please send me ppt on computational intelligence in wireless sensor networks as soon as possible

computational intelligence based optimization in wireless sensor networks ppt

Abstract
"Wireless sensor networks (WSNs) are networks of autonomous nodes that sense, compute and communicate in order to monitor an environment collectively. Ad hoc deployment, dynamic environment and resource constraints in nodes need to be considered while addressing WSN challenges such as deployment, localization, routing and scheduling. Adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments are desirable to address these challenges. The potential of computational intelligence ( CI) based approaches for addressing WSN challenges is investigated in this study. Contributions of this dissertation are in the following three areas: critical literature analysis, new architectures and approaches, and new solutions to WSN challenges.
Challenges in WSNs are discussed, paradigms of CI are introduced and a comprehensive survey of CI-based WSN applications is conducted with an emphasis on pros, cons and suitability of CI methods for WSN applications. A discussion on multidimensional optimization in WSNs and a survey of the applications of particle swarm optimization (PSO) in WSNs are presented.
An adaptive critic design (ACD) having a new combination of a PSO-based actor and a multilayer perceptron (MLP) critic is introduced for dynamic optimization. Its effectiveness is demonstrated through dynamic sleep scheduling of WSN nodes for wildlife monitoring. Compact generalized neuron (GN) is investigated as a resource-efficient alternative to MLPs for classification, nonlinear function approximation and time series prediction. A recurrent GN (RGN) structure is introduced. The performance of GN and RGN is shown to be comparable to that of MLPs having a larger number of trainable parameters.
Autonomous deployment of sensor nodes from an unmanned aerial vehicle and distributed iterative node localization are investigated. These tasks are formulated as multidimensional optimization problems, and addressed through PSO and bacterial foraging algorithm

Wireless sensors (nodes) in the network sense extract data from the various surrounding environment, sequence the sensed data locally, and then transfer the data to a base station for further processing through wireless communication. WSNs face various problem, communication failures, storage and computational constraints and limited power supply. The main challenge occur in WSN to save energy and prolong the network lifetime. Clustering is a technique that is used for managing energy consumption. However, clustering is NP hard optimization problem that can't be solved effectively by traditional methods. Different Computational Intelligence (CI) techniques are used to alter WSN dynamic nature.