A Graph-Based Approach to Address Trust and Reputation in Ubiquitous Networks

The increasing popularity of virtual computing environments such as Cloud and Grid computing is helping to drive the realization of ubiquitous and pervasive computing. However, as computing becomes more entrenched in everyday life, the concepts of trust and risk become increasingly important. In this paper, we propose a new graph-based theoretical approach to address trust and reputation in complex ubiquitous networks. We formulate trust as a function of quality of a task and time required to authenticate agent-to-agent relationship based on the Zero-Common Knowledge (ZCK) authentication scheme. This initial representation applies a graph theory concept, accompanied by a mathematical formulation of trust metrics. The approach we propose increases awareness and trustworthiness to agents based on the values estimated for each requested task, we conclude by stating our plans for future work in this area

Lateral forces on circularly polarizable particles near a surface

Optical forces allow manipulation of small particles and control of nanophotonic structures with light beams. While some techniques rely on structured light to move particles using field intensity gradients, acting locally, other optical forces can push particles on a wide area of illumination but only in the direction of light propagation. Here we show that spin orbit coupling, when the spin of the incident circularly polarized light is converted into lateral electromagnetic momentum, leads to a lateral optical force acting on particles placed above a substrate, associated with a recoil mechanical force. This counterintuitive force acts in a direction in which the illumination has neither a field gradient nor propagation. The force direction is switchable with the polarization of uniform, plane wave illumination, and its magnitude is comparable to other optical forces.This work has been supported, in part, by EPSRC (UK). 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Efficiency of Plasmon-Induced Dual-Mode Fluorescence Enhancement upon Two-Photon Excitation

Anisotropic noble metal nanoparticles supporting more than one localized surface plasmon resonance can be tailored for efficient dual-mode fluorescence enhancement by ensuring an adequate coupling to both absorption and emission bands of fluorophores. This approach is naturally extended to two-photon excitation fluorescence, where a molecule is excited by simultaneous nonlinear absorption of two photons. However, the relative impact of plasmon coupling to excitation and emission on the overall fluorescence enhancement can be very different in this case. Here, by using the finite-difference time-domain method, we study the two-photon excitation fluorescence of near-infrared fluorescent protein (NirFP) eqFP670, which is the most red-shifted NirFP to date, in proximity to a silver nanobar. By optimizing the length and aspect ratio of the particle, we reach a fluorescence enhancement factor of 103. We show that the single mode coupling regime with highly tuned near-field significantly outperforms the dual-mode coupling enhancement. The plasmon-induced amplification of the fluorophore’s excitation rate becomes of utmost importance due to its quadratic dependence on light intensity, defining the fluorescence enhancement upon two-photon excitation. Our results can be used for the rational design of hybrid nanosystems based on NirFP and plasmonic nanoparticles with greatly improved brightness important for developing whole-body imaging techniques

A multi-objective genetic algorithm for minimising network security risk and cost

Security countermeasures help ensure information security: confidentiality, integrity and availability(CIA), by mitigating possible risks associated with the security event. Due to the fact, that it is often difficult to measure such an impact quantitatively, it is also difficult to deploy appropriate security countermeasures. In this paper, we demonstrate a model of quantitative risk analysis, where an optimisation routine is developed to help a human decision maker to determine the preferred trade-off between investment cost and resulting risk. An offline optimisation routine deploys a genetic algorithm to search for the best countermeasure combination, while multiple risk factors are considered. We conduct an experimentation with real world data, taken from the PTA(Practical Threat Analysis) case study to show that our method is capable of delivering solutions for real world problem data sets. The results show that the multi-objective genetic algorithm (MOGA) approach provides high quality solutions, resulting in better knowledge for decision making