## 4 Partition Aware-RPL Protocol Overview
### Parent Selection Algorithm
We fixed the transmission (respectively interference) range to \(50\) m (respectively \(100\) m). Our simulation alternatively runs the standard RPL [5] and the proposed PA-RPL [14], over all the nodes, for sake of performance assessment and comparative analysis. The farmland is split into a number of parcels, \(N\), that varies from 1 to 9. For each splitting number, the shape of the individual parcels is also taken quite arbitrary (or irregular), as illustrated in Figure 6a-i, corresponding to \(N=1\) to 9, respectively. In Figure 6j we show the whole farmland with nine parcels and the spatial coordinates of their borders. Figure 6: _Cont._In Figure 7, we illustrate the DODAG constructed by the standard RPL that is not aware of any limitations between parcels, here 8 parcels are shown. This can be seen as constraint-less DODAG (or blind DODAG), in which the spatial partitioning of the farmland is ignored. In fact, we observe that the communication links, cross the borders between the parcels almost randomly (in any point and any direction), enabling free/unconstrained communication between the nodes that are close to the boundaries. It is straightforward to see that standard RPL does not help the establishment of local (parcel level) processing. This is mainly because the information of nodes, belonging to same parcel, are not necessarily gathered and transferred through the same DODAG branch or sub-DODAG. We now apply the PA-RPL assuming the exact same system settings in terms of number of nodes, their type, and spatial location, their transmission and interference ranges, in addition to farmland/parcels' shapes and areas (i.e., Figure 6). The results are illustrated in Figure 8, where we draw the obtained DODAG for each splitting number \(N=1,\ldots,9\). We clearly see that PA-RPL adapts to the simulated parcel constraints (or the boundaries between parcels), and changes the DODAG structure accordingly. Figure 7(a) corresponds to the scenario where the whole farmland is considered as a single parcel. Figure 6: Splitting of the farmland. Figure 7: The DODAG built by RPL with 150 nodes. Figure 8: The DODAG built by PA-RPL. ## 5 In-Network Aggregation

When the surface, diversity, and complexity of the farm increase, the number of sensing nodes consequently increases. However, as the number of nodes in the LLN increases, the volume of data substantially increases as well. Therefore, and if we are not careful, our LLN would suffer from different problems such network overhead, traffic congestion, bandwidth consumption, transmission delay, etc. To overcome many of the aforementioned problems, techniques, such in-network data aggregation, become naturally very attractive, mainly because of their capacity to reduce the amount of induced flowing data inside the network [13]. In fact, in-network aggregation deals with a distributed processing of data at the intermediate nodes within the network, mainly executing aggregation functions. There are mainly two approaches of in-network aggregation, namely lossy and lossless:

* lossy in-network aggregation is the approach that combines and processes the data received by a sensor node from its neighbors by performing a lossy aggregation function such as MAXIMUM, AVERAGE, MINIMUM, etc. In this case, only the output of this function is forwarded up instead of the whole information. * lossless in-network aggregation refers to the process of merging or compressing the data received from different source nodes into a single data packet, i.e., the whole information is concatenated and then forwarded up. With the help of these approaches we can decrease the number of data exchanges in the LLN. Therefore, it increases the life time of the network and reduces its energy consumption. In order to aggregate data flowing from the source nodes towards the sink, the first step is to elect some special nodes that operate as aggregation points (or aggregators) and define a preferred path for forwarding data.