IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning

Detection of small, undetermined moving objects or objects in an occluded environment with a cluttered background is the main problem of computer vision. This greatly affects the detection accuracy of deep learning models. To overcome these problems, we concentrate on deep learning models for real-time detection of cars and tanks in an occluded environment with a cluttered background employing SSD and YOLO algorithms and improved precision of detection and reduce problems faced by these models. The developed method makes the custom dataset and employs a preprocessing technique to clean the noisy dataset. For training the developed model we apply the data augmentation technique to balance and diversify the data. We fine-tuned, trained, and evaluated these models on the established dataset by applying these techniques and highlighting the results we got more accurately than without applying these techniques. The accuracy and frame per second of the SSD-Mobilenet v2 model are higher than YOLO V3 and YOLO V4. Furthermore, by employing various techniques like data enhancement, noise reduction, parameter optimization, and model fusion we improve the effectiveness of detection and recognition. We further added a counting algorithm, and target attributes experimental comparison, and made a graphical user interface system for the developed model with features of object counting, alerts, status, resolution, and frame per second. Subsequently, to justify the importance of the developed method analysis of YOLO V3, V4, and SSD were incorporated. Which resulted in the overall completion of the proposed method

IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

Behavior-Based Interpretable Trust Management for IoT Systems

Establishing appropriate trust at the “right” level for the “right” application at the “right” time is important in the constantly evolving environment of the Internet of Things (IoT). Nevertheless, the process is challenging due to the lack of explainability and interpretability of the machine learning models. This paper presents a novel approach to managing IoT trust by employing explainable artificial intelligence (XAI) to connect complex algorithmic decisions with human understanding. Specifically, we propose a mutual information selection technique to determine the most significant behaviour-based features to identify trustworthy and untrustworthy behaviour in IoT device systems. Based on these behaviour-based features we develop a rule-based decision tree (DT) method to help enhance the explainability of our model. We evaluate our approach with a transformed UNSW NB 15 dataset, the results demonstrate improved user trust and system transparency. In addition, we did a comparative analysis of our XAI-behavioural-based trust management system (BB-TMS) with state-of-the-art methods, which demonstrated that our model surpasses competitors in terms of precision and interpretability. This highlights the effectiveness of integrating XAI with traditional machine learning approaches in the IoT domain

Monolithic Newton-Multigrid Solver for Multiphase Flow Problems with Surface Tension

We have developed a monolithic Newton-multigrid solver for multiphase flow problems which solves velocity, pressure and interface position simultaneously. The main idea of our work is based on the formulations discussed in [1], where it points out the feasibility of a fully implicit monolithic solver for multiphase flow problems via two formulations, a curvature free level set approach and a curvature free cut-off material function approach. Both formulations are fully implicit and have the advantages of requiring less regularity, since neither normals nor curvature are explicitly calculated, and no capillary time restriction has to be respected. Furthermore, standard Navier-Stokes solvers might be used, which do not have to take into account inhomogeneous force terms. The reinitialization issue is integrated within the formulations. The nonlinearity is treated with a Newton-type solver with divided difference evaluation of the Jacobian matrices. The resulting linearized system inside of the outer Newton solver is a typical saddle point problem which is solved using a geometrical multigrid method with Vanka-like smoother using higher order stable Q_2/P_1^disc FEM for velocity and pressure and Q_2 for all other variables. The method is implemented into an existing software package for the numerical simulation of multiphase flows (FeatFlow). The robustness and accuracy of this solver is tested for two different test cases, static bubble and oscillating bubble, respectively

An adaptive discrete Newton method for regularization-free Bingham model

[EN] Developing a numerical and algorithmic tool which correctly identifies unyielded regions in yield stress fluid flow is a challenging task. Two approaches are commonly used to handle the singular behaviour at the yield surface, i.e. the Augmented Lagrangian approach and the regularization approach, respectively. Generally in the regularization approach, solvers do not perform efficiently when the regularization parameter gets very small. In this work, we use a formulation introducing a new auxiliary stress. The three field formulation of the yield stress fluid corresponds to a regularization-free Bingham formulation. The resulting set of equations arising from the three field formulation is solved efficiently and accurately by a monolithic finite element method. The velocity and pressure are discretized by the higher order stable FEM pair Q2/Pdisc 1 and the auxiliary stress is discretized by the Q2 element. Furthermore, this problem is highly nonlinear and presents a big challenge to any nonlinear solver. Therefore, we developed a new adaptive discrete Newton method, which evaluates the Jacobian with the divided difference approach. We relate the step length to the rate of the actual nonlinear reduction for achieving a robust adaptive Newton method. We analyse the solvability of the problem along with the adaptive Newton method for Bingham fluids by doing numerical studies for a prototypical configuration ”viscoplastic fluid flow in a channel”.We would like to thank the Deutsche Forschungsgemeinschaft (DFG) for their financial support under the DFG Priority Program SPP 1962. The authors also acknowledge the support by LS3 and LiDO3 team at ITMC, TU Dortmund UniversityFatima, A.; Turek, S.; Ouazzi, A.; Afaq, MA. (2022). An adaptive discrete Newton method for regularization-free Bingham model. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 180-189. https://doi.org/10.4995/YIC2021.2021.12389OCS18018

An Adaptive Discrete Newton Method for Regularization-Free Bingham Model

Developing a numerical and algorithmic tool which correctly identifies unyielded regions in yield stress fluid flow is a challenging task. Two approaches are commonly used to handle the singular behaviour at the yield surface, i.e. the Augmented Lagrangian approach and the regularization approach, respectively. Generally in the regularization approach, solvers do not perform efficiently when the regularization parameter gets very small. In this work, we use a formulation introducing a new auxiliary stress. The three field formulation of the yield stress fluid corresponds to a regularization-free Bingham formulation. The resulting set of equations arising from the three field formulation is solved efficiently and accurately by a monolithic finite element method. The velocity and pressure are discretized by the higher order stable FEM pair Q_2/P_1^disc and the auxiliary stress is discretized by the Q_2 element. Furthermore, this problem is highly nonlinear and presents a big challenge to any nonlinear solver. Therefore, we developed a new adaptive discrete Newton method, which evaluates the Jacobian with the divided difference approach. We relate the step length to the rate of the actual nonlinear reduction for achieving a robust adaptive Newton method. We analyse the solvability of the problem along with the adaptive Newton method for Bingham fluids by doing numerical studies for a prototypical configuration ”viscoplastic fluid flow in a channel”

Robust Monolithic Multigrid FEM Solver for Three Field Formulation of Incompressible Flow Problems

Numerical simulation of three field formulations of incompressible flow problems is of interest for many industrial applications, for instance macroscopic modeling of Bing-ham, viscoelastic and multiphase flows, which usually consists in supplementing the mass and momentum equations with a differential constitutive equation for the stress field. The variational formulation rising from such continuum mechanics problems leads to a three field formulation with saddle point structure. The solvability of the problem requires different compatibility conditions (LBB conditions) [1] to be satisfied. Moreover, these constraints over the choice of the spaces may conflict/challenge the robustness and the efficiency of the solver. For illustrating the main points, we will consider the three field formulation of the Navier-Stokes problem in terms of velocity, stress, and pressure. Clearly, the weak form imposes the compatibility constraints over the choice of velocity, stress, and pressure spaces. So far, the velocity-pressure combi-nation took much more attention from the numerical analysis and computational fluid dynamic community, which leads to some best interpolation choices for both accuracy and efficiency, as for instance the combination Q2/P1disc. To maintain the computational advantages of the Navier-Stokes solver in two field formulations, it may be more suitable to have a Q2 interpolation for the stress as well, which is not stable in the absence of pure viscous term [2]. We proceed by adding an edge oriented stabilization to overcome such situation. Furthermore, we show the robustness and the efficiency of the resulting discretization in comparison with the Navier-Stokes solver both in two field as well as in three field formulation in the presence of pure viscous term. Moreover, the benefit of adding the edge oriented finite element stabilization (EOFEM) [3, 4] in the absence of the pure viscous term is tested. The nonlinearity is treated with a Newton-type solver [5] with divided difference evaluation of the Jacobian matrices [6, 7]. The resulting linearized system inside of the outer Newton solver is a typical saddle point problem which is solved using a geometrical multigrid method with Vanka-like smoother [8, 9]. The method is implemented into the FeatFlow [10] software package for the numerical simulation. The stability and robustness of the method is numerically investigated for ”flow around cylinder” benchmark [7, 11]

Monolithic Newton-Multigrid Solver for Multiphase Flow Problems with Surface Tension

[EN] We have developed a monolithic Newton-multigrid solver for multiphase flow problems which solves velocity, pressure and interface position simultaneously. The main idea of our work is based on the formulations discussed in [1], where it points out the feasibility of a fully implicit monolithic solver for multiphase flow problems via two formulations, a curvature-free level set approach and a curvature-free cutoff material function approach. Both formulations are fully implicit and have the advantages of requiring less regularity, since neither normals nor curvature are explicitly calculated, and no capillary time restriction. Furthermore, standard Navier-Stokes solvers might be used, which do not have to take into account inhomogeneous force terms. The reinitialization issue is integrated with a nonlinear terms within the formulations.The nonlinearity is treated with a Newton-type solver with divided difference evaluation of the Jacobian matrices. The resulting linearized system inside of the outer Newton solver is a typical saddle point problem which is solved using the geometrical multigrid with Vanka-like smoother using higher order stable FEM pair $Q_2/P^{\text{disc}}_1$ for velocity and pressure and $Q_2$ for all other variables. The method is implemented into an existing software packages for the numerical simulation of multiphase flows (FeatFlow). The robustness and accuracy of this solver is tested for two different test cases, i.e. static bubble and oscillating bubble, respectively [2].Muhammad Aaqib Afaq would like to thank Erasmus Mundus INTACT project, funded by the European Union as part of the Erasmus Mundus programme and the National University of Sciences and Technology (NUST) for their financial support. The authors also acknowledge the support by LS3 and LiDO3 team at ITMC, TU Dortmund University.Afaq, MA.; Turek, S.; Ouazzi, A.; Fatima, A. (2022). Monolithic Newton-Multigrid Solver for Multiphase Flow Problems with Surface Tension. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 190-199. https://doi.org/10.4995/YIC2021.2021.12390OCS19019

Robust adaptive discrete Newton method for regularization-free Bingham model

Developing a numerical and algorithmic tool which accurately detects unyielded regions in yield stress fluid flow is a difficult endeavor. To address these issues, two common approaches are used to handle singular behaviour at the yield surface, i.e. the augmented Lagrangian approach and the regularization approach. Generally, solvers do not operate effectively when the regularization parameter is very small in the regularization approach. In this work, we use a formulation involving a new auxiliary stress tensor, wherein the three-field formulation is equivalent to a regularization-free Bingham formulation. Additionally, a monolithic finite element method is employed to solve the set of equations resulting from the three-field formulation accurately and effciently, where the velocity, pressure fields are discretized by the higherorder stable FEM pair Q2=Pdisc1 and the auxiliary stress is discretized by the Q2 element. Furthermore, this article presents a novel adaptive discrete Newton method for solving highly nonlinear problems, which exploits the divided difference approach for evaluating the Jacobian. The step size of the solver is dynamically adjusted according to the rate of nonlinear reduction, enabling a robust and efficient approach. Numerical studies of several prototypical Bingham fluid configurations ("viscoplastic fluid flow in a channel", "lid driven cavity" and "rotational Bingham flow in a square reservoir") are used to analyse the performance of this method