Predição de séries temporais de parâmetros de redes HSPA - WCDMA

With the growing demand of data traffic in the networks of third generation (3G), the mobile operators have attempted to focus resources on infrastructure in places where it identifies a greater need. The channeling investments aim to maintain the quality of service especially in dense urban areas. WCDMA - HSPA parameters Rx Power, RSCP (Received Signal Code Power), Ec/Io (Energy per chip/Interference) and transmission rate (throughput) at the physical layer are analyzed. In this work the prediction of time series on HSPA network is performed. The collection of values of the parameters was performed on a fully operational network through a drive test in Natal - RN, a capital city of Brazil northeastern. The models used for prediction of time series were the Simple Exponential Smoothing, Holt, Holt Winters Additive and Holt Winters Multiplicative. The objective of the predictions of the series is to check which model will generate the best predictions of network parameters WCDMA - HSPA.

Modelagem do consumo de energia de redes de sensores sem fio usando SystemC-AMS e stateflow

This work aims at modeling power consumption at the nodes of a Wireless Sensor Network (WSN). For doing so, a finite state machine was implemented by means of SystemC-AMS and Stateflow modeling and simulation tools. In order to achieve this goal, communication data in a WSN were collected. Based on the collected data, a simulation environment for power consumption characterization, which aimed at describing the network operation, was developed. Other than performing power consumption simulation, this environment also takes into account a discharging model as to analyze the battery charge level at any given moment. Such analysis result in a graph illustrating the battery voltage variations as well as its state of charge (SOC). Finally, a case study of the WSN power consumption aims to analyze the acquisition mode and network data communication. With this analysis, it is possible make adjustments in node-sensors to reduce the total power consumption of the network.

Ferramenta para avaliação e inferência de parâmetros de redes industriais sem fio

Wireless sensor networks (WSN) have gained ground in the industrial environment, due to the possibility of connecting points of information that were inaccessible to wired networks. However, there are several challenges in the implementation and acceptance of this technology in the industrial environment, one of them the guaranteed availability of information, which can be influenced by various parameters, such as path stability and power consumption of the field device. As such, in this work was developed a tool to evaluate and infer parameters of wireless industrial networks based on the WirelessHART and ISA 100.11a protocols. The tool allows quantitative evaluation, qualitative evaluation and evaluation by inference during a given time of the operating network. The quantitative and qualitative evaluation are based on own definitions of parameters, such as the parameter of stability, or based on descriptive statistics, such as mean, standard deviation and box plots. In the evaluation by inference uses the intelligent technique artificial neural networks to infer some network parameters such as battery life. Finally, it displays the results of use the tool in different scenarios networks, as topologies star and mesh, in order to attest to the importance of tool in evaluation of the behavior of these networks, but also support possible changes or maintenance of the system.

FTE-LEACH: um protocolo energeticamente eficiente e tolerante a falhas aplicado às redes industriais de sensores sem fio

A Wireless Sensor Network (WSN) consists of distributed devices in an area in order to monitor physical variables such as temperature, pressure, vibration, motion and environmental conditions in places where wired networks would be difficult or impractical to implement, for example, industrial applications of difficult access, monitoring and control of oil wells on-shore or off-shore, monitoring of large areas of agricultural and animal farming, among others. To be viable, a WSN should have important requirements such as low cost, low latency, and especially low power consumption. However, to ensure these requirements, these networks suffer from limited resources, and eventually being used in hostile environments, leading to high failure rates, such as segmented routing, mes sage loss, reducing efficiency, and compromising the entire network, inclusive. This work aims to present the FTE-LEACH, a fault tolerant and energy efficient routing protocol that maintains efficiency in communication and dissemination of data.This protocol was developed based on the IEEE 802.15.4 standard and suitable for industrial networks with limited energy resources

Critério de correntropia no treinamento de redes fuzzy wavelet neural networks para identificação de sistemas dinâmicos não lineares

The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

Contribuições em escalonamento e análise de desempenho de redes WirelessHART

Wireless Communication is a trend in the industrial environment nowadays and on this trend, we can highlight the WirelessHART technology. In this situation, it is natural the search for new improvements in the technology and such improvements can be related directly to the routing and scheduling algorithms. In the present thesis, we present a literature review about the main specific solutions for Routing and scheduling for WirelessHART. The thesis also proposes a new scheduling algorithm called Flow Scheduling that intends to improve superframe utilization and flexibility aspects. For validation purposes, we develop a simulation module for the Network Simulator 3 (NS-3) that models aspects like positioning, signal attenuation and energy consumption and provides an link individual error configuration. The module also allows the creation of the scheduling superframe using the Flow and Han Algorithms. In order to validate the new algorithms, we execute a series of comparative tests and evaluate the algorithms performance for link allocation, delay and superframe occupation. In order to validate the physical layer of the simulation module, we statically configure the routing and scheduling aspects and perform reliability and energy consumption tests using various literature topologies and error probabilities.

Contribuições em escalonamento e análise de desempenho de redes WirelessHART

Wireless Communication is a trend in the industrial environment nowadays and on this trend, we can highlight the WirelessHART technology. In this situation, it is natural the search for new improvements in the technology and such improvements can be related directly to the routing and scheduling algorithms. In the present thesis, we present a literature review about the main specific solutions for Routing and scheduling for WirelessHART. The thesis also proposes a new scheduling algorithm called Flow Scheduling that intends to improve superframe utilization and flexibility aspects. For validation purposes, we develop a simulation module for the Network Simulator 3 (NS-3) that models aspects like positioning, signal attenuation and energy consumption and provides an link individual error configuration. The module also allows the creation of the scheduling superframe using the Flow and Han Algorithms. In order to validate the new algorithms, we execute a series of comparative tests and evaluate the algorithms performance for link allocation, delay and superframe occupation. In order to validate the physical layer of the simulation module, we statically configure the routing and scheduling aspects and perform reliability and energy consumption tests using various literature topologies and error probabilities.

Implementação de uma matriz de neurônios dinamicamente reconfigurável para descrição de topologias de redes neurais artificiais multilayer perceptrons

The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.

Implementação de uma matriz de neurônios dinamicamente reconfigurável para descrição de topologias de redes neurais artificiais multilayer perceptrons

The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Estudo e desenvolvimento de algoritmos criptográficos para redes de sensores sem fio, utilizando técnicas de programação genética

Cryptography is the main form to obtain security in any network. Even in networks with great energy consumption restrictions, processing and memory limitations, as the Wireless Sensors Networks (WSN), this is no different. Aiming to improve the cryptography performance, security and the lifetime of these networks, we propose a new cryptographic algorithm developed through the Genetic Programming (GP) techniques. For the development of the cryptographic algorithm’s fitness criteria, established by the genetic GP, nine new cryptographic algorithms were tested: AES, Blowfish, DES, RC6, Skipjack, Twofish, T-DES, XTEA and XXTEA. Starting from these tests, fitness functions was build taking into account the execution time, occupied memory space, maximum deviation, irregular deviation and correlation coefficient. After obtaining the genetic GP, the CRYSEED and CRYSEED2 was created, algorithms for the 8-bits devices, optimized for WSNs, i.e., with low complexity, few memory consumption and good security for sensing and instrumentation applications.

Estratégias distribuídas para a coexistência de redes de banda larga sem fio em bandas não-licensiadas

The increasing demand for Internet data traffic in wireless broadband access networks requires both the development of efficient, novel wireless broadband access technologies and the allocation of new spectrum bands for that purpose. The introduction of a great number of small cells in cellular networks allied to the complimentary adoption of Wireless Local Area Network (WLAN) technologies in unlicensed spectrum is one of the most promising concepts to attend this demand. One alternative is the aggregation of Industrial, Science and Medical (ISM) unlicensed spectrum to licensed bands, using wireless networks defined by Institute of Electrical and Electronics Engineers (IEEE) and Third Generation Partnership Project (3GPP). While IEEE 802.11 (Wi-Fi) networks are aggregated to Long Term Evolution (LTE) small cells via LTE / WLAN Aggregation (LWA), in proposals like Unlicensed LTE (LTE-U) and LWA the LTE air interface itself is used for transmission on the unlicensed band. Wi-Fi technology is widespread and operates in the same 5 GHz ISM spectrum bands as the LTE proposals, which may bring performance decrease due to the coexistence of both technologies in the same spectrum bands. Besides, there is the need to improve Wi-Fi operation to support scenarios with a large number of neighbor Overlapping Basic Subscriber Set (OBSS) networks, with a large number of Wi-Fi nodes (i.e. dense deployments). It is long known that the overall Wi-Fi performance falls sharply with the increase of Wi-Fi nodes sharing the channel, therefore there is the need for introducing mechanisms to increase its spectral efficiency. This work is dedicated to the study of coexistence between different wireless broadband access systems operating in the same unlicensed spectrum bands, and how to solve the coexistence problems via distributed coordination mechanisms. The problem of coexistence between different networks (i.e. LTE and Wi-Fi) and the problem of coexistence between different networks of the same technology (i.e. multiple Wi-Fi OBSSs) is analyzed both qualitatively and quantitatively via system-level simulations, and the main issues to be faced are identified from these results. From that, distributed coordination mechanisms are proposed and evaluated via system-level simulations, both for the inter-technology coexistence problem and intra-technology coexistence problem. Results indicate that the proposed solutions provide significant gains when compare to the situation without distributed coordination.

Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities

Desenvolvimento de sistemas embarcados para redes de sensores e atuadores sem fio aplicadas em unidades de elevação de petróleo do tipo Plunger-Lift

The Wireless Sensor Networks (WSN) methods applied to the lifting of oil present as an area with growing demand technical and scientific in view of the optimizations that can be carried forward with existing processes. This dissertation has as main objective to present the development of embedded systems dedicated to a wireless sensor network based on IEEE 802.15.4, which applies the ZigBee protocol, between sensors, actuators and the PLC (Programmable Logic Controller), aiming to solve the present problems in the deployment and maintenance of the physical communication of current elevation oil units based on the method Plunger-Lift. Embedded systems developed for this application will be responsible for acquiring information from sensors and control actuators of the devices present at the well, and also, using the Modbus protocol to make this network becomes transparent to the PLC responsible for controlling the production and delivery information for supervisory SISAL

Um sistema para a inferência de variáveis usando redes neurais artificiais

This work aims to obtain a low-cost virtual sensor to estimate the quality of LPG. For the acquisition of data from a distillation tower, software HYSYS ® was used to simulate chemical processes. These data will be used for training and validation of an Artificial Neural Network (ANN). This network will aim to estimate from available simulated variables such as temperature, pressure and discharge flow of a distillation tower, the mole fraction of pentane present in LPG. Thus, allowing a better control of product quality