Hannover 2008 V2

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Viene descritta la piattaforma EiAGRID/SmartGeo, un portale di calcolo e analisi dati per sismica a riflessione e acquisizioni GPR multioffset, che mette a disposizione dell'utente una serie di servizi di calcolo e di processing accessibili attraverso un'interfaccia Web basata su un'infrastruttura Grid. La piattaforma consente all'utente in campo, tramite un dispositivo client (laptop, PC, tablet, etc.), di usufruire di una serie di servizi computazionali che risiedono e girano su server remoti, secondo il paradigma SaaS (Software as a Service). Verranno illustrate le soluzioni modellistiche e tecnologiche adottate e alcuni risultati ottenuti su dati reali.

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Provenance, from the French word “provenir”, describes the lineage or histo-ry of a data entity. Provenance is critical information in the sensors domain to identify a sensor and analyze the observation data over time and geographical space. In this paper, we present a framework to model and query the provenance information associated with the sensor data exposed as part of the Web of Data using the Linked Open Data conventions. This is accomplished by developing an ontology-driven provenance man-agement infrastructure that includes a representation model and query infrastructure. This provenance infrastructure, called Sensor Provenance Management System (PMS), is underpinned by a domain specific provenance ontology called Sensor Provenance (SP) ontology. The SP ontology extends the Provenir upper level provenance ontology to model domain-specific provenance in the sensor domain. In this paper, we describe the implementation of the Sensor PMS for provenance tracking in the Linked Sensor Data. Authors - Harshal Patni, Satya S. Sahoo, Cory Henson, Amit Sheth

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A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. In this paper, we present a framework to make this sensor data openly accessible by publishing it on the Linked Open Data (LOD) Cloud. This is accomplished by converting raw sensor observations to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis. Authors - Harshal Patni, Cory Henson, Amit Sheth * First attempt on Linked Sensor Data

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Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region. Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.

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The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network is used.

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Climate change has caused an increasing number of landslides, especially in the mountainous provinces of Vietnam, resulting in the destruction of vital transport and other infrastructure. Current monitoring and forecasting systems of the meteorology department cannot deliver accurate and reliable forecasts for weather events and issue timely warnings. This paper describes the development of a simple, low cost, and efficient system for monitoring and warning landslide in real-time. The authors focus on the use of wireless and related technologies in the implementation of a technical solution and some of the problems of the wireless sensor network (WSN) related to power consumption. Promising compressed sensing (CS) based solution for landslide monitoring is discussed and evaluated in the paper.

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Landslides are natural disasters cause losses in many human lives and damage properties every year around the globe. Using of physical and electronic monitoring makes possible in prediction and prevention losses from landslide. Today, the wireless sensor network technology has been developed rapidly for landslide predictions. The primary object of this present paper is to overview the landslide prediction method using wireless sensor networks (WSN) and geographical information systems (GIS). The paper focuses on various landslide conditioning factors, WSN design requirement, and small scale down slope model similar to study area (hazard location). Landslide prediction database to support warning system are also discussed.

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Disasters like earthquake, landslide, flood etc. typically generate enormous amount of debris in the disaster struck area. As a result of these disasters, there will be destruction of properties like home, public infrastructure which results in insecurity, loss of life and property and also interrupts public services. Wireless Sensor Networks play a vital role in disaster debris detection and management. Debris flows usually occurs in mountain regions having steep front. They comprise a huge congregation of solid matter like mud, soil are other waste generated due to earthquake, landslide etc. in water that flows down in the form of a wave. Landslides, earthquake and floods generate debris. They are amongst the foremost vulnerable natural disasters in mountainous torrents. Different factors are attributable to quality of debris flow: firstly their potentiality of transportation and deposition of enormous quantity of solid matters, which can additionally approach gigantic sizes, their sheer fronts, can attain huge meters of altitude and additionally their high speeds and depth of flow. To mitigate the effects of debris, a disaster debris management system is designed containing an different sensors, Nmote and an N-gateway to measure various parameters of debris flow and an alert is sent to the public and rescue team by using cloud services in advance so that there is less loss of life and property.

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This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.

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This paper deals in building a 3D vision-based obstacle avoidance and navigation. In order for an autonomous system to work in real life condition, a capability of gaining surrounding environment data, interpret the data and take appropriate action is needed. One of the required capability in this matter for an autonomous system is a capability to navigate cluttered, unorganized environment and avoiding collision with any present obstacle, defined as any data with vertical orientation and able to take decision when environment update exist. Proposed in this work are two-step strategy of extracting the obstacle position and orientation from point cloud data using plane based segmentation and the resultant segmentation are mapped based on obstacle point position relative to camera using occupancy grid map to acquire obstacle cluster position and recorded the occupancy grid map for future use and global navigation, obstacle position gained in grid map is used to plan the navigation path towards target goal without going through obstacle position and modify the navigation path to avoid collision when environment update is present or platform movement is not aligned with navigation path based on timed elastic band method.

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In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

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