# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Hybrid-Driven Model  
A hybrid-driven model is a type of data collection and transmission from the sensor nodes that combines aspects of three data models: event-driven, query-driven, and time-driven. When an event occurs, the sensor collects and sends data of the event that is occurring only. After that, once the event stops, then nodes collect the data normally or periodically. On the other hand, when the user sends or requests query-driven according to their need, the sensor nodes send the data on the request of the user through a query. In WSNs, hybrid model sensors have the right to choose the data-driven model according to the occurrence of the event by modification of sensor processes. In existing studies, some researchers combined the methods and schemes to build hybrid models. [PERSON] et al. [90] propose a class of service traffic priority-based MAC (CSTP-MAC). In the work, data are classified into two classes; the first class is high-priority data (HPD) with priority tag 0 similar to a normal continuous data-driven model, and the second class is low-priority data (LPD) with priority tag 1 similar to an event-driven data model for a wireless body area network (WBAN). The body sensor monitors the body blood, temperature, heartbeat rate, and oxygen level where data collection on various body parts for monitoring prioritizes the patient's most critical conditions. The hybrid data are used in two different ways, such as critical and non-real-time data. Both data have various data communication processes, traffic for low and high data, and bandwidth usages. The hybrid data dissemination protocol (HDDP) uses two data-driven models proposed by [PERSON] and [PERSON] [91]. The event-driven model detects the data; when an event occurs, the sensor nodes send data to the sink through an optimal path for energy consumption, and latency increases. The query-driven model is used when a user requests a sensor node through a source in the network. Similarly, WSNs are used in a fixed and specific field called coffee production. An efficient technique cluster-based data aggregation (CBDA) for a hybrid model is proposed [92]. A hybrid model is composed of two models such as a time-driven and an event-driven model for less energy consumption in data transmission. The time-driven model sensors collect data from the fields and aggregate them into a constant time and next forward them to the base station. In an event-driven model, if any event occurs, data are aggregated by data aggregation technique and sent to a base station. The data aggregated CWSB technique uses cluster-based, either post event detected or after a specific time expiry. A hybrid monitoring of software design [93] presents security specifications that are divided into two types for observed monitor objects. The first type is event monitor (inline) and the second is the time monitor (inline). Most of the time-static structure and dynamic behavior are used to build a hybrid monitoring model for enhancing the performance and advantages. Time monitoring is used for critical time limitations, while non-critical limits are used for event monitoring in hybrid data models. Monitoring software is used to monitor both the time and event, to execute out on a log result. WSNs are made up of various individual sensor nodes that may connect to several other nodes for collecting data from the surrounding environment. The hostile environment conditions necessitate the development of WSNs that can be dropped with minimum danger from airplanes, where sensor nodes immediately begin to collect data and send it back to the base station for further analysis. In order to communicate, sensor nodes send the data to the base station, by using intermediate hoping in terms of neighbor collaborations. Thus, energy saving is a major issue in WSNs because the lifetime of sensor nodes depends on battery power.