170,658 research outputs found

    Distributed Sparse Signal Recovery For Sensor Networks

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    We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective measurements at a minimal communication cost and with low computational complexity. A naive distributed implementation of IHT would require global communication of every agent's full state in each iteration. We find that we can dramatically reduce this communication cost by leveraging solutions to the distributed top-K problem in the database literature. Evaluations show that our algorithm requires up to three orders of magnitude less total bandwidth than the best-known distributed basis pursuit method

    Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

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    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors

    Multi-Sensor System for Land and Forest Fire Detection Application in Peatland Area

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    Forest fire has a dangerous impact on environments and humans because of haze and carbon emitted from it. A common technology to detect fire hotspots is to use satellite images and then process them to determine the number of hotspots and their location. However, satellite systems cannot penetrate in bad weather or cloudy condition. This research proposes a ground sensor system, which uses several sensors related to the indicators of fire, especially fire in peatland area with unique characteristics. Common parameters of fire, such as temperature, smoke, haze, and carbon dioxide, are applied in this system. Indicators are measured using special sensors. Results of every sensor are analyzed by implementing intelligent computer programming, and an algorithm to determine fire hotspots and locations is applied. The fire hotspot location and intensity determined by integrated multiple sensors are more accurate than those determined by a single sensor. Data collected from every sensor are kept in a database, and a graph is generated for reporting and recording. In case of sensor readings with parameters, potential of fire and hotspots detected can be forwarded to the representative department for corresponding actions

    Penerapan Fuzzy Tsukamoto pada Alat Deteksi Penyakit Hipoksemia, Hipotermia, Hipertensi, dan Diabetes untuk Health Care Kiosk

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    Most of people in Indonesia need fast, right, and accurate health medical service. But as we know in hospital takes many time just for check our health condition. This research make a Health Care Kiosk for medical check up, without using a doctor, so that kiosk can detect many deseases automatically. This research focused on 4 deseases such as hypothermia, hypoxemia, hypertension and diabetes. System using Embedded PC for data processing automatically. There are many medical sensor such as thermometer, heart rate sensor, blood pressure sensor, SPO2 sensor, and glucometer sensor for check health condition. System can make a decision if that patient healthy or not automatically because it uses fuzzy method for that decision. The result of this paper is this system can detect every deseases and that error for each sensor are body temperature has 1.05% error, oxygen level has 1.90% error, heart rate has 5.78% error, blood pressure sistolic has 4.16% error, blood pressure diastolic has 4.87% error and glucosa level in blood has 4.01% error. This system integrated with database MySQL for save that result. The accuracy from fuzzy method is 100% right and fuzzy tsukamoto can process input well.Most of people in Indonesia need fast, right, and accurate health medical service. But as we know in hospital takes many time just for check our health condition. This research make a Health Care Kiosk for medical check up, without using a doctor, so that kiosk can detect many deseases automatically. This research focused on 4 deseases such as hypothermia, hypoxemia, hypertension and diabetes. System using Embedded PC for data processing automatically. There are many medical sensor such as thermometer, heart rate sensor, blood pressure sensor, SPO2 sensor, and glucometer sensor for check health condition. System can make a decision if that patient healthy or not automatically because it uses fuzzy method for that decision. The result of this paper is this system can detect every deseases and that error for each sensor are body temperature has 1.05% error, oxygen level has 1.90% error, heart rate has 5.78% error, blood pressure sistolic has 4.16% error, blood pressure diastolic has 4.87% error and glucosa level in blood has 4.01% error. This system integrated with database MySQL for save that result. The accuracy from fuzzy method is 100% right and fuzzy tsukamoto can process input well

    Using the Advantages of NOSQL: A Case Study on MongoDB

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    With such a big volume of data growing tremendously every day, the storage of information, support and maintenance have become difficult. A further level of difficulty is added by the variety of data being captured. The data stored and updated on daily bases is in the form of logs, audio, video, sensor data and so on, which is not easy to be stored and queried using relational database. The paper gives the overview of NOSQL databases which provide more scalability and efficiency in storage and access of the data. A case study on MongoDB is done as to show the representational format and querying process of NOSQL database. The concepts of MongoDB are compared to the relational databases

    Service oriented architecture for real time data fusion.

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    This project will provide a service-oriented architecture to handle sensor data in real time as the information comes in. There are two types of sensors we\u27re implementing into our project, mobile sensors and stationary sensors. These sensors attach unto motes to gather data about temperature, light and acoustics. The fusion part of the topic is taking both types of sensors, bringing the data together and storing the data in a SQL Database. This project will focus on the gathering, storing and preprocessing of the data. The data from the sensors is stored every three minutes using the BULK INSERT command. We found that storing the data every three minutes is about the most efficient for our implementation

    A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing

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    The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically. In this paper, we present a wireless sensor network where each sensor node takes images from the field and internally uses image processing techniques to detect any unusual status in the leaves. This symptom could be caused by a deficiency, pest, disease or other harmful agent. When it is detected, the sensor node sends a message to a sink node through the wireless sensor network in order to notify the problem to the farmer. The wireless sensor uses the IEEE 802.11 a/b/g/n standard, which allows connections from large distances in open air. This paper describes the wireless sensor network design, the wireless sensor deployment, how the node processes the images in order to monitor the vineyard, and the sensor network traffic obtained from a test bed performed in a flat vineyard in Spain. Although the system is not able to distinguish between deficiency, pest, disease or other harmful agents, a symptoms image database and a neuronal network could be added in order learn from the experience and provide an accurate problem diagnosis

    Development of a prototype for remote current measurements of PV panel using WSN

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    Sensing, monitoring, actuating and information retrieving are expected to play a key role in smart grid energy management strategy. For energy consumption metering, power measurement systems generally implement transformers contactless current sensors but also have a wide variety of technologies especially for integrating renewable generations. One of the key problems of future smart grid is to develop data communication system for distributed intermittent renewable generations to build an efficient energy management and demand response system. In this paper, a test bed has been developed using Wireless Sensor Network (WSN) based on IEEE 802.15.4 standard for remote real time monitoring of current production in a distributed Photovoltaic (PV) plant. ZigBee based WSN is integrated with Arduino microcontroller and current sensor to sense produce current by PV at every moment and forward this data to control unit instantaneously. In the control unit, a LabVIEW based program is developed to receive the data and store in to a database for further processing of energy management by the control unit
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