# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Event-Driven Model  
Event-driven model applications are used for emergency and disaster recovery-based applications such as health emergencies, forest fires, earthquakes, monitoring of air quality, animal movement, rain, lava eruption, military applications [96], and volcanic eruption [97; 98; 99]. The main feature of the event-driven model is that the collection of data is not formed on a continuous, regular basis while an event is occurring. An event-driven model requires reliability and assurance of delivery on time due to the emergency in the specific region. In the same environmental conditions in which the event occurs, region nodes collect and transmit redundant or raw data, which causes a high transmission rate. The raw form or redundant data causes serious event issues to occur as the region's sensors face congestion, data redundancy, and network overload, as high traffic generates high energy consumption and high transmission cost. In WSNs, the data fusion with Dempster-Shafer evidence theory and event-driven algorithm (EDDS) are proposed to reduce the data size and energy consumption [81]. The EDDS algorithm is used on a group of nodes, and the data sampling rate is based on the threshold when an event occurred. Furthermore, weighted data fusion is used for practical confidence and structure rules when data are transmitted from cluster heads (CHs) toward the sink. EDDS detects anomaly data and finds the data redundancy to identify the occurrence of event. An event-driven routing between event and fault disambiguation is a very big challenge. [PERSON] et al. [82] present a true event-driven and fault-tolerant routing (TEDFTR) algorithm for event-driven routing. When an event occurs, any node sends events occurrence information report to the base station through multi hopping with the help of the TEDFTR algorithm. TEDFTR is a distributed event detection algorithm that detects true environmental events such as earthquakes, floods, high chemical density, and wildfire. To assure that the event is true, fault measures between neighbor nodes are carried out through a voting system. To relay is selected based on the hop-count and multi-object weight sum method through the next-hop node for an event alert. In WSNs, updating the mobile sink's node's location is a very big challenge for high communication overhead and high energy consumption. An event-driven virtual wheel-based data dissemination (EDVWDD) scheme is proposed for sensor fields to include multi wheels to decrease the mobile sink node updates [83]. Whenever an event occurs, general sensor nodes send event-sensed data to the mobile sink. With the help of EDVWDD, a new virtual wheel structure is constructed where the event occurs. The nodes in the virtual wheel structure access the mobile's sink location easily while the remaining nodes do not send data toward the mobile's sink. When under the virtual wheel structure, nodes receive information about the mobile's sink and then send their data to the sink with the help of a geographical routing algorithm for the shortest path selection and data delivery delay minimization. [PERSON] et al. [84] investigate the problems of hostile environment applications or event-driven wireless sensor networks (EWSNs) such as forest and fire detection events. In these applications, data harvesting is a big challenge to handle from sensor nodes in the field. These applications are used in many physical and arbitrary trajectory obstacles where the mobile sink cannot easily move. The predefined trajectory is based on EWSNs in the environment where there is no possibility to visit each sensor node one by one. Thus, sensor nodes are deployed randomly by aircraft. The study aims to select a set of rendezvous points (RPs) with a predefined trajectory for two main purposes, which are less energy consumption and the control of high data delivery delay in the EWSNs. A direct influence on the lifetime of WSNs is found on energy consumption is directly related to the rate of data transmission. Similarly, [PERSON] et al.