk management practices.

Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

Although a lot of different types of sensors are available in the market only a limited number of sensors can be installed on a structure. Proper placement of these sensors plays a vital role in effectively achieving the objectives of a monitoring system. Sensor placement becomes especially critical in the case of bridges where the applied loading keeps on changing its location. A sensor layout that provides good quality structural response estimates for a given applied loading may not yield acceptable results for a different loading arrangement. Further, usually different types of sensors are installed on a structure e.g. strain gauges and accelerometers. These sensors measure different physical quantities having different units and orders of magnitude thus cannot be easily incorporated in a sensor placement optimization (SPO) process. So, this research work proposes a multi-objective sensor placement optimization approach that can effectively deal with different types of sensor measurements and spatially varying loading such as in the case of bridges. The proposed method employs an augmented Kalman filter (AKF) for structural response estimation and a multi-objective genetic algorithm for SPO. The AKF can effectively estimate structural response using a few heterogeneous noisy measurements while incorporating the modeling error. The effectiveness of the proposed method is demonstrated using a numerical example of a 3D truss bridge structure. The results show that the proposed multi-objective optimization method yields a sensor arrangement that remains effective against spatially varying dynamic loading.

Sensor placement optimization for critical-grid coverage problem of indoor positioning

It is more practical and efficient to deploy sensors in critical areas rather than common areas to ensure indoor positioning accuracy and reduce deployment cost. This study focused on the sensor placement optimization for critical-grid coverage problem with two objectives: accuracy and cost. After reviewing some related works, this article proposed a multi-objective optimization model for critical-grid coverage problem of indoor positioning considering k-coverage problem as well as the topological rationality of sensor distribution. Then, NSGA-II algorithm was used to solve the optimizing model of sensor placement. At last, the simulation experiment and real environment validation were conducted for proposed method. The results showed that the optimized schemes obtain a lower error (1.13, 1.21 m) and a higher reduction of sensor deployment cost than the uniform deployment scheme (1.44 m). As a conclusion, the proposed method could reduce the cost of sensor deployment while ensuring the accuracy of indoor positioning for critical areas. It also provides a new direction for improving the accuracy of indoor positioning.