# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Hybrid-Driven Model
Existing studies propose several techniques for saving energy in WSNs such as [100] hierarchical clustering, duty cycling and data-driven approach, rendezvous algorithms, [101] mobility-based energy conservation schemes, energy efficient sleep scheduling in [102; 103] data reduction, protocol overhead reduction, and topology control. In Table 2, various healthcare applications are used in the query-driven model in terms of data delivery delay, and scheduling algorithms are used to save energy in WSNs [78; 79; 80; 94; 95]. If an unexpected event occurs in the WSNs environment such as fire, temperature, or a road accident, then emergency applications are used in the event-driven model [81; 82; 83; 84; 85]. Sensor nodes use the shortest minimal hop routing path for data while analyzing the reliability and true or fault probability-based events that occur. The time-driven model emphasizes natural phenomena such as light, wind speed, temperature difference, rain, etc. Sensor nodes continuously collect and transmit data, which increase energy cost. High energy cost has a harmful impact on the sensors lifetime due to redundant data, data congestion, high traffic data, and network overload. For less energy consumption, data aggregation, clustering, data redundancy reduction, and routing protocols are used in WSNs [86; 87; 88; 89]. Previous studies used a combination of time-driven and event-driven (hybrid) models. Sensor nodes handle two types of data, which are critical and non-critical data, which are classified on a priority basis. If a time-driven model is used, then non-critical data are sent; if an event occurs, the priority goes to the event-driven model. Most event-driven models are based on priority in the hybrid-driven model. Authentication methods, cluster-based data aggregation, hybrid data dissemination protocol, and a class of service traffic priority-based mechanisms are proposed to resolve the issues of data accuracy, data delivery rate, and latency [90; 91; 92; 93]. However, many issues and challenges need to be resolved by future researchers related to data-driven models according to the WSNs types, application, and architectures. Table 2 presents various existing studies and the analysis that the current work has done, and we identified the problems with respect to all four data-driven models and used applications regarding the performance metrics such as network lifetime, energy wastage, data delivery, and data aggregation in WSNs. As query driven data models are used for healthcare applications to handle the issues of data quality and data reliability, the results improve the data delivery ratio and delay. All these processes are carried out by using sleep/active scheduling algorithms. Event-driven data models in previous work target emergency applications such as wildfire, fire temperature, earthquakes, and road accidents. The true or false event direction and end-to-end delay problems are solved by increasing the latency and data delivery ratio. Query responses on data delay can be made by using mechanisms such as sleep/active scheduling and a virtual ringing wheel. On the other hand, the time-driven data model in earlier work focuses on environmental monitoring applications such as temperature, humidity, light, pet mining, and pressure. The data redundancy, temporal, and spatial correlation issues are solved for decreasing the data size, reducing the transmission cost, and enhancing energy efficiency in WSNs by using mechanism such as data aggregation and data reduction methods. Similarly, the hybrid data model in previous work focuses on security specification, industrial environment, healthcare, and coffee arabica applications, whereas the two data-driven models are combined to detect the data including the fire, face detection, and temperature. If the time-driven model is used in one of the hybrid models, then it removes the data redundancy and increases the energy efficiency in WSNs.