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Among the modern wireless networks, wireless sensor network plays a prominent role. Bunch reduces the general energy consumption. In this paper HEF (High Energy First) bunch protocol is employed. This protocol provides improved network time period since the energy level of the nodes are considered while choosing the cluster head. The cluster area is fashioned dynamically and sporadically. The cluster heads are usually having more resources (generally energy) on comparison with other nodes in the cluster. We tend to propose a globally trust management theme that enhance security in WSNs. In this trust management scheme, trust model takes 2 methodologies, trust from direct observation methodology and trust from indirect observation method. In direct observation methodology observer node gets trust value by exploiting theorem reasoning. On the other hand, with indirect observation trust value is obtained from neighbor nodes of the observer node. The trust value for this methodology is based on Dempster-Shafer theory. Combining these 2 trust models, we tend to get a lot of correct trust values that results the effectiveness of our methodology.

Hand geometry is one among the first biometrics to find practical use across an assortment of real-world security applications. A hand geometry based recognition system works by acquiring the image of a hand to determine the geometry and metrics namely the finger length, width and other attributes. Some of the existing hand geometry biometrics systems measure different parameters for efficient recognition. An important aspect of geometry based approach is the assumption that an individual's hand does not drastically change after a certain age. Most of the existing systems use more number of attributes to describe a hand of which some like finger width may slightly vary over time. Including such attributes in the process of distance metric will notably reduce the accuracy of the system during practical implementation. So, we consider only some of the selected attributes which will not change significantly over short periods of time.Several segmentation algorithms were used in the process of extracting different kinds of features from the hand image. In this paper we present a model for hand geometry based human recognition. The paper proposes and uses some distinct features that enhance the accuracy of the recognition. In our previous work we successfully implemented a simple and very fast algorithm for hand image segmentation employing filtering, edge detection and region labeling techniques and arrived at comparable segmentation results. This technique has been employed to segment the hand images. In addition to the above, we propose the usage of some distinct features, which would enhance hand recognition much more precisely.

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