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Geoinformatics_03-2009
article grid computing and gis when looking at a brief history of grid technologies, it can be easily seen that it has been rapidly evolving since its inception in the early 1990s. at that time primary concerns were the issues of integrating large-scale computational resources and services. the increasing diversity of computational and human resources created a grid problem , which required new mechanisms for sharing resources dynamically. accordingly, efforts were started to deploy several gigabit test-beds to link supercomputing sites across the usa, an approach known as metasystem and metacomputing. the grid is not only a computing paradigm for providing computational resources but also a distributed computing infrastructure that supports flexible, secure, coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. the grid was initially designed for sharing geographically distributed high-end com- putational power, resources and persistent infrastructure for advanced science and engineering research applications. but emerged with the vision of sharing computing resources like content on the web. today, grid computing is considered the third information technology wave after the internet and web. research in this field will build the main structure of the next generation of services and applications that are going to further the research and development of gis and related areas. with this approach, parallel processing is the key item which allows for the use of multiple cpus to execute different sections of a program at the same time. to solve performance issues in gis solutions there has been a lot of research into the area of parallel processing of gis information. this involves the utilization of a single computer with multiple processors or multiple computers that are connected over a network working on the same task. there are many different types of distributed computing, two of the most common are clustering and grid processing. interrelated and evolving hazardous phenomena (multi-risk) environmental risk models for predicting the evolution of hazardous phenomena using a robust grid computing infrastructure public-private sector cooperation (e.g., house/land owner, security-company) as an "active player" in the uri zone protection and the monitoring of hazardous events each citizen has to be informed of the potential disaster (fire and flood) he may be subjected to. this information has to enable the citizen to: confirm the reception of the message and thus his/her presence in the area anticipate his/her actions to optimize safety and protection of belongings be informed of an action check list to follow inform other citizens (tourists) of the incoming danger the dynamic follow-up of the disaster must be done in a bilateral way. indeed, the citizen who receives from the authorities the evolution of the disaster will in turn be able to inform the authorities on the situation and scope of the disaster. if the citizen is absent, the authorities then know that they are the only ones in charge of protecting the person s belongings. this information is extremely important to establish the most appropriate action to be taken. sensor networks wireless sensor networks have attracted many research efforts over the past few years. sensor networks, usually composed of a few sink nodes and a large quantity of inexpensive and small sensor nodes, have been deployed in a variety of applications, such as habitat monitoring, forest fire detection, etc. in this context, the sensing system of the scier is composed of two components, the wireless sensor network (wsn) and the sensing system proxy. the role of the wireless sensor network is to collect measurements from the deployed sensors and to send them to a base, through a sink node, using wireless transmission. the sensing system proxy provides the access point to the local alerting control units (lacu) and the higher layers of the scier system in general. it is the front-end of the sensor network: it collects all data and passes them to the lacu in the appropriate format. it also receives and executes commands coming from the lacu (or the scier higher layers). the lacus are deployed all over the area that needs to be secured against potential emergency situations, arising from environmental risks, such as forest fires or floods. lacu controls a network of variable type sensors, receives input from them, runs fusion algorithms on the received data and sends the fused data to the computing subsystem (cs) for further processing. from an architectural point of view the lacu can be thought of as a set-top-box device, which stands between the back-end infrastructure (computing subsystem) and the sensors that have been deployed in the web user interface (wui). in scier, two types of lacus are identified: public lacu (p-lacu). this type of lacu is installed and operated by public authorities and controls a wireless network of public owned sensors. private lacu (r-lacu). this type of lacu controls citizen owned sensors and is installed by individuals in order to protect their private properties. the third release of the sensing system extends the release specifications 1 and 2. rs1 and rs2 comprised an operational sensing system that integrated a couple of in-field sensors as well as a vision sensor (out-of-field sensor). the sensors were able to regularly report measurements and respond to requests coming from the lacu. rs3 extends the system functionality mainly by finalizing the operation of the vision april/may 2009 figure 3: flood model results latest news? visit www.geoinformatics.com 47
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