GTRSSN: Gaussian trust and reputation system for sensor networks

This paper introduces a new Gaussian trust and reputation system for wireless sensor networks based on sensed continuous events to address security issues and to deal with malicious and unreliable nodes. It is representing a new approach of calculating trust between sensor nodes based on their sensed data and the reported data from surrounding nodes. It is addressing the trust issue from a continuous sensed data which is different from all other approaches which address the issue from communications and binary point of view. © Springer Science+Business Media B.V. 2008

Bayesian fusion algorithm for inferring trust in wireless sensor networks

This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust) to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary) and the data trust component (continuous) and proves that either component by itself, can mislead the network and eventually cause a total breakdown of the network. As a result of this, new algorithms are needed to combine more than one trust component to infer the overall trust. The proposed algorithm is simple and generic as it allows trust components to be added and deleted easily. Simulation results demonstrate that a node is highly trustworthy provided that both trust components simultaneously confirm its trustworthiness and conversely, a node is highly untrustworthy if its untrustworthiness is asserted by both components. © 2010 ACADEMY PUBLISHER

Can we trust trusted nodes in wireless sensor networks?

In this paper we extend our previously designed trust model in wireless sensor networks to include both; communication trust and data trust. Trust management in wireless sensor networks is predominantly based on routing messages; whether the communication has happened or not (successful and unsuccessful transactions). The uniqueness of sensing data in wireless sensor networks introduces new challenges in calculating trust between nodes (data trust). If the overall trust is based on just the communication trust, it might mislead the network, that is; untrustworthy nodes in terms of sensed data can be classified as trusted nodes due to their communication capabilities. Hence we need to develop new trust models to address the issue of the actual sensed data. Here we are comparing the two trust models and proving that one model by itself is not enough to decide on the trustworthiness of a node, so new techniques are required to combine both data trust and communication trust. ©2008 IEEE

Modelling Trust In Wireless Sensor Networks from the Sensor Reliability Prospective

This paper surveys the state of the art trust-based systems in Wireless Sensor Networks (WSN); it highlights the difference between Mobile ad hoc networks (MANET) and WSN and based on this observed difference (monitoring events and reporting data) a new trust model is introduced, which takes sensor reliability as a component of trust. A new definition of trust is created based on the newly introduced component of trust (sensor data) and an extension of node misbehaviour classification is also presented based on this new component of trust

RBATMWSN: Recursive Bayesian approach to trust management in wireless sensor networks

This paper introduces a new trust model and a reputation system for wireless sensor networks based on a sensed continuous data. It establishes the continuous version of the beta reputation system introduced in [1] and applied to binary events and presents a new Gaussian Reputation System for Sensor Networks (GRSSN) . We introduce a theoretically sound Bayesian probabilistic approach for mixing second-hand information from neighbouring nodes with directly observed information. ©2007 IEEE

Statistical Mechanics in the Extended Gaussian Ensemble

The extended gaussian ensemble (EGE) is introduced as a generalization of the canonical ensemble. The new ensemble is a further extension of the Gaussian ensemble introduced by J. H. Hetherington [J. Low Temp. Phys. {\bf 66}, 145 (1987)]. The statistical mechanical formalism is derived both from the analysis of the system attached to a finite reservoir and from the Maximum Statistical Entropy Principle. The probability of each microstate depends on two parameters $\beta$ and $\gamma$ which allow to fix, independently, the mean energy of the system and the energy fluctuations respectively. We establish the Legendre transform structure for the generalized thermodynamic potential and propose a stability criterion. We also compare the EGE probability distribution with the $q$-exponential distribution. As an example, an application to a system with few independent spins is presented.Comment: Revtex 4, 8 pages, 8 figure

Topology of event distribution as a generalized definition of phase transitions in finite systems

We propose a definition of phase transitions in finite systems based on topology anomalies of the event distribution in the space of observations. This generalizes all the definitions based on the curvature anomalies of thermodynamical potentials and provides a natural definition of order parameters. The proposed definition is directly operational from the experimental point of view. It allows to study phase transitions in Gibbs equilibria as well as in other ensembles such as the Tsallis ensemble.Comment: 4 pages, 3 figure

Depinning Transition of a Two Dimensional Vortex Lattice in a Commensurate Periodic Potential

We use Monte Carlo simulations of the 2D one component Coulomb gas on a triangular lattice, to study the depinning transition of a 2D vortex lattice in a commensurate periodic potential. A detailed finite size scaling analysis indicates this transition to be first order. No significant changes in behavior were found as vortex density was varied over a wide range.Comment: 5 pages, 8 figures. Revised discussion of correlation length exponent using a more accurate finite size scaling analysis. New figs. 5 and 6. Old figs. 6 and 7 now figs. 7 and

Extending canonical Monte Carlo methods II

Previously, we have presented a methodology to extend canonical Monte Carlo methods inspired on a suitable extension of the canonical fluctuation relation $C=\beta^{2}$ compatible with negative heat capacities $C<0$. Now, we improve this methodology by introducing a better treatment of finite size effects affecting the precision of a direct determination of the microcanonical caloric curve $\beta (E) =\partial S(E) /\partial E$, as well as a better implementation of MC schemes. We shall show that despite the modifications considered, the extended canonical MC methods possibility an impressive overcome of the so-called \textit{super-critical slowing down} observed close to the region of a temperature driven first-order phase transition. In this case, the dependence of the decorrelation time $\tau$ with the system size $N$ is reduced from an exponential growth to a weak power-law behavior $\tau(N)\propto N^{\alpha}$, which is shown in the particular case of the 2D seven-state Potts model where the exponent $\alpha=0.14-0.18$.Comment: Version submitted to JSTA

Submillimeter Wave Astronomy Satellite (SWAS) Launch and Early Orbit Support Experiences

The Submillimeter Wave Astronomy Satellite (SWAS) was successfully launched on December 6, 1998 at 00:58 UTC. The two year mission is the fourth in the series of Small Explorer (SMEX) missions. SWAS is dedicated to the study of star formation and interstellar chemistry. SWAS was injected into a 635 km by 650 km orbit with an inclination of nearly 70 deg by an Orbital Sciences Corporation Pegasus XL launch vehicle. The Flight Dynamics attitude and navigation teams supported all phases of the early mission. This support included orbit determination, attitude determination, real-time monitoring, and sensor calibration. This paper reports the main results and lessons learned concerning navigation, support software, star tracker performance, magnetometer and gyroscope calibrations, and anomaly resolution. This includes information on spacecraft tip-off rates, first-day navigation problems, target acquisition anomalies, star tracker anomalies, and significant sensor improvements due to calibration efforts