Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

Intelligent tracking control of a DC motor driver using self-organizing TSK type fuzzy neural networks

[[abstract]]In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Rare coding variants in genes encoding GABA(A) receptors in genetic generalised epilepsies : an exome-based case-control study

Background Genetic generalised epilepsy is the most common type of inherited epilepsy. Despite a high concordance rate of 80% in monozygotic twins, the genetic background is still poorly understood. We aimed to investigate the burden of rare genetic variants in genetic generalised epilepsy. Methods For this exome-based case-control study, we used three different genetic generalised epilepsy case cohorts and three independent control cohorts, all of European descent. Cases included in the study were clinically evaluated for genetic generalised epilepsy. Whole-exome sequencing was done for the discovery case cohort, a validation case cohort, and two independent control cohorts. The replication case cohort underwent targeted next-generation sequencing of the 19 known genes encoding subunits of GABA(A) receptors and was compared to the respective GABA(A) receptor variants of a third independent control cohort. Functional investigations were done with automated two-microelectrode voltage clamping in Xenopus laevis oocytes. Findings Statistical comparison of 152 familial index cases with genetic generalised epilepsy in the discovery cohort to 549 ethnically matched controls suggested an enrichment of rare missense (Nonsyn) variants in the ensemble of 19 genes encoding GABA(A) receptors in cases (odds ratio [OR] 2.40 [95% CI 1.41-4.10]; p(Nonsyn)=0.0014, adjusted p(Nonsyn)=0.019). Enrichment for these genes was validated in a whole-exome sequencing cohort of 357 sporadic and familial genetic generalised epilepsy cases and 1485 independent controls (OR 1.46 [95% CI 1.05-2.03]; p(Nonsyn)=0.0081, adjusted p(Nonsyn)=0.016). Comparison of genes encoding GABA(A) receptors in the independent replication cohort of 583 familial and sporadic genetic generalised epilepsy index cases, based on candidate-gene panel sequencing, with a third independent control cohort of 635 controls confirmed the overall enrichment of rare missense variants for 15 GABA(A) receptor genes in cases compared with controls (OR 1.46 [95% CI 1.02-2.08]; p(Nonsyn)=0.013, adjusted p(Nonsyn)=0.027). Functional studies for two selected genes (GABRB2 and GABRA5) showed significant loss-of-function effects with reduced current amplitudes in four of seven tested variants compared with wild-type receptors. Interpretation Functionally relevant variants in genes encoding GABA(A) receptor subunits constitute a significant risk factor for genetic generalised epilepsy. Examination of the role of specific gene groups and pathways can disentangle the complex genetic architecture of genetic generalised epilepsy. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.Peer reviewe

Tracking Control of Uncertain DC Server Motors Using Genetic Fuzzy System

[[abstract]]A controller of uncertain DC server motor is presented by using the fuzzy system with a real-time genetic algorithm. The parameters of the fuzzy system are online adjusted by the real-time genetic algorithm in order to generate appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop system, an energy fitness function derived from backstepping technique is involved in the genetic algorithm. According to the experimental results, the genetic fuzzy control scheme performs on-line tracking successfully.

Neural networks output feedback controllers using nonlinear parametric wavelet functions

[[abstract]]Purpose – The purpose of this paper is to propose an adaptive output feedback controller using wavelet neural networks with nonlinear parameterization for unknown nonlinear systems with only system output measurement. Design/methodology/approach – An error observer is used to estimate the tracking errors through output measurement information, and the wavelet neural networks are utilized to online approximate an unknown control input by adjusting their internal parameters. Findings – The controller integrates an error observer and wavelet neural networks with nonlinear parameterization into adaptive control design and is derived in accordance with implicit function and mean value theorem. The adjustment mechanism for the parameters of the wavelet neural networks can be derived by means of mean value theorem and Lyapunov theorem, and the stability of the closed-loop system can be guaranteed. Originality/value – This paper utilizes the nonlinear parametric wavelet neural networks with estimate state inputs to obtain the adaptive control input for nonaffine systems with only system output measurement, and the nonlinear wavelet parameters can be adjusted efficiently.

Backstepping Nonlinear Control Using Nonlinear Parametric Fuzzy Systems

[[abstract]]Based on nonlinear parametric fuzzy systems, an adaptive backstepping controller is proposed for a class of strict-feedback nonlinear systems. The nonlinear parametric fuzzy systems are capable of automatically learning their membership functions and tuning their weightings. Since the adjustable parameters of the membership functions nonlinearly appear in the fuzzy systems, the adaptive laws are derived by estimating the derivative of the fuzzy systems. Moreover, the stability of the closed-loop system is analyzed by means of Lyapunov theory, and some tracking performance is guaranteed. Finally, two examples are provided to demonstrate the effectiveness and applicability of the proposed scheme.

Adaptive Approach of Integrating DNA-based Evolution fuzzy-neural networks and Q-learning for mobile robots

[[abstract]]本提案書的目的是在提出一整合DNA-Based演化模糊類神經網路與Q-learning之 適應性學習方法，以使一移動機器人能適應實際複雜的環境(系統)。雖然先前計 畫研究有關DNA-Based演化演算法(evolution algorithm) 取代傳統的遺傳演算法 (genetic algorithm)來演化模糊類神經網路內部的架構與參數，以避免傳統的遺傳 演算法的性能(performance)和族群的大小(population size)有密切關係等限制。然 而，針對一般移動機器人學習之研究， DNA-Based演化演算法(evolution algorithm) 仍然與傳統的遺傳演算法一樣需要精確模擬器(simulator)與大量演化時間，方可 讓移動機器人使其行為適應實際複雜的環境(系統)。因此，本計畫將具有可藉由 與實際環境互動而產生最佳行為之學習方法Q-learning，整合至DNA-Based演化模 糊類神經網路，以避免因精確模擬器不易建立而造成模擬器和實際系統的誤差， 且因模擬器簡化而可降低大量DNA-Based演化時間。更確切來說，本計畫將完成 下列研究內容：針對移動機器人，提出一整合性的學習方法，可使DNA-Based演 算法只需藉由簡化的模擬器來演化模糊類神經網路，以塑模移動機器人之行為， 再經由實際環境之Q-learning學習方法，以適應性調整移動機器人行為。此外，我 們以實際移動機器人推動球體(ball-pushing)工作為例，來驗證本計劃所提出整合 性技術之可行性與效能。

Adaptive Backstepping Fuzzy Control for a Class of Nonlinear Systems

[[abstract]]By using a nonlinear parametric fuzzy identifier, an adaptive backstepping controller is proposed for a class of nonlinear systems. The nonlinear parametric fuzzy identifier is capable of automatically learning its membership functions. Since the fuzzy identifier is highly nonlinear, the derivative computation burden is enormous. Thus, this paper uses an estimation technique to effectively alleviate the derivative computation burden, and demonstrates the applicability of the proposed scheme by using computer simulation.

B-Spline Backstepping Control with Derivative Matrix Estimation and Its application

[[abstract]]A B-spline backstepping controller is proposed for a class of multiple-input multiple-output (MIMO) nonlinear systems. The control scheme incorporates the backstepping design technique with a B-spline neural network which is utilized to estimate the system dynamics. The B-spline neural network has the advantage of locally controlling its output behavior compared with other neural networks; therefore, it is very suitable to online estimate the system dynamics by tuning its interior parameters, including control points and knot points. Based on the mean-value theorem, the derivative of B-spline basis functions in relation to parameters can be estimated to online adjust these parameters. In addition, the validity of the proposed scheme is verified through an experiment on a servo motor system which is controlled by the output voltage of the Buck DC–DC converter.