## 4 Partition Aware-RPL Protocol Overview
### Parent Selection Algorithm
In modern potato farms, late blight disease is commonly treated using a set of chemical fungicides. To optimize the number and doses of fungicide applications per season, numerous decision support systems, based on late blight forecast models, have been developed [34]. As expected, these models are based on climatic data, mainly temperature and humidity. The goal is either to react proactively by avoiding to meet the late blight adapted weather conditions (i.e., in case of greenhouses), or re-actively, by the application of fungicides quickly before the disease spreads out (i.e., in case of open potato fields). The use of weather data for late blight forecasting has long been a common practice by farmers. In 1951, [PERSON] [35] set in place a first empirical forecast model based on weather data used for blight gardens. This model assumes that the disease evolution is proportional to the duration of exposure of the plant to a humidity higher than 90% in certain interval of temperature. Later, the [PERSON]'s model was some improved giving what is called the _SIMCAST_ forecast model [36]. The model describes the degree of development of the Phytophthora Infestans in potato by computing an equivalent risk factor called Blight units. This risk factor is computed based on the number of successive hours where relative humidity is \(>=90\%\), and the average temperature belonging to any of these respective ranges: (<3\({}^{\circ}\), 3-7\({}^{\circ}\), 8-12\({}^{\circ}\), 13-22\({}^{\circ}\), 23-27\({}^{\circ}\), and \(>\)27\({}^{\circ}\)). Based on the SIMCAST forecast model [36], many decision support systems were developed to prevent from potato late blight, such as euroblight [37], blightPro [34], and castor [38]. In these systems, the cumulative estimated blight units are used in order to make decisions about the timing/scheduling of fungicide application on the plants. Recall here, that the goal of this section is not to create a more effective prediction model, or a better schedule for fungicide application. We are rather interested in providing an effective, long life, and reliable sensor/actuator network that can effectively support such existing support decision systems, or more precisely, an effective management of such network. Figure 5:0 Phytophtora life cycle. ### Performance Evaluation without In-Network Aggregation: PA-RPL vs. RPL

After presenting the late blight pathogen, we move to describing our considered scenario where a wireless sensor/actuator network is deployed. We thus assume that we have a farmland similar to the one described in Figure 6j, where we grow potato crops of different types or planted at different times, along with other kinds of crops. A set of nodes (sensors/actuators) are uniformly distributed across the farm. At this stage, our goal is to evaluate the performance of PA-RPL in comparison to the standard RPL, when a standard centralized data collection application (i.e., a conventional support decision system) is running at the sink node, i.e., without any in-network processing. It is clear that this is not the scenario where PA-RPL performs best. In fact, the load balancing of the DODAG is potentially affected by the spatial partitioning of the farmland and the nodes distribution. However, through this study, we try to answer two main questions corresponding to two main case-studies:

* In comparison with RPL, how effective is PA-RPL in a scenario where all parcels of the farmland are monitored simultaneously? * In comparison with RPL, how effective is PA-RPL in a scenario where only few parcels of the farmland are monitored simultaneously? #### 6.2.1 Case 1: All Parcels Are Simultaneously Monitored

For this scenario, we assume that all monitored parcels are growing potatoes, and that the number of nodes might cover all the parcels in the farmland. Our goal here is to push the PA-RPL to its limits and see how it would perform if we need to collect data, at the same sustained rate and from all parcels in the farm (i.e., draw a theoretical lower bound on the PA-RPL performance).