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Wireless Sensor Networks (WSNs) are defined as dynamic, self-deployed, highly constrained structured network. It`s high computational environment with limited and controlled transmission range, processing, as well as limited energy sources. The sever power constraints strongly affect the existence of active nodes and hence the network lifetime. In order to prolong the network life time we have to overcome the scarcity in energy resources and preserve the processing of the sensor nodes as long as possible. Power management approaches efficiently reduce the sensor nodes energy consumption individually in each sensor node and the adaptive efficient routing technique has greatly appeals a great attention in research. The potential paradigms of soft-computing (SC) highly addressed their adaptability and compatibility to overwhelm the complex challenges in WSNs. This paper is introducing and surveying some of the Soft Computing proposed routing models for WSNs that optimally prolongs its life time.

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Biometrics have lately been receiving attention in popular media. Biometrics deal with identification and verification of individuals based on their behavioral or physiological characteristics. Biometrics will become one of the most important ways of the identification technology. Ear recognition might be a good solution since ear is visible, ear images are easy to be taken, and the ear structure does not change radically over time. In this paper an algorithm based on SIFT features for ear recognition is proposed. SIFT key points are extracted from ear image and an augmented vector of extracted SIFT features are created for matching. Firstly, a pre-processing phase is done by converting image to gray level. Then a median filter is applied to smooth the image and to remove noise if found. Edge detection is used for cropping ear part from the image. Then the SIFT features were extracted from ear image. Finally, the extracted features were classified by using minimum distance classifier. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results compared with other researchers and obtained over all accuracy almost 95.2 % for IIT Delphi database and 100% for AMI database.

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Cloud computing refers to the services and applications that are accessible throughout the world from data centers. All services and applications are available online. Virtual machine migration is an important part of virtualization which is considered as essential part in cloud computing environment. Virtual Machine Migration means transferring a running Virtual Machine with all its applications and the operating system state as it is to target destination machine where it continues to run as if nothing happened. It makes balancing between servers. This improves the performance by redistributing the workload among available servers. There are many algorithms of load balancing classified into two types: static load balancing algorithms and dynamic load balancing algorithms. This paper presents the algorithm (Balanced Throttled Load Balancing Algorithm- BTLB). It compares the results of the BTLB with round robin algorithm, AMLB algorithm and throttled load balancing algorithm. The results of these four algorithms would be presented in this paper. The proposed algorithm shows the improvement in response time (75 µs). Cloud analyst simulator is used to evaluate the results. BTLB was developed and tested using Java.