## 6 Conclusions  
This article is anchored on the improvements offered by a combination of data compression, prediction and adaptive sampling strategies, to achieve a robust intelligent data acquisition process in WSNs. We have proposed and implemented a light-weight data acquisition algorithm, termed SWIFTNET, for an event-detection monitoring system. One of the main contributions of this work is that we have successfully employed a mix of two major fields of data-driven methods, (1) a data reduction method, and (2) an energy-efficient data compression method. Both categories have been widely used separately in various domains, as surveyed in [15]. However, we have provided a holistic approach that combines a mix of efficient algorithms in these categories, to further optimize the energy consumption in a real-world wireless sensor network deployment. This is achieved by building an adaptive prediction method as a layer on top of a compressed sensing algorithm.  
Unfortunately, attempts to optimize energy efficiency are often in conflict with the goals of network reactiveness for alarms and urgent messages. Thus, we presented SWIFTNET, a data acquisition protocol for a reactive network application. A wildfire monitoring application has been presented as a use-case for our design framework, to test the performance of SWIFTNET in a real-world deployment testbed.  
The design method and implementation are presented, followed by the performance analysis. In summary, the performance of SWIFTNET proved to significantly improve the network lifetime of a WSN deployment compared with a default adaptive prediction approach. On average, over 50% improvement is achieved in terms of data transmission, which translates to energy savings, with no loss in accuracy over the default method widely referenced in the literature. We note that, although, the signal sources used are nearly sparse or exhibit an average sparsity rate, it is possible to reconstruct the original signal using SWIFTNET at a desirable level of accuracy without significant loss.  
In conclusion, this study reveals that the network lifetime of a WSN deployment can be significantly prolonged by employing an algorithmic approach such as SWIFTNET. This algorithm design incurs minimal cost, and requires no prior existing knowledge of the monitoring phenomenon. Hence, it is easily scalable for very large deployments that are envisaged to span into decades, requiring little or no human intervention. An interesting future direction for this study would be to test the capability ofSWIFTNET on a larger network comprising hundreds of nodes, and with respect to different application scenarios, such as monitoring drastic changes in micro-climate conditions indicating occupancy in a building. Similarly, a performance test of SWIFTNET on different network topologies could be an interesting direction. However, we leave these topics as open research directions for the future.  
The research contribution presented in this article has been supported by the project GreenDataNet (grant No. 609000) funded by the EU 7 th Framework Programme. Moreover, this work is partially supported by the Postgraduate Publishing Grant (No. GRS3091) of the University of Otago, New Zealand. Finally, the authors would like to thank WISPES srl for the implementation of the W24 TH prototypes. We would also like to thank all the anonymous reviewers for their valuable feedback.