## 1 Introduction  
### Background  
The technique for integrating tactile, visual, and other sensor information is an essential and important issue in measurement and control, and such sensor fusion techniques can effectively contribute to engineering fields. To seamlessly integrate the sensor data obtained, a method for data-acquisition synchronization is required, which can fuse the sensor data that are coherent over a time series. Consequently, such a technique would simplify sensor data fusion, because the data with the synchronization are highly consistent with time.  
Moreover, in the case where tactile, visual, acceleration, force, angle, and angular velocity sensor data are used for accurately controlling robots, machines, and precision devices, a high sampling rate of data acquisition in the sensor network system is required. Further, from the viewpoints of the recent trends in technology such as the Internet of things, artificial intelligence, and big-data analysis, highly dense data are also strongly required to increase the analysis efficiency and validity of its result. Consequently, the performance of various applications described above can be significantly improved using such a data-acquisition technique with a high sampling rate and the synchronization of data acquisition.  
Therefore, the required specification in the sensor network system to achieve an accurate measurement and control is summarized as follows:  
1. data acquisition at the same timing in every sensor; and
2. highly dense data.  
By performing the first term, a transition of the state (for example, among vision, proximity, and tactile) of a target can be observed, and a switching control according to the state transition is enabled. In addition, in the stereo vision (three-dimensional measurement) for a dynamic object, the synchronization of shutter timings of two cameras is required to achieve a precise measurement. Further, considering an occlusion problem that is a common problem in computer vision, multiple camera network systems are required. In this case, the shutter timings of all cameras are to be synchronized. The necessity of the second term is trivial. Thus, we have developed a high-speed sensor network system of various sensors with a high sampling rate and good data synchronization.  
Meanwhile, wireless sensor networks have been actively discussed recently. For example, [PERSON] et al. reviewed various wireless sensor network systems. In particular, they study surveyed three kinds of categories, and summarized the designs, algorithms, protocols, and services in the wireless sensor network systems [1]. [PERSON] and [PERSON] discussed the healthcare applications of the wireless sensor network [2]. [PERSON] et al. proposed a mathematical model to determine the optimal placement of wireless nodes to maximize the lifetime of data flow [3]. [PERSON] et al. suggested the routing protocol for increasing the network lifetime in the wireless body of networks [4]. It is considered that our proposed method can extend to the wireless sensor network in the future, and this research contributes to these fields and applications described previously. In the wireless sensor network, the software method is also required to achieve the specifications above. Because our method can be performed in the software architecture, the method is considered applicable to the wireless sensor network.