In disaster situations communication and power infrastructure could be damaged, disrupting their associated services. This could delay relief efforts and cost more lives. In situations where connections are damaged an...
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Cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. Clouds rely on proper management to provide such computational resou...
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Cloud computing is a suitable solution for professionals, companies, and institutions that need to have access to computational resources on demand. Clouds rely on proper management to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical function to achieve such proper management. Cloud monitoring systems have to accomplish requirements to perform its functions properly, and currently, there are plenty of requirements which includes: timeliness, adaptability, comprehensiveness, and scalability. However, such requirements usually have mutual influence, which is positive or negative, among them-selves, and it has prevented the development of complete cloud monitoring solutions. This paper presents a mathematical model to predict the mutual influence between timeliness and scalability, which is a step forward in cloud monitoring because it paves the way for the development of complete monitoring solutions. It complements our previous work that identified the monitoring parameters (e.g., frequency sampling, amount of monitoring data) that influence timeliness and scalability. Evaluations present the effectiveness of the mathematical model based on a comparison of the results provided by the mathematical model and the results obtained via simulation.
One of the great prospects of exascale computing is to simulate challenging highly complex multi-physics scenarios with different length and time scales. A modular approach re-using existing software for the single-ph...
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Scalable locking is a key building block for scalable multi-threaded software. Its performance is especially critical in multi-socket, multi-core machines with non-uniform memory access (NUMA). Previous schemes such a...
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Localization and tracking of small animals in the wild using sensor networks require nodes with ultra-low power consumption, which are particularly challenging to design. Here, we target the tracking of bats in their ...
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ISBN:
(纸本)9781467398152
Localization and tracking of small animals in the wild using sensor networks require nodes with ultra-low power consumption, which are particularly challenging to design. Here, we target the tracking of bats in their natural habitat and have to limit the weight of the mote to 2g. To optimize the energy consumption in this scenario, the combination of data communication and ranging is essential. The limitations of the platform and the specific use case ask for a dedicated signal design. We start exploring the use of Binary Offset Carrier (BOC), which is known to be well suited for localization. In this paper, we concentrate on the data communication part of the system. We develop a BOC transceiver in Software Defined Radio (SDR) and perform simulations as well as lab measurements to evaluate its performance and compare it to Binary Phase-Shift Keying (BPSK), which is often used in low-power sensor systems. Most importantly, we conducted realistic field measurements to study the effects of multipath fading and shadowing. Our results clearly show that BOC is perfectly suited for ultra-low power communication in forest environments.
Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living ...
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The demand for computing resources to process the geographical information (GI) queries has been increased drastically. The query helps the users to get the variety of information to serve their needs. Resolving the s...
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ISBN:
(纸本)9781509045747
The demand for computing resources to process the geographical information (GI) queries has been increased drastically. The query helps the users to get the variety of information to serve their needs. Resolving the spatial queries, huge number of heterogeneous data sources along with different computing services are involved. Getting appropriate results within a specific time bound, orchestration among those data sources and web services are required. These services are available on the web and require different resource specifications in order to resolve a geospatial query. A cloud infrastructure has been utilized for scalable resource allocation. An orchestration engine has been developed to access the geospatial resources according to query requirement. In this paper, we have proposed and developed geographical data query processing framework which orchestrates spatial services according to user query in cloud environment. The empirical experimentation shows the efficiency of the proposed framework to resolve spatial queries in timely manner.
We present a coupled simulation approach for fluid–structure–acoustic interactions (FSAI) as an example for strongly surface coupled multi-physics problems. In addition to the multi-physics character, FSAI feature m...
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Due to today's product complexity and variety and a shortening of development cycles, for instance, in the automotive domain, a conventional knowledge representation and management of the various design methods is...
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ISBN:
(纸本)9789897581113
Due to today's product complexity and variety and a shortening of development cycles, for instance, in the automotive domain, a conventional knowledge representation and management of the various design methods is not reasonable anymore. By acquiring the relevant domain and business knowledge from IT applications, documents and experts and creating ontologies, business rules and queries thereof, domain experts can manage this knowledge independently and thus react faster to the ever-changing development process. Using SemanticWeb technologies, we created a methods ontology that enables domain experts to analyze and compare method meta knowledge, e.g., about physical and virtual CAx methods. Furthermore, this particular domain and business knowledge features interdependencies with the remaining enterprise knowledge, including business processes, organizational aspects and the IT architecture. Therefore, we show concepts for the integration of this method meta knowledge into an enterprise architecture.
The wide adoption of smart devices has stimulated a fast shift of security-critical data from desktop to mobile devices. However, recurrent device theft and loss expose mobile devices to various security threats and e...
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