Earticle

Security management in pervasive networks should be fundamentally flexible. The dynamic and heterogeneous character of these environments requires a security infrastructure which can be tailored to different operating conditions, at variable levels of granularity, during phases of design, deployment, and execution. This is possible with component-based security architecture. We illustrate the benefits of this approach by presenting AMISEC, an integrated authentication and authorization middleware. Through the component paradigm, AMISEC supports different network topologies of TTPs, cryptographic algorithms, protocols, or trust management strategies, resulting in a fully à la carte security infrastructure.

Vehicular Ad-Hoc Networks are networks of communication between vehicles and roadside units. These networks have the potential to increase safety and provide many services to drivers, but they also present risks to privacy. Researching mechanism to protect privacy requires two key ingredients: 1. a precise definition of privacy that reflects citizens’ concern and perceptions, and 2. an upstanding of the type of attacks in VANETs. In this research, we formulate a workable definition of privacy, and focus on tracking attacks, which we found to lacking. Although considerable research has been performed in tracking none of the published solutions ensures full protection. We propose to combine a set of published solutions, namely: Mix Zones, Silent Periods, and Group Signatures in order to improve the privacy of drivers. Vehicles enters a region where, vehicles change their pseudonyms (Mix Zone) as well as network addresses; next enter the silent period, and then use one group key for communication. It could help make tracking more difficult and increase the safety and confidence of drivers using VANET.

Software verification and validation (SVV) are major ingredients of current software engineering projects. Among the available methods to solve this problem, formal methods (although very costly at the computational level) are arguably the most powerful ones. One of the most promising approaches in this field is that based on Petri nets. This paper discusses some issues regarding the application of Petri nets to SVV from a hybrid mathematical/computational point of view. The paper also describes a Mathematica package developed by the author for a class of Petri nets, which is applied to address the SVV problem in the context of graphical semantic web services based on virtual agents evolving in digital 3D worlds.

This paper proposes an efficient two stage demosaicing method to interpolate color filter array images. The proposed method based on the edge sensing technique improves the interpolation performance by adopting the color difference model for a green channel as well as a red/blue channel. In particular, the green channel interpolation method with a new concept includes the gradient operator, which uses the total amount of slope changes in adjacent color information, and the missing green color estimation, which uses Approximated Directional Line Averages. Comparing with various comparative experiments between the conventional results and the proposed ones, the performances of the proposed method in this paper outperform to existing algorithms in terms of visual performance both in numerical and visual aspects. Our method of demosaicing improves the standard performance by8.927dB on the average in comparison of other methods in MSE(Mean square Error).

A mobile Ad-hoc network consists of dynamic nodes that can move freely. These nodes communicate with each other without a base station. In this paper, we propose a Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering algorithm for mobile ad hoc networks. It has the ability to find the optimal or near-optimal number of clusters to efficiently manage the resources of the network. The cluster-heads do the job of routing network packets within the cluster or to the nodes of other clusters. The proposed CLPSO based clustering algorithm takes into consideration the transmission power, ideal degree, mobility of the nodes and battery power consumption of the mobile nodes. It is a weighted clustering algorithm that assigns a weight to each of these parameters of the network. Each particle of the swarm contains information about the cluster-heads and the members of each cluster. It uses the evolutionary capability to optimize the number of clusters. We compare the simulation results with two other well-known clustering algorithms. The results show that the proposed technique is effective and works better than the other two approaches.