Abstract-Wireless Sensor Networks assume an imperative part to monitor and gather information from complex geological ranges. Energy conservation plays a fundamental role in WSNs since such sensor networks are designed to be located in dangerous and non-accessible areas and has gained popularity since the last decade. The main issue of Wireless Sensor Network is energy consumption. Therefore, management of energy consumption of the sensor node is the main area of our research. Sensor nodes use non-changeable batteries for power supply and the lifetime of Sensor node greatly depends on these batteries. The replacement of these batteries is very difficult in many applications, such as an alternative solution to this problem is to use Energy Harvesting system in Wireless Sensor Network to provide a permanent power supply to sensor nodes. This process of extracting energies from nature and converting it into electrical energy is called energy harvesting. Energy can be harvested from the environment for sensor nodes. There are many sources of energies in nature like solar, wind and thermal which can be harvested and used for WSNs. In this research, we suggest to use energy harvesting system for Cluster Heads in a clustering based Wireless Sensor Networks. We will compare our proposed technique to a well-known clustering algorithm Low Energy Adaptive Cluster Hierarchy (LEACH).

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One of the major challenges encountered by current face recognition techniques lies in the difficulties of handling varying poses, i.e., recognition of faces in arbitrary in-depth rotations. The face image differences caused by rotations are often larger than the inter-person differences used in distinguishing identities. Face recognition across pose, on the other hand, has great potentials in many applications dealing with uncooperative subjects, in which the full power of face recognition being a passive biometric technique can be implemented and utilized. Extensive efforts have been put into the research toward pose-invariant face recognition in recent years and many prominent approaches have been proposed. However, several issues in face recognition across pose still remain open, such as lack of understanding about subspaces of pose variant images, problem intractability in 3D face modelling, complex face surface reflection mechanism, etc. This paper provides a critical survey of researches on image-based face recognition across pose. The existing techniques are comprehensively reviewed and discussed. They are classified into different categories according to their methodologies in handling pose variations. Their strategies, advantages/disadvantages and performances are elaborated. By generalizing different tactics in handling pose variations and evaluating their performances, several promising.