1,108 research outputs found

    Operation of Modular Smart Grid Applications Interacting through a Distributed Middleware

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    IoT-functionality can broaden the scope of distribution system automation in terms of functionality and communication. However, it also poses risks regarding resource consumption and security. This article presents a field approved IoT-enabled smart grid middleware, which allows for flexible deployment and management of applications within smart grid operation. In the first part of the work, the resource consumption of the middleware is analyzed and current memory bottlenecks are identified. The bottlenecks can be resolved by introducing a new entity that allows to dynamically load multiple applications within one JVM. The performance was experimentally tested and the results suggest that its application can significantly reduce the applications' memory footprint on the physical device. The second part of the study identifies and discusses potential security threats, with a focus on attacks stemming from malicious software applications within the framework. In order to prevent such attacks a proxy based prevention mechanism is developed and demonstrated

    Remote Data Retrieval for Bioinformatics Applications: An Agent Migration Approach

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    Some of the approaches have been developed to retrieve data automatically from one or multiple remote biological data sources. However, most of them require researchers to remain online and wait for returned results. The latter not only requires highly available network connection, but also may cause the network overload. Moreover, so far none of the existing approaches has been designed to address the following problems when retrieving the remote data in a mobile network environment: (1) the resources of mobile devices are limited; (2) network connection is relatively of low quality; and (3) mobile users are not always online. To address the aforementioned problems, we integrate an agent migration approach with a multi-agent system to overcome the high latency or limited bandwidth problem by moving their computations to the required resources or services. More importantly, the approach is fit for the mobile computing environments. Presented in this paper are also the system architecture, the migration strategy, as well as the security authentication of agent migration. As a demonstration, the remote data retrieval from GenBank was used to illustrate the feasibility of the proposed approach

    Reconfigurable middleware architectures for large scale sensor networks

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    Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration

    BioClimate: a Science Gateway for Climate Change and Biodiversity research in the EUBrazilCloudConnect project

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    [EN] Climate and biodiversity systems are closely linked across a wide range of scales. To better understand the mutual interaction between climate change and biodiversity there is a strong need for multidisciplinary skills, scientific tools, and access to a large variety of heterogeneous, often distributed, data sources. Related to that, the EUBrazilCloudConnect project provides a user-oriented research environment built on top of a federated cloud infrastructure across Europe and Brazil, to serve key needs in different scientific domains, which is validated through a set of use cases. Among them, the most data-centric one is focused on climate change and biodiversity research. As part of this use case, the BioClimate Science Gateway has been implemented to provide end-users transparent access to (i) a highly integrated user-friendly environment, (ii) a large variety of data sources, and (iii) different analytics & visualization tools to serve a large spectrum of users needs and requirements. This paper presents a complete overview of BioClimate and the related scientific environment, in particular its Science Gateway, delivered to the end-user community at the end of the project.This work was supported by the EU FP7 EUBrazilCloudConnect Project (Grant Agreement 614048), and CNPq/Brazil (Grant Agreement no 490115/2013-6).Fiore, S.; Elia, D.; Blanquer Espert, I.; Brasileiro, FV.; Nuzzo, A.; Nassisi, P.; Rufino, LAA.... (2019). BioClimate: a Science Gateway for Climate Change and Biodiversity research in the EUBrazilCloudConnect project. Future Generation Computer Systems. 94:895-909. https://doi.org/10.1016/j.future.2017.11.034S8959099

    Optimized Error Detection in Cloud User for Networking Services

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    Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN
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