# Current Trends on Green Wireless Sensor Networks
## 5 Discussion and Future Directions
Adjusting the energy expenditure of devices (i.e., the actions they can perform at any given time) to both the energy remaining and the energy expected to be collected in the immediate future can be very useful, so incorporating predictive techniques that allow anticipating random changes in certain natural energy sources seems peremptory. In solutions where the receiver initiates the information exchanges (RI-MAC protocols), automatic learning techniques could also help the transmitter to synchronize its activation times with the receiver's listening, avoiding energy loss while waiting for the sounding beacon. In any case, complex techniques involving excessive computation may be counterproductive (they require their own energy expenditure), or even not applicable in devices with very limited resources. From the point of view of communication protocols, recent contributions in the field of wake-up radios allow devices to make considerable energy savings by largely avoiding idle listening and overhearing processes, although their applicability is severely restricted by the short distances over which the technique would be effective. Another promising research field is related to the study of the energy consumption associated with the cryptographic mechanisms with which we want to protect communications, given the capital importance that security has acquired presently. On the other hand, as the mobility of network elements (e.g., intelligent transport systems) is increasingly present in our applications, which implies a greater use of radio, it will be necessary to continue redesigning routing and MAC protocols to make them more energy-efficient (using, for example, techniques such as opportunistic forwarding). Undoubtedly, the most productive area of research for energy efficiency in WSNs is cluster-based routing, with LEACH-based protocols playing a major role. This is because in most WSN applications, data flows from sensor nodes to the BS, and data from nearby nodes contains redundancy, so clustering favors data reduction by exploiting spatial correlation. This fact has also led to a huge research activity in the field of data reduction, and more specifically in data aggregation techniques. In the area of cluster-based routing, the new ideas based on heuristic and fuzzy approaches can allow the clustering methods to achieve more optimal results. The CH and relay nodes selection is always an open research area for cluster-based routing as it directly affects performance. In the other hand, most approaches are designed to work only for a small networks. New research work should be conducted to design more scalable clustering techniques which can work with larger and changing network sizes. Finally, while the architectural implications of different scenarios (single-hop or multihop) justify the proposal of alternative communications protocols, it may be interesting to design highly configurable (MAC/routing) protocols that could provide more than acceptable performance for applications with different needs. In the field of data aggregation, there are two main future directions for researching. On the one hand, assuring privacy is an important issue when sensitive data must be aggregated and transmitted. Another interesting approach for aggregating data is to exploit machine learning to select efficient cluster heads or for dimensionality reduction of the data at sensor nodes or cluster heads. Compressive sensing (or compressed sensing) is another relatively new idea for data reduction since it does not need any previous knowledge or assumption on data correlations. The great advantages in terms of limitations and data reduction (part of the redundant data is never acquired) make compressive sensing the most widely used technique presently in IoT, even in the case of multimedia sensor networks.