Abstract
We propose a coding scheme based on the combination of interleaving with systematic channel codes for secrecy. The basic idea consists of generating a random interleaving key that is used to shuffle/interleave information at the source. The message and the interleaving key are then both encoded with a systematic code and the part related to the interleaving key is removed/punctured before being sent to the channel, hence operating as a hidden key for any receiver (legitimate or not) that needs to deinterleave the message. Successfully obtaining the original message then depends on determining the interleaving key, which can only be done through the parity bits that result from jointly encoding the interleaving key and the message. We provide a method to determine the necessary signal-to-noise ratio difference that enables successful reception at the legitimate receiver without the eavesdropper having access to the message. In addition, we provide evidence that this scheme may also be used to turn a realistic channel into a discrete memoryless channel, thus providing a first practical implementation of an abstract channel that can be employed with a wiretap code to provide information-theoretic security guarantees. © 2015 IEEE.

Abstract

Abstract
The paradigm of having everyone and everything connected in an ubiquitous way poses huge challenges to today's networks due to the massive traffic volumes involved. To turn treatable all network tasks requiring traffic analysis, sampling the traffic has become mandatory triggering substantial research in the area. Aiming at fostering the deployment and tuning of new sampling techniques, this paper presents a flexible sampling framework developed following a multilayer design in order to easily set up the characteristics of a sampling technique according to the measurement task to be assisted. The framework implementation relies on a comprehensive sampling taxonomy which identifies the granularity, selection scheme and selection trigger as the inner characteristics distinguishing current sampling proposals. As proof of concept of the versatility of this framework in testing the suitability of distinct sampling schemes, this work provides a comparative performance evaluation of classical and recent sampling techniques regarding the estimation accuracy, the volume of data involved in the sampling process and the computational weight in terms of CPU and memory usage.

Abstract

Abstract
It is proposed the multiplexing of optical fiber-based surface plasmon resonance (SPR) sensors deployed in a ladder topology, addressed in wavelength by combining each sensor with specific fiber Bragg gratings (FBGs) and considering intensity interrogation. In each branch of the fiber layout, the FBGs are located after the sensor and the peak optical power reflected by the FBGs is a function of the relative spectral position between the SPR sensor and the FBG resonances, with the former dependent on the refractive index of the surrounding medium. The concept is tested for the multiplexing of two SPR sensors fabricated in an etched region of a single-mode fiber showing intrinsic refractive index sensitivity up to 5000 nm/RIU, which translates into a sensitivity of similar to 829 dB/RIU from the interrogation approach considered. The obtained refractive index resolution is in the order of 10(-4) RIU, and the crosstalk level between the sensors was found negligible.

Abstract
Micro grids represent an emergent vision to address the challenges imposed by recent trends in smart electrical grids, where the large-scale integration of distributed energy production units plays an important role. Nevertheless, the realization of this vision requires the use of advanced intelligent approaches to manage the micro grids elements, such as distributed renewable energy production units, loads and storage devices. Multi-agent systems provide a suitable framework to design and implement such systems, where autonomous agents are endowed with predictive data analytics capabilities, e.g., for the prediction of renewable energy production and consumption, taking advantage of the large amount of data produced in these environments. In this context, this paper presents an agent-based system for the management of micro grids, where predictive capabilities were embedded in agents to provide real-time data analytics. The proposed approach was applied to an experimental case study where a set of predictive models was tested for short and long term forecasting of the energy produced by photovoltaic units.