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Wireless Sensor Network is an major emerging technique in wireless communication technology for application across a wide array of domains such as the military surveillance, medical diagnosis, weather forecasting, fire detection alarming systems, etc. One of the main challenges of wireless sensor network (WSN) is how to improve its time of livelihood due to the restricted energy of sensor nodes. Data must be aggregated in order to avoid amounts of traffic in the network, limit the recourses and energy. To solve the above dilemmas , data mining process such as clustering and data aggregation is used .clustering is used to group the nodes where as data aggregation function like MIN,MAX,AVG is used for swabbing redundant data transmission and improves the life span of energy in wireless sensor network. In this paper a new approach related to Voronoi based Genetic clustering (VBGC) Algorithm is proposed for energy efficient data aggregation. Our algorithm achieves energy efficiency by reducing the number of data transmission in each round to cluster head and from it to Base station (BS) .The Base Station periodically executes the proposed algorithm to select new Cluster-Heads after a certain period of time. Simulation results reveal that our algorithm outperforms basic GA.

Clustering for data aggregation is essential nowadays for increasing the wireless sensor network (WSN) lifetime, by collecting the monitored information within a cluster at a cluster head. The clustering algorithm reduces overall transmission of data from each sensor to the sink node thus energy spent by individual sensor node is minimized .The cluster heads collect all sensed information from their respective cluster members and performs data aggregation to transmit the data to the sink node. In this paper novel Voronoi Fuzzy multi hop clustering (V-FCM) algorithm is proposed for grouping the sensor node. This algorithm is a mixture of Voronoi diagram and modified Fuzzy C-Means clustering algorithm. In addition to clustering, data aggregation technique such as MAX, MIN and AVG is computed in each cluster head for further reduction of the number of data transmissions. Finally, the simulations are performed and the results are analyzed within the simulation set up to determine the performance of the proposed algorithm in Weather forecasting sensor network. Our proposed approach has achieved higher energy efficiency when compared with the Fuzzy C-Means algorithm.

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This paper describes a framework that works by collecting the trajectory data obtained from the sensors. The data is stored and processed in a way that helps in identifying events such as key activity areas, evolving activity, etc. It helps to attain better insight into the work habits of the population. Trajectory mining is either assumed that the timeordered location data recorded as trajectories are either deterministic or that the uncertainty, e.g., due to equipment or technological limitations, is removed by incorporating some pre-processing routines. Thus, the trajectories are processed as deterministic paths of mobile object location data. Probabilistic trajectory extraction and mining from uncertain trajectory data is the first phase analysis on the subject. It is also interested in identifying and developing alternative approaches with the use of which can make the approach more scalable, e.g. a trajectory compression scheme could be developed to further decrease the length of the trajectories. This paper proposes an efficient distributed mining algorithm to jointly identify a group of sensor data and discover their trajectory of sensor data in wireless sensor networks. Then, Map-Reduce algorithm (Probabilistic Suffix Tree) is introduced which utilizes the discovered group trajectory sensor data shared by the transmitting node.