Energy-Aware Routing And Grooming For IP Transport Over WDM MLR Networks

In this work a mixed integer optimization linear programming (MILP) model was applied to mixed line rate (MLR) IP over WDM and IP over OTN over WDM (with and without OTN grooming) networks, with aim to reduce network energy consumption. Energy-aware and energy-aware & short-path routing techniques were used. Simulations were made based on a real network topology as well as on forecasts of traffic matrix based on statistical data from 2005 up to 2017. Energy aware routing optimization model on IPoWDM network, showed the lowest energy consumption along all years, and once compared with energy-aware & short-path routing, has led to an overall reduction in energy consumption up to 29%, expecting to save even more than shortest-path routing. © 2014 IEEE.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire’s shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.

Old cellulose for new multifunctional networks

Dissertação para obtenção do Grau de Doutor em Ciência e Engenharia de Materiais

Multipacket reception in LTE femtocell networks

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches.

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

Quantifying, generating and mitigating radio interference in Low-Power Wireless Networks

Doctoral Programme in Telecommunication - MAP-tele

QoS support in satellite and wireless networks: study under the network simulator (NS-2)

Aquest projecte es basa en l'estudi de l'oferiment de qualitat de servei en xarxes wireless i satel·litals. Per això l'estudi de les tècniques de cross-layer i del IEEE 802.11e ha sigut el punt clau per al desenvolupament teòric d’aquest estudi. Usant el simulador de xarxes network simulator, a la part de simulacions es plantegen tres situacions: l'estudi de la xarxa satel·lital, l'estudi del mètode d'accés HCCA i la interconnexió de la xarxa satel·lital amb la wireless. Encara que aquest últim punt, incomplet en aquest projecte, ha de ser la continuació per a futures investigacions.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.

Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Coevolutionary networks: a novel approach to understanding the relationships of humans with the infectious agents

Human organism is interpenetrated by the world of microorganisms, from the conception until the death. This interpenetration involves different levels of interactions between the partners including trophic exchanges, bi-directional cell signaling and gene activation, besides genetic and epigenetic phenomena, and tends towards mutual adaptation and coevolution. Since these processes are critical for the survival of individuals and species, they rely on the existence of a complex organization of adaptive systems aiming at two apparently conflicting purposes: the maintenance of the internal coherence of each partner, and a mutually advantageous coexistence and progressive adaptation between them. Humans possess three adaptive systems: the nervous, the endocrine and the immune system, each internally organized into subsystems functionally connected by intraconnections, to maintain the internal coherence of the system. The three adaptive systems aim at the maintenance of the internal coherence of the organism and are functionally linked by interconnections, in such way that what happens to one is immediately sensed by the others. The different communities of infectious agents that live within the organism are also organized into functional networks. The members of each community are linked by intraconnections, represented by the mutual trophic, metabolic and other influences, while the different infectious communities affect each other through interconnections. Furthermore, by means of its adaptive systems, the organism influences and is influenced by the microbial communities through the existence of transconnections. It is proposed that these highly complex and dynamic networks, involving gene exchange and epigenetic phenomena, represent major coevolutionary forces for humans and microorganisms.

Information Mobility: Information Dissemination in Mobile Networks

En el nostre projecte, considerem un escenari urbà o interurbà on persones amb dispositius mòbils (smartphones) o vehicles equipats amb interfícies de comunicació, estan interessats en compartir fitxers entre ells o descarregar-los al creuar Punts d’Accés (APs) propers a la carretera. Estudiem la possibilitat d’utilizar la cooperació en les trobades casuals entre nodes per augmentar la velocitat de descàrrega global. Amb aquest objectiu, plantejem algoritmes per a la selecció de quins paquets, per a quins destins i quins transportistes s’escullen en cada moment. Mitjançant extenses simulacions, mostrem com les cooperacions carry&forward dels nodes augmenten significativament la velocitat de descàrrega dels usuaris, i com aquest resultat es manté per a diversos patrons de mobilitat, col•locacions d'AP i càrregues de la xarxa. Per altra banda, aparells com els smartphones, on la targeta de WiFi està encesa contínuament, consumeixen l'energia de la bateria en poques hores. En molts escenaris, una targeta WiFi sempre activa és poc útil, perque sovint no hi ha necessitat de transmissió o recepció. Aquest fet es veu agreujat en les Delay Tolerant Networks (DTN), on els nodes intercanvien dades quan es creuen i en tenen l’oportunitat. Les tècniques de gestió de l’estalvi d’energia permeten extendre la duració de les bateries. El nostre projecte analitza els avantatges i inconvenients que apareixen quan els nodes apaguen períodicament la seva targeta wireless per a estalviar energia en escenaris DTN. Els nostres resultats mostren les condicions en que un node pot desconnectar la bateria sense afectar la probabilitat de contacte amb altres nodes, i les condicions en que aquesta disminueix. Per exemple, es demostra que la vida del node pot ser duplicada mantenint la probabilitat de contacte a 1. I que aquesta disminueix ràpidament en intentar augmentar més la vida útil.

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption