(m,k)-Gur game: Exploiting message discards in order to increase the lifetime and spatial diversity in autonomic wireless sensor networks

The main concern in Wireless Sensor Networks (WSN) algorithms and protocols are the energy consumption. Thus, the WSN lifetime is one of the most important metric used to measure the performance of the WSN approaches. Another important metric is the WSN spatial coverage, where the main goal is to obtain sensed data in a uniform way. This paper has proposed an approach called (m,k)-Gur Game that aims a trade-off between quality of service and the increasement of spatial coverage diversity. Simulation results have shown the effectiveness of this approach. © 2012 IEEE.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Wireless sensor networks Zigbee applied on sewage treatment station

The good efficiency in a sewage treatment plant (WWTP) is a great importance to the environment. The management of electromechanical equipment installed in these stations is a major challenge due to the fact that they are installed on areas of difficult access and maintenance unhealthy and making the time for the correction of any faults is extended. This paper proposes the development of a Wireless Sensor Network (WSN), in order to monitor electromechanical equipment, allowing the Concessionaire a predictive control in real time. The design of a wireless sensors network for monitoring equipment requires not only the development and assembly of the sensor modules, but must also include the development of software for managing the data collected. Thus, this work includes a Zigbee WSN, small, adapted for monitoring of electromechanical equipment and environmental conditions of a WWTP, type stabilization pond, installed in an area of approximately 0.15 km 2 and the average flow of 320 liters of treatment per second. The experimental results show that this monitoring system can perform with the collection of parameters of performance and quality assessment at the station.

A transmission power self-optimization technique for wireless sensor networks

Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.

Design and implementation of wireless sensor networks for impedance-based structural health monitoring using ZigBee and Global System for Mobile Communications

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Self-optimization of dense wireless sensor networks based on simulated annealing

Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Influence of mobility models in precision spray aided by wireless sensor networks

Precision Spray is a technique to increase performance of Precision Agriculture. This spray technique may be aided by a Wireless Sensor Network, however, for such approach, the communication between the agricultural input applicator vehicle and network is critical due to its proper functioning. Thus, this work analyzes how the number of nodes in a wireless sensor network, its type of distribution and different areas of scenario affects the performance of communication. We performed simulations to observe system's behavior changing to find the most fitted non-controlled mobility model to the system.

Organization model for mobile wireless sensor networks inspired in artificial bee colony

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Surface coverage in wireless sensor networks based on delaunay tetrahedralization

In this work is presented a new method for sensor deployment on 3D surfaces. The method was structured on different steps. The first one aimed discretizes the relief of interest with Delaunay algorithm. The tetrahedra and relative values (spatial coordinates of each vertex and faces) were input to construction of 3D Voronoi diagram. Each circumcenter was calculated as a candidate position for a sensor node: the corresponding circular coverage area was calculated based on a radius r. The r value can be adjusted to simulate different kinds of sensors. The Dijkstra algorithm and a selection method were applied to eliminate candidate positions with overlapped coverage areas or beyond of surface of interest. Performance evaluations measures were defined using coverage area and communication as criteria. The results were relevant, once the mean coverage rate achieved on three different surfaces were among 91% and 100%.

Integration of wireless sensor networks to the internet of things using a 6LoWPAN Gateway

The Internet of Things is a new paradigm where smart embedded devices and systems are connected to the Internet. In this context, Wireless Sensor Networks (WSN) are becoming an important alternative for sensing and actuating critical applications like industrial automation, remote patient monitoring and domotics. The IEEE 802.15.4 protocol has been adopted as a standard for WSN and the 6LoWPAN protocol has been proposed to overcome the challenges of integrating WSN and Internet protocols. In this paper, the mechanisms of header compression and fragmentation of IPv6 datagrams proposed in the 6LoWPAN standard were evaluated through field experiments using a gateway prototype and IEEE 802.15.4 nodes.

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

EthoSeq: A tool for phylogenetic analysis and data mining in behavioral sequences

This article introduces the software program called EthoSeq, which is designed to extract probabilistic behavioral sequences (tree-generated sequences, or TGSs) from observational data and to prepare a TGS-species matrix for phylogenetic analysis. The program uses Graph Theory algorithms to automatically detect behavioral patterns within the observational sessions. It includes filtering tools to adjust the search procedure to user-specified statistical needs. Preliminary analyses of data sets, such as grooming sequences in birds and foraging tactics in spiders, uncover a large number of TGSs which together yield single phylogenetic trees. An example of the use of the program is our analysis of felid grooming sequences, in which we have obtained 1,386 felid grooming TGSs for seven species, resulting in a single phylogeny. These results show that behavior is definitely useful in phylogenetic analysis. EthoSeq simplifies and automates such analyses, uncovers much of the hidden patterns of long behavioral sequences, and prepares this data for further analysis with standard phylogenetic programs. We hope it will encourage many empirical studies on the evolution of behavior.

Comparative studies of the sample decomposition of green and roasted coffee for determination of nutrients and data exploratory analysis

The contents of some nutrients in 35 Brazilian green and roasted coffee samples were determined by flame atomic absorption spectrometry (Ca, Mg, Fe, Cu, Mn, and Zn), flame atomic emission photometry (Na and K) and Kjeldahl (N) after preparing the samples by wet digestion procedures using i) a digester heating block and ii) a conventional microwave oven system with pressure and temperature control. The accuracy of the procedures was checked using three standard reference materials (National Institute of Standards and Technology, SRM 1573a Tomato Leaves, SRM 1547 Peach Leaves, SRM 1570a Trace Elements in Spinach). Analysis of data after application of t-test showed that results obtained by microwave-assisted digestion were more accurate than those obtained by block digester at 95% confidence level. Additionally to better accuracy, other favorable characteristics found were lower analytical blanks, lower reagent consumption, and shorter digestion time. Exploratory analysis of results using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) showed that Na, K, Ca, Cu, Mg, and Fe were the principal elements to discriminate between green and roasted coffee samples. ©2007 Sociedade Brasileira de Química.

The Brazilian Seismographic Integrated Systems (BRASIS): Infrastructure and data management

In geophysics and seismology, raw data need to be processed to generate useful information that can be turned into knowledge by researchers. The number of sensors that are acquiring raw data is increasing rapidly. Without good data management systems, more time can be spent in querying and preparing datasets for analyses than in acquiring raw data. Also, a lot of good quality data acquired at great effort can be lost forever if they are not correctly stored. Local and international cooperation will probably be reduced, and a lot of data will never become scientific knowledge. For this reason, the Seismological Laboratory of the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of São Paulo (IAG-USP) has concentrated fully on its data management system. This report describes the efforts of the IAG-USP to set up a seismology data management system to facilitate local and international cooperation. © 2011 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved.