## 3 System Optimization Model  
### One-Dimension Normal Cloud Model  
Most of the existing algorithms cannot meet all the requirements such as connectivity, coverage, fewer active nodes and lower computation cost. In this paper, a coverage-preserving control scheduling scheme based on a cloud model and partition of the target area in emerging sensor networks is proposed. By employing the normal cloud model, the degree of similarity between the sensor nodes can be measured in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. For the nodes in the adjacent area, measuring the similarity between them is a very difficult task due to their cognitive behavior and instability. In the similarity analysis of the information monitored by the sensor nodes, most of the algorithms concentrate on the comparison of the data according to some statistical model. However, they do not systematically consider two uncertainties: randomness and fuzziness. [PERSON] et al. [45] introduced the concept of membership cloud and membership cloud generators, which provided a new way of combining fuzziness and randomness. It mainly reflects the fuzziness and randomness of the concept of human knowledge, and provides a new method for the study of artificial intelligence. Also, a cloud model can represent the relationship between randomness and fuzziness, and involve the transformation from qualitative notion to its quantitative instances. Due to the characteristics of randomness and fuzziness, it has been widely used and has achieved good results in various aspects of data mining, knowledge discovery, signal handling and decision analysis. The normal cloud model can demonstrate the uncertainty between the qualitative model and quantitative model by using linguistic values, which primarily describe the two kinds of uncertainty in the objective world: fuzziness and randomness. Since many phenomena in the real world are subject to or similar to a normal distribution, the normal cloud model is a significant and universal model which possesses good mathematical properties and general applicability. In the similarity analysis of the information monitored by the sensor nodes, most of the algorithms concentrate on the comparison of the data according to statistical model. Then, the prediction of the uncertainty of the model is seldom taken into account. Actually, in the data collection process, a general regularity exists in that real environmental data tends to vary near the trend and close to the normal distribution. This coincides with the classical statistical theory. In this section, we will present the similarity evaluation method based on a normal cloud model to analyze the historical data collected by the monitoring nodes for sensor classification. **Definition 1:**_Assume \(U\) is the universe of discourse, and \(T\) is a qualitative concept in \(U\). If \(x\) (\(x\in X\)) is a random instantiation of concept \(T\), which meets \(\mu_{T}(x)\in\) [0.1] and has a stable trend. Thus, the distribution of \(x\) in the universe \(T\) can be defined as a one-dimensional normal cloud._  
The cloud is composed of a plurality of cloud drops, which can be produced repeatedly and are integrated to reflect the overall characteristics of qualitative concept. As is can be seen from Figure 1, the digital features of cloud can be repressed as: expectation parameter \(Ex\), entropy parameter \(En\), and hyper entropy parameter \(He\). The explanation of the above parameters in normal cloud is as follows:  
* \(Ex\) is the mathematical expectation of the cloud, which is the most typical sample to represent a qualitative concept. * \(En\) represents the uncertainty metric of a qualitative notion, which demonstrates the level of random dispersion of cloud drops in views of the qualitative concept. It is relevant for both the randomness and the fuzziness of the concept. Mathematically, \(En\) contributes to represent the average scope of the universe.