# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Power Reduction Techniques
#### 3.1.3 Data Mitigation  
_Data mitigation_ presents another power consumption solution for agriculture WSNs to reduce the amount of data transmitted from source nodes to destination nodes. Data mitigation can be achieved via (i) data gathering, (ii) data comparison, (iii) data rate, and (iv) data-driven techniques. _Data gathering_ can extend the lifetime of sensor nodes in WSNs by minimizing redundant data transmission. In different applications of agriculture WSNs including environment observation, the sensed data of the neighboring nodes may spatially be associated. In this case, data combination presents an important method for reducing the amount of redundant data, reducing the number of transmissions, and minimizing the power consumption of the nodes in the network [90]. Data gathering also reduces latency by minimizing data traffic and improving the delay between the sink and sensor nodes; after reducing the number of communications, this approach can improve the exploitation of bandwidth [91]. Data merging can be accomplished along a route starting from sensor nodes to sink nodes. The nodes in a route can rebroadcast only the maximum, minimum, or average amount of received data. The design of an energy-efficient WSN-based data-gathering scheme poses a significant challenge because this scheme must ensure a trade-off among power consumption, latency, security, data accuracy, and fault tolerance. Gathering data in agricultural applications, including humidity, weather, temperature, and soil moisture, can minimize the power consumption of environment sensor nodes by spatially correlating the field and redundant data of the sensor nodes when the temperature does not change significantly [1], especially during nighttime. For example, if the microcontroller of the temperature sensor node measures the same temperature value for extended hours, then the sensor node sends the temperature value every four hours to reduce power consumption. Merging the temperature and humidity sensors into a single sensor may also reduce the power consumption, size, and system complexity. [PERSON] et al. [17] integrated climate condition parameters (i.e., temperature, CO\({}_{2}\), humidity, and illuminance sensors) inside a greenhouse and observed a comfortable growth of plants while saving water resources and energy. The greenhouse was equipped with environment control devices, such as ventilation system, fogging system for humidification, and electrical heaters. A smart system based on fuzzy logic control correlated humidity and temperature measurements is implemented to improve the fogging system operation. In that way, the power consumption of the fogging system can be significantly reduced. In addition, the system was improved by a wireless data observing platform (STM 32/F4 microcontroller and Virtex V FPGA board) for logging and data routing, which provides data access in real time. The suggested control system based on fuzzy logic control was experimentally implemented and validated. The results revealed that the total energy and water usage decreased by 22% and 33%, respectively. [PERSON] et al. [92] introduced a data caching algorithm (DCAL) that optimizes the sleep/wake up periods of ZigBee WSNs to achieve low energy consumption and latency in PA application. In WSNs, data are collected from the source nodes and routed to the sink node; from the sink node, the data are sent to the Internet. Reducing the transmission of duplicated sensed data reduces the power consumption. Energy conservation is paramount during the communication phase to prolong the lifetime of sensor nodes. The main method utilized to conserve energy involves switching off the nodes' transceiver when they are not transmitting nor receiving packets. Additionally, the data-gathering method for agriculture WSNs has been investigated in [93]. The proposed system includes ZigBee wireless sensor nodes, customized farming personal digital assistants (PDAs), and a host PC.