30515858
Protecting Location Privacy with Dynamic Mac Address Exchanging in Wireless Networks
Location information of users can now be collected from most wireless communication using advanced wireless location tracking techniques. Providing location information can be advantageous in some situations. However, there are instances, where it may be critical to protect the location of the individual. Several protection strategies, such as periodically updating interface identifiers, have been proposed so that an adversary cannot track mobiles in long-term movements. In this paper, we introduce a new strategy, DMAS (Dynamical Mac Assignment with Shuffle), in which the mobile client dynamically exchanges its assigned Mac addresses with others.We present a security analysis to show this scheme can greatly secure a client's location privacy.
13311929
Secure Relative Location Determination in Vehicular Network
Relative location information is very useful in vehicular networks although it is vulnerable to various attacks. Many techniques have been proposed for relative positioning and location verification. Due to the high speed and the strict security requirements, the existing relative positioning and location verification techniques are not directly applicable to vehicular networks. Hence we present a scheme called SRLD, which securely determines the relative locations of a set of wirelessly connected vehicles based on the relative locations of each vehicle’s surrounding vehicles. SRLD uses cryptographic keys to authenticate location messages and uses a vehicle’s public key to identify the vehicle while protecting drivers’ privacy. To defend against Sybil attacks, SRLD employs registration and ticket verification mechanisms. It defends Wormhole and black hole attacks by probabilistically monitoring losses of relative location messages. Analysis and simulation results show that SRLD is lightweight and is resilient to Sybil, Wormhole and some other attacks.
2606413
A Mobility Framework for Ad Hoc Wireless Networks
Mobility management in ad hoc wireless networks faces many challenges. Mobility constantly causes the network topology to change. In order to keep accurate routes, the routing protocols must dynamically readjust to such changes. Thus, routing update traffic overhead is significantly high. Different mobility patterns have in general different impact on a specific network protocol or application. Consequently the network performance will be strongly influenced by the nature of the mobility pattern. In the past, mobility models were rather casually used to evaluate network performance under different routing protocols. Here, we propose a universal mobility framework, Mobility Vector Model, which can be used for recreating the various mobility patterns produced in different applications. Case studies on optimal transmission range as a function of mobility and on network performance under various mobility models are presented in the paper. Simulation results show that excessively large transmission range will not improve network performance significantly because of the increased collisions. There is an optimal range between 1.5 - 2 times the mean node distance for free space channel. Also, simulation results show that different mobility models will have different impact on the network performance for a variety of routing protocols (AODV, DSR, FSR). When choosing routing protocols for ad hoc network applications, performance studies under multiple mobility models are recommended. The Mobility Vector model can provide a realistic and flexible framework for reproducing various models.
1889768
LANMAR: landmark routing for large scale wireless ad hoc networks with group mobility
In this paper, we present a novel routing protocol for wireless ad hoc networks - Landmark Ad Hoc Routing (LANMAR). LANMAR combines the features of Fisheye State Routing (FSR) and Landmark routing. The key novelty is the use of landmarks for each set of nodes which move as a group (e.g., a team of co-workers at a convention or a tank battalion in the battlefield) in order to reduce routing update overhead. Like in FSR, nodes exchange link state only with their neighbors. Routes within Fisheye scope are accurate, while routes to remote groups of nodes are "summarized" by the corresponding landmarks. A packet directed to a remote destination initially aims at the Landmark; as it gets closer to destination it eventually switches to the accurate route provided by Fisheye. Simulation experiments show that LANMAR provides efficient and scalable routing in large, mobile, ad hoc environments in which group mobility applies.
6978130
Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization
This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.
18316871
Fast Kernel Classifier Construction Using Orthogonal Forward Selection to Minimise Leave-One-Out Misclassification Rate
We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier’s generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.
6169265
Automatic Kernel Regression Modelling Using Combined Leave-One-Out Test Score and Regularised Orthogonal Least Squares
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least squares. The proposed algorithm aims to achieve maximised model robustness via two effective and complementary approaches, parameter regularisation via ridge regression and model optimal generalisation structure selection. The major contributions are to derive the PRESS error in a regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised orthogonal least squares forward regression framework and hence construct a model with a good generalisation property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated model construction procedure without resort to any other validation data set for model evaluation.
10796129
A Forward Constrained Selection Algorithm for Probabilistic Neural Network
A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples and the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.
2411991
Robust nonlinear model identification methods using forward regression
In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.