Improving quality-of-service in wireless sensor networks by mitigating “Hidden-Node Collisions”

Wireless sensor networks (WSNs) emerge as underlying infrastructures for new classes of large-scale networked embedded systems. However, WSNs system designers must fulfill the quality-of-service (QoS) requirements imposed by the applications (and users). Very harsh and dynamic physical environments and extremely limited energy/computing/memory/communication node resources are major obstacles for satisfying QoS metrics such as reliability, timeliness, and system lifetime. The limited communication range of WSN nodes, link asymmetry, and the characteristics of the physical environment lead to a major source of QoS degradation in WSNs-the ldquohidden node problem.rdquo In wireless contention-based medium access control (MAC) protocols, when two nodes that are not visible to each other transmit to a third node that is visible to the former, there will be a collision-called hidden-node or blind collision. This problem greatly impacts network throughput, energy-efficiency and message transfer delays, and the problem dramatically increases with the number of nodes. This paper proposes H-NAMe, a very simple yet extremely efficient hidden-node avoidance mechanism for WSNs. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes that scales to multiple clusters via a cluster grouping strategy that guarantees no interference between overlapping clusters. Importantly, H-NAMe is instantiated in IEEE 802.15.4/ZigBee, which currently are the most widespread communication technologies for WSNs, with only minor add-ons and ensuring backward compatibility with their protocols standards. H-NAMe was implemented and exhaustively tested using an experimental test-bed based on ldquooff-the-shelfrdquo technology, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. H-NAMe effectiveness was also demonstrated in a target tracking application with mobile robots - over a WSN deployment.

Quality-of-service in wireless sensor networks: state-of-the-art and future directions

Wireless sensor networks (WSNs) are one of today’s most prominent instantiations of the ubiquituous computing paradigm. In order to achieve high levels of integration, WSNs need to be conceived considering requirements beyond the mere system’s functionality. While Quality-of-Service (QoS) is traditionally associated with bit/data rate, network throughput, message delay and bit/packet error rate, we believe that this concept is too strict, in the sense that these properties alone do not reflect the overall quality-ofservice provided to the user/application. Other non-functional properties such as scalability, security or energy sustainability must also be considered in the system design. This paper identifies the most important non-functional properties that affect the overall quality of the service provided to the users, outlining their relevance, state-of-the-art and future research directions.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Classification of some penalty methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem. There are many kinds of optimization problems and, consequently, many kinds of methods to solve them. When the involved functions are nonlinear and their derivatives are not known or are very difficult to calculate, these methods are more rare. These kinds of functions are frequently called black box functions. To solve such problems without constraints (unconstrained optimization), we can use direct search methods. These methods do not require any derivatives or approximations of them. But when the problem has constraints (nonlinear programming problems) and, additionally, the constraint functions are black box functions, it is much more difficult to find the most appropriate method. Penalty methods can then be used. They transform the original problem into a sequence of other problems, derived from the initial, all without constraints. Then this sequence of problems (without constraints) can be solved using the methods available for unconstrained optimization. In this chapter, we present a classification of some of the existing penalty methods and describe some of their assumptions and limitations. These methods allow the solving of optimization problems with continuous, discrete, and mixing constraints, without requiring continuity, differentiability, or convexity. Thus, penalty methods can be used as the first step in the resolution of constrained problems, by means of methods that typically are used by unconstrained problems. We also discuss a new class of penalty methods for nonlinear optimization, which adjust the penalty parameter dynamically.

A multidimensional scaling classification of robotic sensors

This chapter analyzes the signals captured during impacts and vibrations of a mechanical manipulator. Eighteen signals are captured and several metrics are calculated between them, such as the correlation, the mutual information and the entropy. A sensor classification scheme based on the multidimensional scaling technique is presented.

An overview of the higher level classification of Pucciniomycotina based on combined analyses of nuclear large and small subunit rDNA sequences

Mycologia, Vol. 98, nº6

Partial classification of Lorenz knots: Syllable permutations of torus knots words

We define families of aperiodic words associated to Lorenz knots that arise naturally as syllable permutations of symbolic words corresponding to torus knots. An algorithm to construct symbolic words of satellite Lorenz knots is defined. We prove, subject to the validity of a previous conjecture, that Lorenz knots coded by some of these families of words are hyperbolic, by showing that they are neither satellites nor torus knots and making use of Thurston's theorem. Infinite families of hyperbolic Lorenz knots are generated in this way, to our knowledge, for the first time. The techniques used can be generalized to study other families of Lorenz knots.

Architecture to Support Quality of Service in Arrowhead Systems

Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Covilhã, Portugal.

Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. 2015 Elsevier B.V. Al

Classification of varieties of pasteurella pestis strains isolated in the interior of the state of Pernambuco-Brazil

Studies were made on the biochemical behavior of 100 strains of P.pestis isolated in Northeastern Brazil with regard to production of nitrous acid, reduction of nitrates to nitrltes, and aciáification of glycerol. Results showed that 98 strains can be classified as "orientalis variety", while the remaining two could not be included in any of the existing "varieties".

Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Retrospective histopathological classification of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil

We report a retrospective histopathological classification carried out under laboratory conditions by the method of Ridley & Jopling of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil.

Using the International Classification of Functioning, Disability and Health as a tool for analysis of the effect of physical therapy on spasticity in HAM/TSP patients

INTRODUCTION: This study aimed to evaluate spasticity in human T-lymphotropic virus type 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) patients before and after physical therapy using the International Classification of Functioning, Disability and Health (ICF). METHODS: Nine subjects underwent physical therapy. Spasticity was evaluated using the Modified Ashworth Scale. The obtained scores were converted into ICF body functions scores. RESULTS: The majority of subjects had a high degree of spasticity in the quadriceps muscles. According to the ICF codes, the spasticity decreased after 20 sessions of physical therapy. CONCLUSIONS: The ICF was effective in evaluating spasticity in HAM/TSP patients.

Evaluation of the World Health Organization 2009 classification of dengue severity in autopsied individuals, during the epidemics of 2011 and 2012 in Brazil

Abstract: INTRODUCTION: The dengue classification proposed by the World Health Organization (WHO) in 2009 is considered more sensitive than the classification proposed by the WHO in 1997. However, no study has assessed the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue. In the present study, we evaluated the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue in Northeast Brazil, where the disease is endemic. METHODS: This retrospective study included 121 autopsied individuals suspected of having dengue in Northeast Brazil during the epidemics of 2011 and 2012. All the autopsied individuals included in this study were confirmed to have dengue based on the findings of laboratory examinations. RESULTS: The median age of the autopsied individuals was 34 years (range, 1 month to 93 years), and 54.5% of the individuals were males. According to the WHO 1997 classification, 9.1% (11/121) of the cases were classified as dengue hemorrhagic fever (DHF) and 3.3% (4/121) as dengue shock syndrome. The remaining 87.6% (106/121) of the cases were classified as dengue with complications. According to the 2009 classification, 100% (121/121) of the cases were classified as severe dengue. The absence of plasma leakage (58.5%) and platelet counts <100,000/mm3 (47.2%) were the most frequent reasons for the inability to classify cases as DHF. CONCLUSIONS: The WHO 2009 classification is more sensitive than the WHO 1997 classification for identifying dengue deaths among autopsied individuals suspected of having dengue.