530903
ANODR: anonymous on demand routing with untraceable routes for mobile ad-hoc networks
In hostile environments, the enemy can launch traffic analysis against interceptable routing information embedded in routing messages and data packets. Allowing adversaries to trace network routes and infer the motion pattern of nodes at the end of those routes may pose a serious threat to covert operations. We propose ANODR, an anonymous on-demand routing protocol for mobile ad hoc networks deployed in hostile environments. We address two closely related problems: For route anonymity, ANODR prevents strong adversaries from tracing a packet flow back to its source or destination; for location privacy, ANODR ensures that adversaries cannot discover the real identities of local transmitters. The design of ANODR is based on "broadcast with trapdoor information", a novel network security concept which includes features of two existing network and security mechanisms, namely "broadcast" and "trapdoor information". We use simulations and implementation to validate the effectiveness of our design.
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.
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.
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.
28248774
Dual-orthogonal radial basis function networks for nonlinear time series prediction
A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.
15393741
Backward Elimination Methods for Associative Memory Network Pruning
Three hybrid data based model construction/pruning formula are introduced by using backward elimination as automatic postprocessing approaches to improved model sparsity. Each of these approaches is based on a composite cost function between the model fit and one of three terms of A-/D-optimality / (parameter 1-norm in basis pursuit) that determines a pruning process. The A-/D-optimality based pruning formula contain some orthogonalisation between the pruned model and the deleted regressor. The basis pursuit cost function is derived as a simple formula without need for an orthogonalisation process. These different approaches to parsimonious data based modelling are applied to the same numerical examples in parallel to demonstrate their computational effectiveness.
12169365
Orthogonal Forward Selection for Constructing the Radial Basis Function Network with Tunable Nodes
An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.
9825053
M-estimator and D-optimality model construction using orthogonal forward regression
This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.
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.