## 4 Enhanced Objective Function for Routing in Agricultural Low-Power and Lossy Network  
The main idea of our proposal is a routing solution of A-LLNs scenario based on RPL's OF with a combination of path weight value calculating algorithms and composite routing metrics, considering of elementary metrics defined in [26].  
In real world systems, the dynamic environment causes various issues, for example, the complexity of after analysis (_i.e._, unavoidable radio interference, noise, _etc._) and unpredictability of system problems (_i.e._, hardware, OS, _etc._), which are the main challenges for protocol design and evaluation. In addition, A-LLN devices have resource constraints, such as low memory capacity, low computation capability and low energy budgets. Unfortunately, the literature on improving and adapting RPL for specific real world applications is very scarce, if we consider that only the smart grid, industrial automation and E-health areas have been addressed, not to mention agricultural WSNs.  
To the best of our knowledge, our study is the first one to extend the conventional WSN of ICT in agriculture to the concept of LLNs [11], exclusively supporting IPv6 that is main ingredient of an IoT platform. Moreover, the main purpose of this contribution is to enhance the applicability, robustness, and scalability of RPL for A-LLNs. Thereby to adapt RPL to the precision agriculture application, we start with a simple OF by considering energy-aware metrics. Then, we propose a scalable OF with composite RPL routing metrics.  
For dealing with the intricacies presented by A-LLN, one routing metric is not sufficient to represent the QoS requirements defined by a PA application. Meanwhile, since the PA application needs its underlying A-LLNs respecting to a required level of QoS (delay, reliability, energy consumption and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Platform Name** & **Size of Network** & **Indoor/Outdoor** & **Hardware Platform** & **Evaluated RPL Model** \\ \hline
[55, 56, 57, 58] & Indriya testbed & 135 WSN nodes & Indoor & TelosB nodes with Arduino & ContikiRPL\(\sim\)ORPL \\ \hline
[59] & \
\begin{tabular}{c} SensLAB platform \\ of INRIA Lille \\ \end{tabular} & 100 WSN nodes & Indoor &
\begin{tabular}{c} WSN430 boards with TI \\ CC2420 radio chip \\ \end{tabular} & ContikiRPL \\ \hline
[60] & TinyRPL testbed & 51 WSN nodes & Indoor & TelosB motes & TinyRPL and BLIP \\ \hline
[61] &
\begin{tabular}{c} PLC testbed \\ on INRIA \\ \end{tabular} & 6 PLC nodes & Indoor & CC2420 & RPL for PLC network \\ \hline
[24] &
\begin{tabular}{c} Multi-hop \\ topology testbed \\ \end{tabular} & 30 WSN nodes & Indoor & TelosB motes & ContikiRPL \\ \hline \hline \end{tabular}
\end{table}
Table 2: Existing real world testbeds for the evaluation of RPL protocol.  
packet loss) a set of rational routing metrics are elected. We use a RPL routing metric composition approach [37] to combine different primary routing metrics to satisfy the needs of A-LLNs' characteristics and limitations. The corresponding mechanisms are designed as accessories of RPAL model to capture the concerned effects (e.g., link reliability, remaining energy, _etc._).