Intelligent Distributed Computing (Advances in Intelligent Systems and Computing, Volume 321)

Intelligent Distributed Computing (Advances in Intelligent Systems and Computing, Volume 321)

Language: English

Pages: 310

ISBN: 3319488287

Format: PDF / Kindle (mobi) / ePub


* Recent research in Intelligent Distributed Computing
* Carefully reviewed post-conference proceedings of the Third International Symposium on Intelligent Informatics (ISI'14) held in Delhi, India during September 24-27, 2014
* The papers are organized in topical sections on Intelligent Distributed Computing , data mining, clustering, multi agent systems, pattern recognition, and signal and image processing

This book contains a selection of refereed and revised papers of the Intelligent Distributed Computing Track originally presented at the third International Symposium on Intelligent Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers selected for this Track cover several Distributed Computing and related topics including Peer-to-Peer Networks, Cloud Computing, Mobile Clouds, Wireless Sensor Networks, and their applications.

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such task. In our technique we consider that an unknown node would be in a triangle (created by three anchors), if distances of an unknown point from all three vertices of the triangle are less than respective altitudes from those vertices. If this condition is false, we will not consider a particular geometry as acceptable geometry. Fig. 4 (a) represents a geometry where given condition is true and Fig. 4 (b) represents a geometry where geometry condition is false. Our given condition selects

complicated and distribution systems issues. In the MapReduce [11] mode as in fig.1, the map and reduce are the data processing functions. The parallel map tasks are run on input data which is partitioned in to fixed size blocks and yield intermediary output as a group of pairs. These combinations are shuffled across different reduce tasks based on pairs. Each reduce tasks accept only one key at a time and process data for that key and output the result as

International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–6. IEEE (2008) 16. Narula, P., Dhurandher, S.K., Misra, S., Woungang, I.: Security in mobile adhoc networks using soft encryption and trust based multipath routing. Sci. Direct Comput. Commun. 31, 760–769 (2008) 17. Ayday, E., Fekri, F.: A protocol for data availability in Mobile Ad-Hoc Networks in the presence of insider attacks. Ad Hoc Networks 8(2), 181–192 (2010) 18. Li, X., Jia, Z., Zhang, P., Zhang, R.,

introduced the use of cellular automata in Wireless sensor networks to control Topology. In the network the sensor nodes are redundantly deployed in the same field. Due to this redundant deployment the many nodes in the network have remained in their active state simultaneously. This causes reduction in the global energy of the network and step-up in the lifetime of the mesh. Thus, the principal aim of the topology control algorithms is to cut the initial topology of wireless sensor network by

cases have been used for training and testing of neural network. 5.2 Neural Training and Testing Model ANN has been trained using four feature vector (as shown in Table 2) from historical database. 75% of data has been used for training 15% for validation and 10% data has been used for testing purpose using feed forward network and, SCG fast supervised learning algorithm. As shown in Fig. 7 where w is a weight vector and b is a bias term. Fig. 7 ANN training model Table 3 is a model

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