Establishment of Ad-Hoc Communities through Policy-Based Cryptography

A policy-based encryption scheme allows to encrypt a message according to a credential-based policy formalized as monotone Boolean expression written in standard normal form. The encryption is so that only the users having access to a qualified set of credentials for the policy are able to decrypt the message. In this paper, we first revisit the formal definition of policy-based encryption and describe a policy-based encryption scheme from bilinear pairings. Our scheme improves the one proposed in [W. Bagga and R. Molva. Policy-based cryptography and applications. In Proceedings of Financial Cryptography and Data Security (FC'05), volume 3570 of LNCS, pages 72–87. Springer-Verlag, 2005] in terms of ciphertext size, while at the same time preserving the computational efficiency. Then, we describe an application of policy-based encryption in the context of ad-hoc networks. More precisely, we show how the policy-based encryption primitive can be used to achieve a privacy-enhanced secure establishment of ad-hoc communities.     

Cell‐loss control in multiplexing of VBR video over ATM networks

In this article we investigate the impacts of interspacing and source polling policies on the cell‐loss rates in transmission of variable bit rate video sources over Asynchronous Transfer Mode networks. We present a mathematical model that finds the approximate optimal starting times of the video sources that minimize the congestion at the multiplexer. Combined with the approximate optimal source starting times, we propose a source ordering and polling policy for reducing the cell‐loss rates. The proposed policy is tested against alternative policies using simulation of pre‐recorded video data. This revised version was published online in June 2006 with corrections to the Cover Date.     

Transfer characteristic of a Goto pair in small-size Josephson junctions

The response of a Goto pair, based on small-size Josephson junctions, to voltage jumps has been studied within the framework of a resistive model. An analytical formula for the time of establishment of the transfer characteristic is obtained and the corresponding estimates are calculated.     

Quality of MPEG2 signal on a simulated digital terrestrialtelevision

The monitoring of the quality of service in digital television networks needs instruments to assess automatically the perceived quality of audio and video components of an MPEG2 signal. In order to improve the knowledge of the impact of the impairments on the final service, and to validate the algorithms that will be implemented in the quality evaluation instruments, a general simulation chain has been designed and developed. This paper presents the simulations performed as well as the analysis and the synthesis of the obtained results     

Efficient Access Control for Wireless Sensor Data

Although very developed in many sectors (databases, filesystems), access control schemes are still somewhat elusive when it comes to wireless sensor networks. However, it is clear that many WSN systems—such as healthcare and automotive ones—need a controlled access to data that sensor nodes produce, given its high sensitivity. Enforcing access control in wireless sensor networks is a particularly difficult task due to the limited computational capacity of wireless sensor nodes. In this paper we present a full-fledged access control scheme for wireless sensor data. We enforce access control through data encryption, thus embedding access control in sensor data units. We also propose a lightweight key generation mechanism, based on cryptographic hash functions, that allows for hierarchical key derivation. The suggested protocol only relies on simple operations, does not require interactions between nodes and data consumers and has minimal storage requirements.

Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

Unsupervised clustering and clustering validity are used as essential instruments of data analytics. Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings. Also, validity measures may be biased towards the underlying clustering method. Moreover, neglecting a confidence requirement may result in over-partitioning. In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system. Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine learning. Herein, the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy clustering. It involves only uni-dimensional membership weights, regardless of the data dimension, stipulates no specific distribution, and is independent of the underlying similarity measure. Inclusive tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions, besides behaving more robust to over partitioning. Also, in the comparative correlation analysis between true clustering error rates and some known internal validity indices, the suggested index exhibited the highest strong correlations. This relationship has been also proven stable through additional statistical acceptance tests. Thus the provided relative uncertainty measure can be used as a probable error estimate in the clustering as well. Besides, it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.     

Bayesian Computations for a Class of Reliability Growth Models

In this article, we consider the development and analysis of both attribute- and variable-data reliability growth models from a Bayesian perspective. We begin with an overview of a Bayesian attribute-data reliability growth model and illustrate how this model can be extended to cover the variable-data growth models as well. Bayesian analysis of these models requires inference over ordered regions, and even though closed-form results for posterior quantities can be obtained in the attribute-data case, variable-data models prove difficult. In general, when the number of test stages gets large, computations become burdensome and, more importantly, the results may become inaccurate due to computational difficulties. We illustrate how the difficulties in the posterior and predictive analyses can be overcome using Markov-chain Monte Carlo methods. We illustrate the implementation of the proposed models by using examples from both attribute and variable reliability growth data.     

Design and implementation of highly reliable dual-computer systems

Two of the main parameters of real-time computer systems are reliability and performance. Researchers are always looking for solutions to increase the values of these parameters, which is the goal of this study. To this end, we propose an architecture for a dual-computer system that operates in real-time with fault tolerance implemented purely by hardware. The hardware, as designed and implemented, performs the following key services: 1) determination of the fault type (temporary or permanent) and 2) localization of the faulty computer without using self-testing techniques or diagnostic routines. Our design has several benefits: 1) the designed hardware shortens the recovery point time period; 2) the proposed nontrivial sequence of fault-tolerant services reduces (to two) the number of logical segments that must be re-run to recover computational processes; and 3) the determination of the fault type allows for the elimination of only computers with permanent faults. These contributions yield improvements in both the performance and reliability of the system.