## 4 Discussion
In these studies, the functions of the missing nodes are recovered or compensated in two ways: replication and complement. The proposed approach of this paper belongs to the complement of malicious states of the nodes. In our approach, the faulted sensors are replaced with virtual ones by information processing. The proposed approach creates the virtual sensors as if they had been in the sensor network. In the case studies, the proposed model detects the faulty sensors and create virtual ones. Finally, space weather forecasting based on the dynamic relational network was successful, even the real sensors fail in operation. Therefore, the spatiotemporal interpolation for missing sensors is the significant approach for achieving the resilient sensor networks. In the proposed model, resilience for faulted sensors depend on various kinds of properties such as network structures, number of faulty sensors, locations of the sensors, and relations among sensors. The precision of spatiotemporal interpolation depend on relations among the sensors. In this study, for instance, the locations of STEREO-A and STEREO-B are changing, so that they are moving on their orbit. Their relations for ACE are also changing dynamically. This paper selected the data set in 2008 for training the profiles. However, the period of the test data set covers from 2008 to 2009. In other words, the relations need to involve the changes of the sensor locations by updating the profiles among them. An other example is the number of the nodes in the sensor networks. Some sensor nodes require a few nodes for spatiotemporal interpolation if they had been faulty. On the other hand, some faulted nodes need many neighbor sensors for creating virtual sensors. These characteristics would be found by analysing relations among sensors. In other case, resilience of the sensor networks would vanish if the number of the faulty nodes had exceeded the particular threshold. Identifying relations among sensors is not only crucial topic but also it would be helpful information for constructing the resilient sensor networks. This paper states there are remained issues to build information systems based on the proposed model. Profiling relations among sensors is also a crucial topic to obtain resilience of the sensor networks. This paper uses the SVM to describe relations among the sensors as profiles. The SVM-based profiles were used for diagnosis of relations and interpolation of the missing data. The interpolation of the sensor values between two sensors involves an effect of time series data since the input data for the SVM is a sequence the past 24 h. Modeling and profiling the relations among the sensors depend on the data processing. The work of this paper is the first step for constructing the resilient sensor networks that handle the missing sensors. Other techniques for creating input data and interpolation methods are available and applicable for the relationships among the sensors, such as Hidden Markov Models (HMMs) and Vector Autoregressive Models (VAR models) [4; 6]. This paper considered a computation model to attain the resilient sensor networks with information processing. Protocols for communications among sensors are also necessary to build applications of the proposed model. The sensors need to exchange their observed data and information among them by following the protocols. Concrete examples for the protocols are ZigBee [32] and Simple Network Management Protocol (SNMP) [33]. In computer networks, further, the Internet of Things (IoT) is spreading to collect information from the real world. The resilience of the sensor networks is the crucial topic because the actuators (e.g., satellites) in the networks depend on each other. Therefore, the protocol involving the idea of the proposed model is also required to construct the resilient sensor networks.