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Read e-book online Inter-cooperative Collective Intelligence: Techniques and PDF

By Artur Arsénio, Hugo Serra, Rui Francisco (auth.), Fatos Xhafa, Nik Bessis (eds.)

ISBN-10: 3642350151

ISBN-13: 9783642350153

ISBN-10: 364235016X

ISBN-13: 9783642350160

This publication covers the most recent advances within the speedy turning out to be box of inter-cooperative collective intelligence aiming the mixing and cooperation of assorted computational assets, networks and clever processing paradigms to jointly construct intelligence and complicated determination help and interfaces for end-users. The ebook brings a entire view of the cutting-edge within the box of integration of sensor networks, IoT and Cloud computing, significant and clever querying and processing of knowledge. hence, the e-book provides classes discovered up to now and identifies new study matters, demanding situations and possibilities for extra learn and improvement agendas. rising components of functions also are pointed out and value of inter-cooperative collective intelligence is envisaged.

Researchers, software program builders, practitioners and scholars attracted to the sphere of inter-cooperative collective intelligence will locate the excellent assurance of this publication necessary for his or her examine, educational, improvement and perform activity.

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Takeda, M. Yamashita, High performance grid and cluster computing for some optimization problems, in Applications and the Internet Workshops (2004) 66. C. Rolim, F. Koch, C. Westphall, J. Werner, A. Fracalossi, G. Salvador, A cloud computing solution for patient’s data collection in health care institutions, in Second International Conference on eHealth, Telemedicine, and Social Medicine (2010) 67. V. M. S. Ponmagal, P. Anbalagan, Integration of wireless sensor network with cloud, in International Conference on Recent Trends in Information, Telecommunication and Computing (2010) 68.

Because of the time and space dynamic nature of sensor densities, system performance depends on the mobility patterns of the sensing devices. Uniform coverage for a given area is hard to achieve, as sensors tend to visit zones in a given area in a non-uniform fashion. And as interesting events might be rare, sparse data models need to be considered. For such cases data-mining techniques can be applied. e. sensors that are tasked to visit uncovered areas [15]. Some authors have provided a systematic architecture that can be used as a viewpoint to face these issues, consisting of five layers: pervasive sensing, data anonymization, hybrid learning, semantic inference, and application [11].

Greenwood, G. Blair, G. Coulson, F. Pappenberger, P. Smith, K. Beven, An intelligent and adaptable grid-based flood monitoring and warning system, in Proceedings of the UK eScience All Hands Meeting (2006) 61. H. B. Lim, Y. M. Teo, P. Mukherjee, V. T. Lam, W. F. Wong, S. See, Sensor grid: integration of wireless sensor networks and the grid. Local Comput. Netw. (2005) 62. L. Mingming, L. Baiping, L. Wei, Information security wireless sensor grid, in 5th International Conference on Information Assurance and, Security (2009) 63.

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Inter-cooperative Collective Intelligence: Techniques and Applications by Artur Arsénio, Hugo Serra, Rui Francisco (auth.), Fatos Xhafa, Nik Bessis (eds.)


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