A Wireless Sensor Network (WSNs) is a collection of number of sensor nodes which are left open in an unsecured environment. Sensor nodes work and communicate together to attain the desired goals. They are placed at the locations where monitoring is otherwise impossible. Wireless Sensor Networks are resource constrained which may be computational power, memory capacity, battery power etc. As Wireless Sensor Networks are implemented in the unattended environment, they are prone to discrete type of security attacks. Because of their limitations these networks are easily targeted by intruders. Sinkhole attack is one of the security attacks which try to disturb the ongoing communication in wireless sensor network. In sinkhole attack, the intruder or the malicious node try to attract the network traffic towards itself, that sensor nodes will pass data packets through this compromised node thereby manipulating messages which sensor nodes are transferring to the base station. In this paper we analyze the impact of Sinkhole attack on AODV protocol under various conditions. We analyzed the impact of Sinkhole attack on AODV protocol with varying number of attacker nodes.

Six reddish orange Sm3+ complexes were synthesized with the help of organic ligand and secondary ligands via one-step significant liquid-assisted grinding method and characterized spectroscopically.

-Data mining is a process of extracting desired and useful information from the pool of data. Clustering in data mining is the grouping of data points with some common similarity. Clustering is an important aspect of data mining. It simply clusters the data sets into given no. of clusters. Various no. of methods have been used for the data clustering among which K-means is the most widely used clustering algorithm. In this paper we have briefed in the form of a review work done by different researchers using K-means clustering algorithm. We have also analysed different distance metrics used by them for distance evaluation.

Abstract-In today's world competition is increasing day by day. In field of higher education as competition is increasing so the student self-harm rate is increasing. The reason for this is because students are not able to cope with studies and under pressure they do self-harm. Data mining is a technique which can be used to decrease this self-harm rate. In this paper we want to explain that using data mining we can predict result of students beforehand by using previous year result or any other factors in early stages of any course. This technique is called Prediction Analysis and this an example of use of data mining in the field of education. It can also be called as educational data mining. Any Data mining can be used for this prediction analysis and we will be using WEKA tool of data mining to predict result.

No abstract