## 3 Data Fusion
### State-of-the-Art Research on Data Fusion
#### 3.1.3 Data Fusion Computing  
Data fusion algorithms are the core of data fusion computing. A lot of research has been done in integration of data of the same type. In data fusion algorithm design, modeling information of different types and fusing them together are problems faced by researchers. Techniques or algorithms of fusion computations can be divided into two categories: the activity classification algorithm and the influence model. The former includes threshold-based classification, hierarchical methods, decision trees, and k-nearest neighbor (K-NN), while the latter includes SVM, BN, ANN, HMM. Besides, Gaussian mixture models, Fuzzy Logic, and Markov chains are also in use. Actually, two or more techniques are usually applied in BSN applications. Activity analysis is an important field of data fusion computation in BSNs. It records activity patterns in a period of time which relies on a classification algorithm to recognize different types of activities by analyzing data coming from wearable sensors [24]. Reference [72] adopts a supervised learning approach to recognize 14 physical activities, using a binary decision-tree with a Naive Bayes classifier at each internal node. It also develops a feature selection algorithm based on mutual information to process data from 14 tri-axial accelerometers, which can recognize human activity with a 96% average accuracy. Threshold-based classification could differentiate static postures and dynamic activity, and identify dynamic activities of different types, such as discriminating between going up and down stairs from walking straight [73]. Nowadays, the methods of threshold-based classification and robustness algorithm are being combined together and applied to the detection of falls. The authors of Reference [45] use three kinds of classifier, which are AdaBoost, HMM, and K-NN, to analyze data from accelerometers when identifying human hand activity. The result shows that AdaBoost can achieve the highest recognition accuracy (86%). The drawback of these classifiers is that they need a large amount of training data to ensure accuracy of recognition. In recent years, researchers have done a lot of research on influence models, and the major research directions include ANN, BN and HMM. ANN is used as a solid clustering algorithm, and it works like the biological nervous system of a brain that consists of a large number of small and simple interconnected components, that is, neurons. Each neuron has a certain amount of computing power and a strong learning ability, which enables it to easily add new context information into the system. The WSN system based on ANN will inevitably produce noise, but it can still work well, which shows the good nature of ANN. Moreover, the unsupervised training on input data can be realized effectively [74]. Self-Organizing Map (SOM) is a kind of ANN method. In addition to the advantages inherited from ANN, SOM provides an efficient way of visualizing and clustering data. However, SOM has the features of instability and dependence on the training data. To solve these problems [PERSON] [28] proposes a novel Spatio-Temporal Self-Organizing Map (STSOM). Compared with traditional SOM, STSOM not only minimizes the number of neurons, but also reduces the iterations of the learning and classification processes, which enable the system to run locally in a low-power way. BN is one of the most effective theoretical models for uncertain knowledge representation and reasoning. A BN is a directed acyclic graph. Nodes represent a random variable, while directed edges between nodes represent the relationship between nodes. BNs use conditional probability to represent the relationship between the various information elements and can effectively conduct expression and integration of multi-source information. In the study of BN, it follows an independence assumption that each node in graph is strictly independent from other nodes. However, in BSN applications, the assumption is challenged by the increase of dependency between parent and child.