# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Time-Driven Model  
A time-driven model is a sequence of data points composed of overtime intervals by a track system based on time. In WSNs, time-driven data are produced via a sensor node by a periodic form from environmental conditions. The environmental condition does not affect network sensor nodes, these sensor nodes are continuously sensing and sending data to the sink node. The sink node collects the data from the sensor nodes in a form of a round base on time duration. Now, each sensor node is divided into the period, and each period is divided into fixed time slots that represent specific reading. Time-driven networks sometimes experience the physical observed environment condition that features dynamic change slow down and speed up. As a result, a lot of redundant or raw data products that cause various types of traffic are generated in WSNs such as packet losses, high energy consumption, data representation, data accuracy, data redundancy, network overload, high transmission cost rate, data latency, congestion, data delivery ratio, etc. In recent years, various studies have been proposed to tackle these challenges and issues [86; 87; 88; 89]. In WSNs, data redundancy is a challenging issue that arises due to temporal and spatial correlation data collection from sensor nodes. [PERSON] et al. [86] propose two new lightweight fold aggregated techniques such as exact matching-based weight-enabled local data aggregation EMWA, and marginal weight-enabled local data aggregation MWA at the node level. For exact matching and to remove marginally aggregated data, EMWA is used to reduce redundancy by similar and average functions. The main aim of EMWA is to reduce the average data transfer rate. To eliminate data redundancy, data with similar values are removed and weighted values are used. To achieve high data accuracy, integrity, and reliability, EMWA uses exact matching data values instead of approximate matching values. MWA aims to calculate the distance between two consecutive readings with a predefined threshold value for identifying marginal differentiation. These extremely small values with updated weighted values are stored in memory and consume energy. In contrast, outlier detection with an efficient data aggregated scheme is proposed for a fundamental process of saving energy and prolonging network lifetime in WSNs [87]. The novel data aggregated scheme is a radial basis function for data aggregate RBFDA based on cosine similarity and neural network that observes dense areas in a cluster neighborhood together with sensor nodes due to high similar data achievements. The neighborhood member nodes calculate data by the cosine similarity function for the detection of similarities. The Mahalanobis function uses the data outline detected from multivariant data at the node level. The CH node receives a data set from the member nodes and then checks data determination and classification, deletes outlier and reduces data redundancy based on multivariant values for improving data accuracy, and decreases network overload, all of which save energy save prolong the network lifetime. [PERSON] et al. [88] propose an All-in-One hybrid data gathering and energy-saving mechanism for WSNs. The main idea of their mechanism is a self-reconfigurable sensor based on various parameters to select the mechanism that is suitable for data reduction techniques such as data redundancy amount and remaining power battery. The All-in-One mechanism is divided into three phases. In the first phase, the aim is an on-period data reduction technique to decrease the data transmission amount from sensor nodes by any aggregation, prediction, or compression. In the second phase, based on in-period data adaptation, the rate variation considers the monitoring condition for data transmission by using on-off transmission and adaptive frequency sensing. Third, in-node means the data correlation among the neighboring nodes by using data clustering techniques for in-network correlation.