## 2 Background  
Previous works have proposed the use of wireless sensor networks as a solution to various monitoring application scenarios. For example, [8] proposed FireNet, a WSN architecture for fire rescue applications. The work addresses some requirements and challenges for wildfire application. Similarly, in [9], the authors described a framework that included the system architecture, hardware, and software designs. Although this work provides insight on the necessary requirements for wildfire monitoring, it did not provide the framework implementation.  
In addition, [5] proposed a design for wildfire monitoring using wireless sensor networks. The authors provided some field testings prescribed as test burns. One of the design goals is to investigate how a sensor node copes with high degree temperatures, without losing much of the sensed data. In another related literature [10], they proposed FireWxNet, a multi-tiered portable wireless system for monitoring weather conditions in a wild-land fire. All these works focus on framework and deployment architectures of WSN. They do not address the data acquisition protocol that runs on the sensor node devices and on the sink node.  
Due to the energy spent in receiving and forwarding data, advances in hardware and software solutions have been proposed recently. From the hardware point of view, energy neutral designs are on the increase in the development of WSNs, such as in [11, 12], while from the software point of view, an application-specific approach is proposed in [13, 14]. Their method combines adaptive sampling and an energy-aware routing strategy for a flood warning system called FloodNet. One key elements in these studies is that, hybrid approaches have shown to improve the longevity of WSN deployment if properly designed.  
A survey on energy conservation in WSNs was carried out by [15]. The study highlighted various design methods and the state-of-the-art techniques employed in this domain. Similarly, a more recent study by [16] used an adaptive sampling technique for a snow monitoring application. Their work revealed improvements on energy consumption compared with a fixed-rate sampling method. However, their approach is computationally intensive. In addition, the algorithm is executed at the sink for each sensor node, hence centralized. Sending frequent updates to several nodes in a lossy medium could be counter-productive. Moreover, their work focused on how to reduce oversampling in their application scenario and in some instances the nodes are switched off. This is different from our work, where the nodes are autonomous and where the goal is to achieve a continuous sampling, while limiting unnecessary communication without jeopardizing useful events in the monitored environment.  
In a nutshell, we focus on the implementation of the software architecture that runs on the sensor nodes and the sink particularly designed to meet the requirements of a fast-reactive network. Our algorithm design can also be useful for any application that requires high responsiveness and a low data transmission, without necessarily missing any useful event throughout the monitoring operations. Furthermore, our approach can be implemented together with any of the frameworks described in [8, 9]. For the purpose of empirical verification and due to straightforward method of testing our method, we present a wildfire monitoring application as a use-case. We believe our design approach can easily be extended to other similar event-based application scenarios.