# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 5 Energy Saving on Network Level
### Data Reduction  
Data reduction contributes significantly to energy saving by reducing the packets size and therefore the sending time. This can be carried out by several methods, such as data aggregation, adaptive sampling, data prediction, network coding and compressive sensing [126]. In the data aggregation method, nodes collecting data, such as CH, conduct data fusion technique to decrease the overall amount of transmitted data, reducing data traffic and thus transmission delays.  
Data compression techniques aim to reduce the data packet size by the node itself or by an aggregator node. By applying a data compression algorithm, such as Kalman filter, lossless compression and adaptive model selection [127; 128; 129; 130], the data packet size can be significantly reduced. However, depending on the adopted algorithm, the data may be not accurately recovered, which results in a loss of precision.  
Data prediction techniques intend to reduce the communication between nodes by preventing the transmission of each raw sample from source nodes to the sink node [131; 132]. Therefore, the data traffic is decreased. For this purpose, each node creates a model to estimate the sensed data based on some observed values. This model is transmitted to the sink node to estimate upcoming data within an error tolerance. A new model is forwarded to the sink node only when a difference between predicted and observed values is detected by the source node. In some applications, where data precision is very important, such as in healthcare or military applications, this technique cannot be applied.  
The adaptive sampling approach minimizes the amount of data packets using spatial or temporal correlation [133]. This technique is mostly used in centralized networks, where the sink node needs a high computational effort to process all the received data from different sources.  
Network coding (NC) techniques aim to reduce the data traffic by allowing intermediate nodes between the source node and the sink to broadcast a binary or linear combination of multiple packets rather than forwarding a copy of each packet [134].  
Compressive sensing (CS) techniques help significantly to save the energy by reducing the transmitted data volume [135]. With CS, the signal is transformed to a new signal with fewer values (sparsity). By reducing the sampling frequency, the energy consumption is decreased. A major feature of the CS is its potential to reconstruct a sparse signal from a limited number of measurements with no prior knowledge of the signal structure [135].  
In this section, we review different energy saving techniques at the network level [13]. Radio optimization techniques reduce the signal quality to conserve the power supply. Energy-efficient routing protocols and sleep/wake-up protocols directly affect the network latency. Data reduction approaches can influence the reliability of the gathered information. Although recharging techniques for sensor nodes are highly relevant, energy-saving techniques are still crucial. The choice of a certain technique remains dependent on the specific scenario including the distances to be bridged, the possibility of fading due to obstacles, the number and distribution of nodes, the amount of data to be transmitted, the necessary measurement intervals and the possibility to harvest enough energy from ambient, which is decisive for the available energy budget. On the one hand, the available energy budget defines the necessary energy saving techniques to be applied. On the other hand, the energy saving can significantly reduce the size of the energy harvester and complexity of the energy management circuits and therefore the system costs.