The increasing scale of the Internet of Things (IoT) makes systems vulnerable to serious security threats, especially when the attacks of malicious nodes exist in these networks. Different malicious nodes will launch ...
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The increasing scale of the Internet of Things (IoT) makes systems vulnerable to serious security threats, especially when the attacks of malicious nodes exist in these networks. Different malicious nodes will launch different attacks, but most of them are based on tampering, re-transmission, and discarding methods. For such attacks, an effective method to detect malicious nodes focuses on the received and sent messages of each node. However, gathering messages about each node in the network is time-consuming, as well as collecting all the message in the network would consume the limited resources in the IoT. In this paper, we propose a novel method to detect malicious nodes based on an online learning algorithm. We first calculate the credibility of each path in the network based on the collected packets., then modeled the got path reputation by the online learning algorithm, finally, calculated the trust of each node in the IoT environment and detected the malicious node by a clustering algorithm. To make the model have a good performance when the network scale is small, we perform some processing on the network topology based on the general onlinelearning detection algorithm and get an enhanced onlinelearning detection algorithm. The result of the experiment proves that the methods we proposed can detect malicious nodes with high accuracy and work well with good stability.
In this paper, we are interested in the analysis of regularized onlinealgorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and step sizes are given to ensure convergen...
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In this paper, we are interested in the analysis of regularized onlinealgorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and step sizes are given to ensure convergence. Explicit learning rates are also given for particular step sizes.
Wireless systems are upgraded to use green energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a green coordinated multi...
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ISBN:
(纸本)9781665405409
Wireless systems are upgraded to use green energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a green coordinated multi-point (CoMP) system to handle the volatile arrival of green energy. In the green CoMP, the long-term weighted throughput maximization problem is investigated by expecting a non-positive consumption of the long-term on-grid energy. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning. A tradeoff relation is theoretically established to show that the long-term weighted throughput approaches the O(V)-neighbor of optimal value while the long-term consumed on-grid energy increases at a rate of O(log(2) (V)/root V), where V is an introduced control parameter. Numerical results are used to verify the performance of the online zero-forcing dirty paper precoder.
As the high growth of the number of vehicles, the traffic accidents are becoming more and more serious in recent years. In order to avoid the drivers being in danger, an intelligent vision-based system should focus on...
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ISBN:
(纸本)9781424469208
As the high growth of the number of vehicles, the traffic accidents are becoming more and more serious in recent years. In order to avoid the drivers being in danger, an intelligent vision-based system should focus on the image contents of the front the camera setting under the rear-view mirror in the vehicle. In this paper, we present a functional-link-based neuro-fuzzy network (FLNFN) structure for lane detection and departure warning system application. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. The lane detection method and the departure warning system proposed in this paper have been successfully evaluated on a PC platform of 3.2-GHz CPU, where the average frame-rate is up to 30fps.
The main goal of our research consists in finding a simple, straightforward online solution for obstacle avoiding problem encountered in mobile robots. The solution allows the robot to develop a local autonomous obsta...
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ISBN:
(纸本)9781457702013
The main goal of our research consists in finding a simple, straightforward online solution for obstacle avoiding problem encountered in mobile robots. The solution allows the robot to develop a local autonomous obstacle avoiding behavior every time when a higher-level motor command that is driving the robot (e.g. go forward/backward) put it in imminent danger to collide. The solution we proposed for a robot with 36 evenly distributed infrared (IR) sensors is a very simple one, based only on a minimal artificial neural network (ANN) trained with a backpropagation-like algorithm. Computationally cheap, the online learning algorithm we implemented proved to be very successful in both, static and dynamic clustered environment. The results reported here were obtained in MobotSim 1.0.03 - a configurable 2D simulator of differential drive mobile robots.
This paper considers online classification learningalgorithms for regularized classification schemes with generalized gradient. A novel capacity independent approach is presented. It verifies the strong convergence o...
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This paper considers online classification learningalgorithms for regularized classification schemes with generalized gradient. A novel capacity independent approach is presented. It verifies the strong convergence of sizes and yields satisfactory convergence rates for polynomially decaying step sizes. Compared with the gradient schemes, this al- gorithm needs only less additional assumptions on the loss function and derives a stronger result with respect to the choice of step sizes and the regularization parameters.
online portfolio selection based on Markowitz's mean-variance theory and Kelly's capital growth theory is a basic problem in financial engineering, which has attracted more and more attention and discussion in...
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online portfolio selection based on Markowitz's mean-variance theory and Kelly's capital growth theory is a basic problem in financial engineering, which has attracted more and more attention and discussion in the fields of artificial intelligence and machine learning. Its main purpose is to distribute the existing wealth among investable assets in order to achieve the goal of maximizing the cumulative return. In view of the existing average inversion strategy without fully considering the noise data, slow trend and the characteristics of lagging, we use adaptive kaufman average to predict the price of the stock, which is based on stock market volatility, to avoid the short-term noise produced by false signals and lag of long-term trends, and use the PA of portfolio allocation algorithm. Theoretical analysis and empirical results shows that AMA strategy has superior performance in indicators such as cumulative income, CR, SR, DMM, etc. in DIJA data.
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