Volume 5, Issue 2, 2015

By | August 11, 2018

Mobile Sink Assisted Energy Efficient Routing Algorithm for Wireless Sensor Networks

Deepa V.Jose, Department of Computer Science Christ University Bangalore, India.
G. Sadashivappa, Department of Telecommunication EnginR.V.College of Engineering, Bangalore, India.

Abstract— A plethora of applications in our daily life uses the assistance of wireless sensor networks. The main drawback of the sensors is the resource constrained nature especially with regard to energy storage. The minute structure is an attractive feature of these sensors as they can be easily be deployed or accommodated in sensing or monitoring devices. But because of the miniature size, the energy storage, processing and memory capabilities have to be drastically reduced. The applications which uses the wireless sensor networks will have to have a continuous connectivity with the area where they are deployed without any breakage otherwise the intension of the sensor deployment will be ineffectual. In order to maintain the longevity of the network the only option is to efficiently use the available energy to the maximum extent. Each layer of the protocol stack has its own strategies in order to reduce the energy consumption. We are focusing on the network layer in which routing place an important role. As compared to the sensing and processing functions the process of communication consumes more energy and hence the importance of an energy efficient routing protocol to enhance the life time of the wireless sensor networks. A novel approach where in mobile sinks are involved is proposed with extensive simulations which showcase the efficacy of the proposed Mobile Sink Assisted (MSA) algorithm.

Keywords- Energy Efficiency; Duty Cycling; Mobile Sinks; Routing Protocols; Wireless Sensor Networks.

Secure Mobile Cloud Computing Based-On Fingerprint

Alaa Hussein Al-Hamami, Jalal Yousef AL-Juneidi
Department of Computer Sciences and Informatics Amman Arab University, Amman, Jordan.

Abstract—Cloud computing is a new paradigm shift of computing offers managed, scalable and secured and high available computation resources and software as a service that enables the users to access to cloud services from anywhere and anytime. Mobile Cloud Computing (MCC) refers to the availability of Cloud Computing (CC) services in a mobile environment and it is the combination of the heterogeneous fields like mobile phone device, cloud computing & wireless networks. Nowadays the term of MCC is become the buzzword and a major discussion thread in the IT world. In this paper we have designed a new effective model to solve the identification problem in MCC. The proposed solution which we have provided is based mainly on the fingerprints to prove the users identity to determine if this user is authorized or not. We combine each fingerprint with a password to form a multiple passwords scheme. The password consists from the finger sequence in the hand (left or right) plus a fixed password; this will make the passwords to be easy to remember. The results showed that this scheme is very strong and difficult to break it.

Keywords- Cloud Computing; Mobile Cloud Computing; Smart Phone Device; Fingerprint Recognition.

An Efficient Method to Recognize Human Faces From Video Sequences with Occlusion

Vijayalakshmi A., Dept. of Computer Science Christ University Bangalore, India.
Pethuru Raj, Cloud Architect IBM Global Cloud Center of Excellence (CoE), IBM India Bangalore.

Abstract – There are several research endeavors and results in aptly recognizing faces from video clips. However recognizing partially occluded and pose variant face images poses a greater challenge and concern for professionals in image and video processing fields. There are efforts underway for unearthing pragmatic algorithms and viable mechanisms for better recognition rate of occluded and pose-variant images. In this paper, a new method is proposed for efficiently recognizing human faces from videos with varying poses and partial occlusion. In the proposed model, a training dataset is created with faces of varying inclination and occluded region that are subjected to in-painting. A variety of features are being extracted using the discrete curvelet transform method from the captured faces in the training set and they are matched against the features from the videos for recognition. We have supplied the implementation results in this paper in order to demonstrate the efficiency of our approach and the algorithm leveraged.

Keywords- video based face recognition; discrete curvelet transform; In-painting and face recognition.

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