## 1 Introduction  
The Internet of things (IoT) [1] is a networking paradigm that covers the extension of the traditional Internet to integrate the so called Lossy and Low power Networks (LLNs). A LLN is mainly made up of a high number of tiny and low cost IP-enabled sensor nodes with limited power, limited memory, and limited processing resources. They are interconnected using a variety of short range, low rate, lossy, and low power communication protocols. Since its advent, IoT enabled the emergence of various novel applications including smart buildings, smart cities, smart grids, etc. In this work, we rather focus on a particular an interesting application domain of IoT, that is precision agriculture (PA), also referred to as Smart Farming (SF) [2]. When an LLN is deployed in a large area, it is mandatory to use a routing protocol in order to route/guide data through multi-hop connections [3]. This was the main goal of the Routing Over Low power and Lossy networks (ROLL), originally defined by the Internet Engineering Task Force (IETF)in [4]. In March 2012, this group proposed a new routing protocol, called Routing Protocol for Low power and lossy networks (RPL) [5], designed for IP-enabled smart objects [6]. RPL [5] has quickly gained in popularity, thanks to various factors; most importantly, its flexibility to adapt to different network topologies [7]. While RPL was developed to serve four main applications at first: home automation, industrial control, urban environment monitoring, and building automation. Therefore, and by extending the use of RPL in different other domains, it is expected that any new application, Smart Farming as an example, will bring new issues, constraints, and challenges. For instance, a farm is generally composed of a set of parcels. Each parcel is a contiguous piece of land in which one single crop is cultivated [8]. The partition of the land into parcels is a well known farming management technique in arable spaces. In fact, this spatial partitioning approach of the agricultural field into parcels simplifies the management of the farming activities (irrigation, fertilization, etc.,). In addition, partitioning enables the farmer to diversify his crops both in space and time, either by cultivating several crops in his land, or by growing one single type of crops at different rates insuring a continuous production over time. Moreover, a farm may include other specialized buildings/facilities. These facilities could be dedicated either for breeding animals (stable, sheepfold, piggery, hen-house, hutch, breeding battery, and dovecote), cultivating special plantations (greenhouses, aquaponic/hydroponic farms, or tunnels), storing earth products (dryer, hayloft, barn, attic, silo, cellar), preserving high value materials (shed, hangar, ironwork, hut), or processing/transforming basic products (cereals, wine, cider, dairy, cheese, oil-seeds, etc.). Within the Smart Farming context, each of these special buildings and parcels requires surveillance and monitoring of a set of critical parameters [9]. They should be first collected then forwarded to a decision support system (DSS) [10]. Based on the collected data, the DSS gives insights to the farmer about how to appropriately face issues occurring in each parcel (or sector). This aims to support the whole farming activities including irrigation, pest protection, fertilization, infrastructure control, automated harvesting, alarms management, feed distribution, etc. It is important to note that each farm sector (parcel or building) usually has a built-in infrastructure [11], such as drip irrigation system, air conditioning settings, complex machinery, etc. Straightforwardly, and because of its intrinsic complexity, it is clear that a smart farm [12] will require an appropriate/accurate monitoring of a high number of critical parameters; in addition to the control of an important number of actuators [9].