## 4 Data Mining
### Distributed Data Mining in Sensor Networks  
Data mining in sensor networks faces several challenges. First, sensors are seriously constrained by the resource, including battery lifetime, communication bandwidth, CPU capability and storage [16]. Second, sensor node mobility increases the complexity of sensor data collection and analysis [17, 18]. Third, sensor data come in time-ordered streams over network, which makes traditional centralized mining techniques inapplicable. As in MoDisNet system, not only the types of pollutants measured by sensors, but also since sensors may be mobile it is essential to record the locations of the sensors at each measurement time. As the result, mining these data requires a multitude of analysis components such as statistical, clustering, visualization and classification algorithms and tools. Besides, the analysis of spatiotemporal variation of multiple pollutants with respect to one another can be directly performed over the collected pollution data, however the correlation with third-party data, such as weather, health or traffic is more important and needs novel dynamic data access and integration techniques. For sensor networks, the sensors collect data in time sequence and then there is continuous stream of incoming data for each sensor. Because of the limited storage capability, storing the historical data in each sensor is difficult, even for storing the summary/pattern from the historical data. At the same time, sensor network data management may need the on-line analysis results to be presented for the real-time monitoring and supervising. As the result, the real-time DDM schemes are significantly demanded in such scenario. DDM offers an alternate approach to address above problems of mining data using distributed resources. DDM pays careful attention to distributed data, computing, communication, and human resources to use them in a near-optimal fashion [19]. In recent years, there are a lot of researches on DDM in sensor networks. The existing work can be arranged into three aspects:  
1. intelligent data collection schemes to reduce data volume;  
2. optimal nodes organization strategies to eliminate the heterogeneous of sensor data; and  
3. highly efficient mining algorithms to reduce the computing complexity of DDM. For the first scenario, an unsupervised approach to the outlier detection problem in sensor networks is presented in [20], where kernel density estimators are used to estimate the distribution of the data generated by the sensors. [21] studied the problem of detecting regions of interesting environmental events, which assumes that faults can occur in the equipments though they would be uncorrelated, while environmental conditions are spatially correlated. In [22], the authors partition the sensor grid into several subnets. If the density of nodes is high enough, one node in each subnet can be chosen to give an estimate of the samples at every other node within its subnet. As the result, the collected data volumes are highly reduced. Among the strategies of the second aspect, arranging the nodes of the sensor networks in logical hierarchical styles is a widely used method. In [23], a multi-dimensional clustering approach was proposed to set up a two-layer framework, in which the sensor nodes grouped into cliques on the bottom layer and the data abstraction represents the top layer. Under this framework, a distributed pattern recognition scheme is executed. [24] also proposed a two-layer modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The high efficient mining algorithms can present the distributed mining result directly and many efforts have been made in this area.