## 1 Introduction
Hence, when the surface, the diversity, and the complexity of the farm increase, the number of sensing nodes is expected to increase proportionally. However, as the number of nodes in the LLN increases, the volume of data similarly increases as well; leading to problems like network overhead, traffic congestion, bandwidth consumption, transmission delay, etc. Inherently, in this application domain, in-network data aggregation techniques become naturally very attractive; mainly because of their capacity to reduce the amount of transferred data [13]. However, and due to the typical context of a smart farm [12] in terms of data heterogeneity and parcel/sector level aggregation requests, standard RPL clearly turns out to be inefficient for the implementation of many kinds of in-network aggregation techniques. Through the paper, we illustrate the RPL limitations, especially in terms of in-network aggregations. This motivates the need for an improved RPL, which is capable of building a routing topology that enables efficient in-network data aggregation, taking into account the physical partition of the farm into parcels/sectors. For this purpose, we propose a new objective function to be integrated into RPL [5], so that the built tree-like structure rooted at the sink node, called Destination Oriented Directed Acyclic Graph (DODAG), becomes more suitable and more appropriate for such type of applications. Our proposal is an enhanced version of RPL, and is called PA-RPL, standing for Partition-Aware RPL [14]. An attractive feature of this proposal is the fact that its tree structure reflects the spatial partition of the monitored farm. With such a structure, the collected data in each parcel will be gathered in one particular elected node within the parcel, called Parcel Head (PH), in order to be processed/aggregated before being sent upward to the sink node. This approach is intended to improve RPL in order to enable efficient in-network data aggregation of the collected information for the monitoring/decision system in SF. To the best of our knowledge, none of the literature research works paid enough attention to optimizing the DODAG structure through considering the spatial partitioning of farmlands. In this paper, we recall our proposal, PA-RPL [14], and show how it can be used to enable in-network processing leading to better performance. The key contributions of this research are summarized as follows:  
1. We first recall our new RPL-based routing algorithm, called PA-RPL [14], that adapts the structure of the DODAG, considering the spatial division of the monitored farmland. Through its inherited nature, we show how our proposal enables efficient implementation of aggregation techniques applied to the upward traffic. 2. We then present a proof of concept of our proposal through the simulation of an arbitrary farmland composed of a variable number of parcels. 3. We also assess the performance of PA-RPL [14] in comparison to standard RPL, in case of a centralized data collection scenario, and when high percentage of parcels are simultaneously monitored. We show that in both cases, PA-RPL presents good performance comparable to those of the standard RPL. In contrast, when only few parcels are simultaneously supervised, the PA-RPL shows high gain in energy, reaching up to 20%. 4. Finally, we demonstrate the superiority of PA-RPL over the standard RPL through simulating a realistic Smart Farming application. In this work, we have considered a particular application aiming to detect and prevent the dangerous potato phytophora infestants disease. Our simulation and analysis report substantial energy savings that go up to 40%. The remainder of this paper is organized as follow. In Section 2, we report a set of related works that concern the application of RPL in the context of farming. In Section 3, we describe the mechanism of standard RPL, while in Section 4, we present our proposal, the PA-RPL algorithm [14].