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Restricting network access of routing and packet forwarding to well-behaving nodes and denying access from misbehaving nodes are critical for the proper functioning of a mobile ad-hoc network where cooperation among all networking nodes is usually assumed. However, the lack of a network infrastructure, the dynamics of the network topology and node membership, and the potential attacks from inside the network by malicious and/or noncooperative selfish nodes make the conventional network access control mechanisms not applicable. We present URSA, a ubiquitous and robust access control solution for mobile ad hoc networks. URSA implements ticket certification services through multiple-node consensus and fully localized instantiation. It uses tickets to identify and grant network access to well-behaving nodes. In URSA, no single node monopolizes the access decision or is completely trusted. Instead, multiple nodes jointly monitor a local node and certify/revoke its ticket. Furthermore, URSA ticket certification services are fully localized into each node's neighborhood to ensure service ubiquity and resilience. Through analysis, simulations, and experiments, we show that our design effectively enforces access control in the highly dynamic, mobile ad hoc network.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2005.09.038 Byline: Xiaoqiao Meng (a), Thyaga Nandagopal (b), Li Li (b), Songwu Lu (a) Keywords: Sensor network; Contour map; Spatio-temporal sampling Abstract: Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink. In this paper, we propose an efficient data-collection scheme that can be used for event monitoring or network-wide diagnosis. Our scheme relies on the well-known representation of data -- contour maps, which trade off accuracy with the amount of samples. The scheme consists of three novel algorithms to build contour maps: distributed spatial and temporal data suppression, contour reconstruction at the sink via interpolation and smoothing, and an efficient mechanism to convey routing information over multiple hops. By reducing the number of transmissions required to convey relevant information to the sink, the proposed contour mapping scheme saves energy and improves network lifetime. In a sharp contrast to related work in this area, the scheme does not require all nodes to explicitly share information. The contour mapping scheme can be applied for tasks such as: (1) presenting a global picture of the network in both temporal and spatial domains, (2) being used as a diagnosis tool, e.g., to detect faulty sensors and to scan for residual energy, (3) working in concert with in-network aggregation schemes to further reduce the communication overhead of aggregation schemes. The proposed scheme imposes little processing and storage overhead, allowing for the sensor networking paradigm of 'dumb sensor, smart sink' which enables economical deployment of large-scale sensor networks. Simulation results show that our scheme is resilient to both high packet loss rate and measurement noise. The design is also energy efficient, resulting in up to an-order-of-magnitude power savings when compared with the base line scheme where every sensor sends its report to the sink. Author Affiliation: (a) Computer Science Department, University of California, Los Angeles, CA 90095, United States (b) Bell Laboratories, Holmdel, NJ 07733, United States Article History: Received 26 January 2005; Revised 17 August 2005; Accepted 30 September 2005 Article Note: (miscellaneous) Responsible Editor: E. Ekici

We study the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. Our objective is to resolve the difficulty associated with the persistency of excitation condition inherent to the standard schemes in the neural identification literature. This difficulty is circumvented here by a novel formulation and by using a new class of identification algorithms recently obtained by Didinsky et al. We show how these algorithms can be exploited to successfully identify the nonlinearity in the system using neural-network models. By embedding the original problem in one with noise-perturbed state measurements, we present a class of identifiers (under [L.sub.1] and [L.sub.2] cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. In this respect, many available learning algorithms in the current neural-network literature, e.g., the backpropagation scheme and the genetic algorithms-based scheme, with slight modifications, can ensure the identification of the system nonlinearity. Subsequently, we address the same problem under a third, worst case [L.sub.[infinity]] criterion for an RBF modeling. We present a neural-network version of an [H.sup.[infinity]]-based identification algorithm from Didinsky et al and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information (FSDI) and noise-perturbed full-state-information (NPFSI), it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the nonlinearity. Results from several simulation studies have been included to demonstrate the effectiveness of these algorithms. Index Terms - Backpropagation, feedforward neural networks, [H.sup.[infinity]] optimization, identification, nonlinear systems, persistency of excitation, perturbation method, radial basis function networks.