Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 4 Articles
Background: In the context of smart cities, public participation and citizen science are key ingredients for informed\nand intelligent planning decisions and policy-making. However, citizens face a practical challenge in formulating\ncoherent information sets from the large volumes of data available to them. These large data volumes materialise due\nto the increased utilisation of information and communication technologies in urban settings and local authorities�\nreliance on such technologies to govern urban settlements efficiently. To encourage effective public participation in\nurban governance of smart cities, the public needs to be facilitated with the right contextual information about the\ncharacteristics and processes of their urban surroundings in order to contribute to the aspects of urban governance\nthat affect them such as socio-economic activities, quality of life, citizens well-being etc. The cities on the other hand\nface challenges in terms of crowd sourcing with quality data collection and standardisation, services inter-operability,\nprovisioning of computational and data storage infrastructure.\nFocus: In this paper, we highlight the issues that give rise to these multi-faceted challenges for citizens and public\nadministrations of smart cities, identify the artefacts and stakeholders involved at both ends of the spectrum\n(data/service producers and consumers) and propose a conceptual framework to address these challenges. Based\nupon this conceptual framework, we present a Cloud-based architecture for context-aware citizen services for smart\ncities and discuss the components of the architecture through a common smart city scenario. A proof of concept\nimplementation of the proposed architecture is also presented and evaluated. The results show the effectiveness of\nthe cloud-based infrastructure for the development of a contextual service for citizens...
Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide.\nHowever, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse\ngas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also\nto minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs) with deterministic and\nstochastic demands. In order to ensure a quick response toVMrequests and improve the energy efficiency, a two-phase optimization\nstrategy has been proposed, inwhichVMsare deployed in runtime and consolidated into servers periodically. Based on an improved\nmultidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE) and\na live migration algorithm based on the basic set (LMABBS) are, respectively, developed for each phase. Experimental results have\nshown that under different VMs� stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a\ndefined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also,\nthe difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational\nefficiency....
Interactive Voice Response (IVR) is a technology that allows automatic human-computer interactions, via a telephone\nkeypad or voice commands. The systems are widely used in many industries, including telecommunications and banking.\nVirtualization is a potential technology that can enable the easy development of IVR applications and their deployment\non the cloud. IVR virtualization will enable efficient resource usage by allowing IVR applications to share different IVR\nsubstrate components such as the key detector, the voice recorder and the dialog manager. Resource management is\npart and parcel of IVR virtualization and poses a challenge in virtualized environments where both processing and\nnetwork constraints must be considered. Considering several objectives to optimize the resource usage makes it even\nmore challenging. This paper proposes IVR virtualization task scheduling and computational resource sharing (among\ndifferent IVR applications) strategies based on genetic algorithms, in which different objectives are optimized.\nThe algorithms used by both strategies are simulated and the performance measured and analyzed....
Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to\nhandle the applications. Smart devices are becoming one of themain information processing devices. Their computing features are\nreaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate\nactively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this\nreason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network.\nIn order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and\nleave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using\nCastalia show that our proposal presents a good efficiency and network performance even by using high number of nodes....
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