An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

Distributed detection, localization, and estimation in time-critical wireless sensor networks

In this thesis the problem of distributed detection, localization, and estimation (DDLE) of a stationary target in a fusion center (FC) based wireless sensor network (WSN) is considered. The communication process is subject to time-critical operation, restricted power and bandwidth (BW) resources operating over a shared communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm is proposed to solve the DDLE problem consisting of two dependent stages: distributed detection and distributed estimation. The WSN performs distributed detection first and based on the global detection decision the distributed estimation stage is performed. The communication between the SNs and the FC occurs over a shared channel via a slotted Aloha MAC protocol to conserve BW. In distributed detection, hard decision fusion is adopted, using the counting rule (CR), and sensor censoring in order to save power and BW. The effect of Rayleigh fading on distributed detection is also considered and accounted for by using distributed diversity combining techniques where the diversity combining is among the sensor nodes (SNs) in lieu of having the processing done at the FC. Two distributed techniques are proposed: the distributed maximum ratio combining (dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show superior detection performance when compared to conventional diversity combining procedures that take place at the FC. In distributed estimation, the segmented distributed localization and estimation (SDLE) framework is proposed. The SDLE enables efficient power and BW processing. The SOLE hinges on the idea of introducing intermediate parameters that are estimated locally by the SNs and transmitted to the FC instead of the actual measurements. This concept decouples the main problem into a simpler set of local estimation problems solved at the SNs and a global estimation problem solved at the FC. Two algorithms are proposed for solving the local problem: a nonlinear least squares (NLS) algorithm using the variable projection (VP) method and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS), NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm combination delivers the best localization and estimation performance. In fact , the target can be localized with less than one meter error. The SNs send their local estimates to the FC over a shared channel using the slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel. However, Aloha is known for its relatively high medium access or contention delay given the medium access probability is poorly chosen. This fact significantly hinders the time-critical operation of the system. Hence, multi-packet reception (MPR) is used with slotted Aloha protocol, in which several channels are used for contention. The contention delay is analyzed for slotted Aloha with and without MPR. More specifically, the mean and variance have been analytically computed and the contention delay distribution is approximated. Having theoretical expressions for the contention delay statistics enables optimizing both the medium access probability and the number of MPR channels in order to strike a trade-off between delay performance and complexity

SELF ORGANIZING WIRELESS SENSOR NETWORKS

This dissertation is concerned with the properties of self-organizing network systems, where a large number of distributed sensor nodes with limited sensing, processing and communication capability organize themselves into a cooperative network without any centralized control or management. Due to the distributed nature of the management and lack of global information for in-node decision making, sensor management in such networks is a complicated task. The dynamics of such networks are characterized by constraints and uncertainty, and the presence of disturbances that significantly affect aggregate system behavior. In this dissertation we examine several important topics in the management of self-organizing wireless sensor networks. The first topic is a statistical analysis to determine the minimum requirements for the deployment phase of a random sensor network to achieve a desired degree of coverage and connectivity. The second topic focuses on the development of a viable online sensor management methodology in the absence of global information. We consider consensus based sensor data fusion as a motivating problem to demonstrate the capability of the sensor management algorithms. The approach that has been widely investigated in the literature for this problem is the fusion of information from all the sensors. It does not involve active control of the sensors as part of the algorithm. Our approach is to control the operations of the nodes involved in the consensus process by associating costs with each node to emphasize those with highest payoff. This approach provides a practical, low complexity algorithm that allows the nodes to optimize their operations despite the lack of global information. In the third topic we have studied sensor networks that include "leaders," "followers," and "disrupters." The diffusion of information in a network where there are conflicting strategies is investigated through simulations. These results can be used to develop algorithms to manage the roles in the network in order to optimize the diffusion of information as well as protect the network against disruption

Machine learning algorithms for fluid mechanics

Neural networks have become increasingly popular in the field of fluid dynamics due to their ability to model complex, high-dimensional flow phenomena. Their flexibility in approximating continuous functions without any preconceived notion of functional form makes them a suitable tool for studying fluid dynamics. The main uses of neural networks in fluid dynamics include turbulence modelling, flow control, prediction of flow fields, and accelerating high-fidelity simulations. This thesis focuses on the latter two applications of neural networks. First, the application of a convolutional neural network (CNN) to accelerate the solution of the Poisson equation step in the pressure projection method for incompressible fluid flows is investigated. The CNN learns to approximate the Poisson equation solution at a lower computational cost than traditional iterative solvers, enabling faster simulations of fluid flows. Results show that the CNN approach is accurate and efficient, achieving significant speedup in the Taylor-Green Vortex problem. Next, predicting flow fields past arbitrarily-shaped bluff bodies from point sensor and plane velocity measurements using neural networks is focused on. A novel conformal-mapping-aided method is devised to embed geometry invariance for the outputs of the neural networks, which is shown to be critical for achieving good performance for flow datasets incorporating a diverse range of geometries. Results show that the proposed methods can accurately predict the flow field, demonstrating excellent agreement with simulation data. Moreover, the flow field predictions can be used to accurately predict lift and drag coefficients, making these methods useful for optimizing the shape of bluff bodies for specific applications.Open Acces

Random Finite Set Theory and Optimal Control of Large Collaborative Swarms

Controlling large swarms of robotic agents has many challenges including, but not limited to, computational complexity due to the number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. This work generalizes the swarm state using Random Finite Set (RFS) theory and solves the control problem using Model Predictive Control (MPC) to overcome the aforementioned challenges. Computationally efficient solutions are obtained via the Iterative Linear Quadratic Regulator (ILQR). Information divergence is used to define the distance between the swarm RFS and the desired swarm configuration. Then, a stochastic optimal control problem is formulated using a modified L2^2 distance. Simulation results using MPC and ILQR show that swarm intensities converge to a target destination, and the RFS control formulation can vary in the number of target destinations. ILQR also provides a more computationally efficient solution to the RFS swarm problem when compared to the MPC solution. Lastly, the RFS control solution is applied to a spacecraft relative motion problem showing the viability for this real-world scenario.Comment: arXiv admin note: text overlap with arXiv:1801.0731