# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 4 Algorithm-Based Energy Sustainability in WSNs
### Algorithm-Based Methods for Energy Sustainability in WSNs
#### 4.3.1 Data Driven Methods  
Data driven approaches aim to limit the amount of sampled data, while preserving the accuracy within acceptable limits, depending on the requirements of each application [52]. These approaches are distinguished into two types, data reduction and data acquisition approaches, and they are focused on the optimal management of the sensed data. * Data reduction aims to minimize the amount of the data that need to be sensed and transmitted to the sink and consequently limit the number of transmissions required. This can happen by reducing the sampling frequency of the sensor nodes in order to avoid the creation of redundant samples, or by reducing the mandatory sensing tasks [21]. The methods used for data reduction are:
* Data compression: by compressing the sensed data, the size of the aggregated data is reduced prior to their transmission to the BS. So, both the size of the transmitted packets and the transmission time are reduced and consequently energy is saved [21];
* In-Network processing: is typically performed by intermediate nodes located among the data sources and the BS. Specifically, nodes along with executing their sensing tasks, they can also use their microprocessors in order to process the information data that they have gathered and then transmit only the really essential data packets to the BS. So, the in-network processing of the sensed data reduces the number of the data transmissions performed and consequently saves great amounts of energy [53];
* Data prediction: prediction models are created in order to give answers to the queries generated by the BS. These answers can be either prediction values that are associated with statistical or empirical probabilities, or future metrics that are estimated-based on the prediction model. Perpetual monitoring of a FoI, implies frequent alterations of the measured values. In data prediction approaches, the sensor nodes gather sample data within predefined periods of time and compare the actual data with the prediction values. Then, they transmit their data in case a deviation is noticed, thus decreasing the num  
Figure 7: Categorization of algorithm-based energy-saving mechanisms in WSNs. ber of the unnecessary transmissions and subsequently the corresponding expenditure [23; 54]. * Data acquisition approaches intend to restrict the energy that is consumed during nodes' sensing tasks by using appropriate acquisition methods. Sensing is a power consuming process since power hungry sensors or A/D converters are exploited in many applications [55]. The data acquisition methods are: \(\bigcirc\) In Adaptive sampling, in contrast with the traditional sampling methods where the rate is predefined, the number of samples captured by the nodes is adjusted-based on each application's needs. In this way, the energy dissipation is limited, and the battery life cycle of nodes is prolonged [56]; \(\bigcirc\) Hierarchical sampling is used in networks that are made of nodes that contain sensors of various types. Since every sensor is defined by distinctive characteristics, such as accuracy, resolution, and power consumption; this method dynamically decides which category of sensors to trigger. Typically, simple sensors are more energy efficient than advanced sensor nodes, but they lag behind in terms of their characteristics. Oppositely, sensors with a more complex design and way of operation, provide more precise information of the sensed data. Because of that, in hierarchical sampling approaches, low-power sensor nodes are used in order to monitor data regarding the FoI. Once an event is detected or a more thorough evaluation is required, advanced sensors take charge of the sensing process [57]; \(\bigcirc\) Model-based active sampling: in these methods, mathematical models are implemented, in order to limit the sampling rate and preserve the nodes energy levels. Specifically, these models use the sampled data and aim to predict the corresponding subsequent values within a confidence level, reducing the frequency of the sampling.