# Green IoT: A Review and Future Research Directions
## 5 Energy-Efficient and Eco-Sustainable Wireless Sensor Networks
### Aggregation and Reduction of the Data  
Efficiently managing the amount of data transmitted to the sink nodes by reducing and aggregating it has been proposed as a method to enhance energy efficiency during the transmission process [44]. Data aggregation schemes [83] involve a node re-transmitting only the average or the minimum of the received data, which can reduce latency by decreasing traffic. An example of this would be, in a surveillance application, low-power acoustic detectors that can be utilized to detect intrusions, and when an event is detected, power-consuming cameras can be activated to obtain more detailed information [54]. Additionally, spatial correlation can be employed to decrease the sampling rate in areas where there is little variation in the sensed data. For recognizing human activities, [PERSON] et al. [84] suggested that the data collection frequency should be adjusted to the user activity, as it is not necessary to sample at the same rate when the user is sitting or running. However, these techniques can also decrease the accuracy of the collected data, as the original data may not be recoverable by the sink, leading to a loss of information precision, depending on the aggregation function used [85]. Thus, it is not recommended to use these methods in applications requiring high accuracy. An alternative is adaptive sampling, which adjusts the sampling rate at each sensor while ensuring that application needs are met in terms of coverage or information precision. On the other hand, network coding is used to decrease traffic in broadcast scenarios by sending a linear combination of multiple packets instead of a copy of each packet. Network coding makes use of the trade-off between computation and communication since communication is relatively slow and power-intensive compared to computation. [PERSON] et al. [86] combined network coding with connected dominating sets to further reduce energy consumption in broadcast scenarios. AdapCode is a data dissemination protocol, where a node sends one message for every \(N\) messages received, thereby saving up to \((N-1)/N\) fraction of the bandwidth compared to naive flooding [87]. The receiver node can recover the original packets by using Gaussian elimination after successfully receiving N coded packets. In addition, AdapCode adapts the \(N\) value to the node density for improving reliability; as the density decreases, it becomes more difficult to recover enough packets to decode the data. To enhance the reliability further, it allows the nodes that received less than \(N\) packets to send a negative acknowledgement to retrieve missing data. Ultimately, the choice of the most effective energy-efficient technique for green WSNs depends on several factors such as network topology, energy requirements, and application. It is crucial to assess the different techniques based on these factors and choose the most suitable one for the specific application. Nevertheless, Table 6 presents a summary of the energy-saving estimates of various green WSN techniques discussed in the literature.