## 5 Evaluation and Results
### Experimental Setup and Evaluation
#### 5.3.1 Prediction Model Consideration  
Here we examine what model is suitable for a wildfire monitoring environment, considering that there are several candidate prediction models as examined in literature [4] (pp. 163-170), namely: (1) Replicated models such as Kalman filters, normalized least-mean square, autoregressive models, _etc._; (2) Aggregative models such as, average model, distributed regression, _etc._ Recently, one of the main discussions in literature [65] on prediction models has been centered around its implementation on the node platforms. A common drawback of prediction models is that they require prehistorical data for the model build-up process, which makes it computationally expensive for a node to implement, especially due to memory limits, and hence they are not often feasible to implement. The mode of operation requires the sink node to build a model and to broadcast the model parameters to the entire network. We can easily see that if the model buildup process fails, either due to the constraint of a noisy wireless channel, the entire network will fail. Even if a model is successfully transmitted in the network, when the model ceases to properly fit the data, re-learning a new model would be time consuming for an application that requires high responsiveness, such as in wildfire disaster. The delays associated with gathering the training dataset could result in loss of lives and property.  
To better understand the environment for a wildfire scenario, when there is a fire disaster, there is a high probability that the wildfire characteristics, such as the intensity of the fire, location, sensing needs, duration of the burn, _etc._, will be different from the previous fire disasters. Hence, it is insufficient to use existing datasets to build _a priori_ an efficient prediction model for the future fire disasters. Also, since fast responsiveness is highly desirable in such an application, preliminary gathering of datasets to build a complex model would be undesirable. Based on these considerations, and due to the nature of the wildfire events, models such as multi-variate Gaussian [32], Dual Kalman Filter (DKF) [33], Autoregressive Integrated Moving Average (ARIMA) [34] and the likes are therefore not effective choices. Similarly, other studies such as [66] proposed to use neural network models, however, such models are complex to implement on a resource-limited sensor node. Hence, they are not suitable choice on our platform. The candidate prediction model should be computationally inexpensive, and should report readings that are \"outliers\" with high responsiveness. Models such as Normalized Least Mean Squares (NLMS) [49] and Fixed-Weighted Moving Averages (WMA) [39] (p. 8) are good candidates for a wildfire monitoring application. Due to the results of the comparative study conducted in [65] on data prediction models, we chose NLMS as the prediction model, together with our proposed adaptive sampling strategy; note that the default adaptive prediction method is used interchangeably as NLMS. Furthermore, the choice of NLMS is motivated by its simplicity and ease of implementation on our platform. The NLMS model, while simple, still offers excellent accuracy and high data reduction in WSNs for some event-based monitoring applications.