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Mahmood Z.,Distributed Intelligent Systems
Proceedings - 2011 International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2011 | Year: 2011

Cloud Computing is a generic term for delivering hosted services over the Internet. It follows a pay-as-you-go approach. Cloud Computing offers numerous benefits for the enterprises, however, there are also many issues, as with any new paradigm or technology. One of the main issues relate to the security and confidentiality of customer data in terms of its location, relocation, availability and security. This paper outlines the Cloud benefits, briefly explains the delivery and deployment models and discusses in detail the issues relating to data in the Cloud. The aim is to provide some useful background information for organizations preparing to migrate to the Cloud to take advantage of this latest computing paradigm. © 2011 IEEE. Source

Zhao Y.,Distributed Intelligent Systems | Patwari N.,University of Utah
IEEE Transactions on Mobile Computing | Year: 2015

Device-free localization systems, such as variance-based radio tomographic imaging (VRTI), use received signal strength (RSS) variations caused by human motion in a static wireless network to locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind or rotating or vibrating machinery, also causes RSS variations which degrade the performance of a localization system. In this paper, we propose a new estimator, least squares variance-based radio tomography (LSVRT), which reduces the impact of the variations caused by intrinsic motion. We compare the novel method to subspace variance-based radio tomography (SubVRT) and VRTI. SubVRT also reduces intrinsic noise compared to VRTI, but LSVRT achieves better localization accuracy and does not require manually tuning additional parameters compared to VRTI. We also propose and test an online calibration method so that LSVRT and SubVRT do not require 'empty-area' calibration and thus can be used in emergency situations. Experimental results from five data sets collected during three experimental deployments show that both estimators, using online calibration, can reduce localization root mean squared error by more than 40 percent compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97th percentile error of 1.3 m, a 60 percent reduction compared to VRTI. © 2002-2012 IEEE. Source

Hill R.,Distributed Intelligent Systems
Proceedings - 2nd International Conference on Intelligent Networking and Collaborative Systems, INCOS 2010 | Year: 2010

The capture and analysis of requirements for virtual campus systems that employ emerging technologies is complex. Whilst the agent paradigm can assist the modelling of such systems, the variety, scope and volume of network interactions creates a scenario that is difficult to comprehend and analyse. Transaction Agent Modelling (TrAM) is an approach to early requirements capture that is particularly suited for convoluted, complex domains. It incorporates the use of Conceptual Graphs and an Economic Accouting model to capture and represent the qualitative dimensions inherent in real-world situations, and also the use of Peirce Logic to test the models derived. Through an exemplar in the mobile learning (m-learning) domain, TrAM is demonstrated and explicated to illustrate its use. © 2010 IEEE. Source

Smith A.,Distributed Intelligent Systems | Hill R.,Distributed Intelligent Systems
Proceedings - 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2011 | Year: 2011

In wireless ad-hoc networks where there is no continuous end-to-end path we move into the area of opportunistic networks. Forwarding messages via any encountered nodes, such as the mobile devices that many users already carry. Normally we are looking for the most efficient method of passing these messages across the network, but how do we evaluate the different methods. We propose to develop a framework that will allow us to evaluate how efficiently provisioning has been performed. This has been explored with the use of a case study and two benchmark protocols, Epidemic and PRoPHET. We present the results of this analysis and describe an approach to the validation of this through simulation. © 2011 IEEE. Source

Mauledoux M.,Distributed Intelligent Systems | Shkodyrev V.,Distributed Intelligent Systems
2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010 | Year: 2010

The work is devoted to solve allocation task problem in the distributed way in multi agents systems with multi-objective genetic algorithms. The paper shows the main advantages of genetic algorithms and the way to apply a new genetic operator using the solution information of the other agents for save time in the search a expand the solution of the optimal space. ©2010 IEEE. Source

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