## 1 Introduction  
Wireless sensor networks (WSNs) are designed to provide information about the environment they are deployed to sense. They are typically characterized by a limited energy budget, because they are battery-powered. In recent years, wireless sensor networks have continuously gained attention, particularly due to their increasing capabilities to sense, store, process, and communicate information at low cost. For example, commercial sensor nodes such as the TelosB from Memsic [1], as well as W24 TH from WISPES [2], and the software platforms from TinyOs [3], have facilitated the building of real-world testbeds both for convenient testing and for the evaluation of various application scenarios. The demand for the use of WSNs for applications such as health, habitat, seismic and wildfire monitoring has driven the growth of WSNs over the years partly because of the new era of Internet-of-Things (IoT), which is currently pushing the growth of the machine-to-machine (M2M) model of communication between several heterogeneous devices. We can expect that the use of WSNs in these fields will grow dramatically in the coming years. Most implementations of WSNs are driven by different application-specific needs. The majority of the current real-world deployments have been applied to event detection systems. Events as used in this context may range from comparatively simple detections, such as changes in micro-climate condition in a home environment, wildfire monitoring, _etc._, to more sophisticated scenarios such as intrusion detection, patient physical movement in a hospital _etc._ The authors in [4] (p. 441) reviewed two main approaches to tackle event detection problems, in particular they discussed extensively an algorithmic approach, which includes threshold usage, pattern recognition, and anomaly detection methods. One of the key characteristics of these methods is that they all leverage on the data processing capability of the sensor nodes to locally extract semantically meaningful information from the sensed data. Overall their goal was to limit the energy consumption in the WSNs by avoiding data transmission, whose cost could be prohibitively high for a continuous data gathering application. Our approach to tackle the event-detection problem is by leveraging on both an architectural design framework and an algorithmic approach. To achieve distributed inference, we utilize a distributed compressed sensing model and data prediction, together with a threshold method as used in [5] to aggressively gather data when necessary. Our method could easily be applicable to a more sophisticated detection system. For example, the threshold method could be replaced with a learning technique such as in [6] or replaced by a sample splitting method [7]. For the remainder of this article, we will focus our attention on the _threshold method_, which could be applied to monitoring applications such as micro changes in climate condition, flood detection, wildfire detection, or applications where a sensor can detect a critical boundary of the measured value. Instead we leave the design of distributive inference solution to complex event detection problems such as face, pattern or speech detection, as an open research issue. The contribution of this work is as follows: (1) We propose a data acquisition scheme for an event-based reactive network, which combines compressive sensing (CS) and prediction algorithms; (2) We design an adaptive sampling technique with feedback from the monitored event; (3) We validate the performance of SWIFTNET on a commercial off-the-shelf node; (4) We quantify the performance of SWIFTNET using a real test-bed environment to demonstrate that it is effective in prolonging the network lifetime of WSN in a wildfire monitoring application, despite being extremely light-weight. Despite previous attempts to find solutions to data acquisition in WSN, to the best of our knowledge, none has a solution that combines compressed sensing and prediction algorithms.