# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Node-Level-Focused Energy Conservation Schemes
#### 4.1.2 Data-Driven Techniques  
The classification of data-driven techniques involves two main categories: data reduction and energy-efficient data acquisition schemes, each addressing specific challenges in WSNs [36; 87; 88]. Data reduction schemes primarily focus on minimizing the number of data transmissions and the volume of data transmitted as information travels from the sensor nodes to the base station. Conversely, energy-efficient data acquisition schemes concentrate on decreasing energy consumption within the sensing subsystem, often achieved by reducing the number of samples generated by the sensors. Data reduction schemes in data-driven approaches are primarily executed through in-network processing within the WSN. In-network processing involves distributed computing, where data are processed as it traverses through the WSN towards the sink. This process includes tasks such as the fusion and aggregation of data as it progresses from one sensor node to another, effectively reducing the amount of redundant data that needs to be transmitted. Various techniques exist for in-network processing, with popular ones including data aggregation, data compression, and data prediction [34; 36; 37; 65; 88; 89]. Data aggregation techniques play a crucial role in extending the network lifetime by efficiently merging data as it traverses through the network from one node to another until it reaches the sink [90; 91]. Similarly, data compression involves encoding information at the sensor nodes and decoding it at the sink, effectively reducing the volume of data transmitted by source nodes [36]. This reduction in data transmission not only decreases the radio module's active time but also reduces the energy consumption of sensor nodes. Additionally, data prediction techniques aim to minimize energy consumption by minimizing communication costs. These techniques create a model describing the evolution of sensed data, enabling the prediction of sensor node values within specified error bounds. The prediction model is deployed both at the sensor nodes and at the sink [57]. Transmissions between nodes and the sink occur only when the sensor node measurements deviate from the prediction model's threshold [37], thereby reducing the transmission frequency and communication energy consumption. The category of energy-efficient data acquisition data-driven schemes encompasses adaptive sensing techniques, including hierarchical sensing, adaptive sampling, and model-based active sensing [57; 61]. These techniques aim to reduce the number of samples generated by sensors, thereby minimizing the amount of data to be processed and potentially transmitted by sensor nodes, leading to energy savings. In hierarchical sensing, a sensor node is equipped with multiple sensing devices that monitor the same physical parameter, each offering varying levels of sensing accuracy and power consumption. This setup allows for a trade-off between accuracy and energy efficiency, as lower-power sensors can provide a rough estimate of the monitored parameter [78]. Once an event is detected, higher-accuracy, power-hungry sensors can be activated to provide more precise readings,albeit at the expense of increased energy consumption. Adaptive sampling strategies are designed to minimize the number of measurements and communications required to achieve an accurate estimate by leveraging spatio-temporal correlations within the sensed data. These strategies involve dynamically adjusting the sampling rate and activating only a subset of sensors, thereby reducing the energy consumption associated with sensing [92]. Spatial correlation exploits the fact that measurements from sensor nodes in close proximity exhibit minimal differences. By activating only a few sensors in spatially correlated regions, energy expenditure on sensing can be reduced significantly. Temporal correlations, on the other hand, rely on the observation that if the monitored phenomenon changes slowly over time, the sampling rate can be decreased without sacrificing relevant information [36].