## 2 Integrating Precision Agriculture and IPv6 Low-Power and Lossy Networks  
Precision agriculture is considered as a use-case scenario based on the LLN framework. A-LLN, which is the integration of PA and LLN, presents the intrinsic characteristics of WSNs used by typical agro-environmental monitoring systems. On the one hand, LLNs exclusively use IPv6 which is the key component of IoT [14]. On the other hand, WSN will be the most popular and practical solution for ICT in agriculture [2] when wired network deployment cannot cover the cultivated fields, grazing lands and monitored sites, which may reach several tens of hectares, with controllable costs. Thus, an efficient A-LLN routing solution can push the WSNs-based application of Information and Communications Technology (ICT) in agriculture to IoT. As a proprietary type of LLN, the RPL applicability analysis of A-LLN inspired by [15] has been elaborated in [11]. As a new trend in the agricultural sector in the past few years, ICT in PA concentrates on providing various techniques to cover a wide range of applications, from herd management to field crop production [16]. In most of the cases, PA is always referred in the issues of site-specific crop management and includes three aspects:  
* Environmental monitoring (e.g., temperature, light intensity, atmospheric pressure, soil moisture or air humidity, UV intensity, strength and direction of wind, rainfall, gases, pH of dust or rainwater, and heavy metals) in a field which is separated by some complete parcels;
* Utilizing DSS to obtain possible treatments analysis, which can be applied for field-wide or specific parcel;
* The methods of adjusting corresponding operations in real-time, such as fertilizer, lime and pesticide utilization, tillage, irrigation, and sowing rate. In the current PA domain, diverse applications have been developed and wireless sensor motes are scattered throughout the cultivated fields to monitor needed data, such as soil moisture, atmospheric temperature, light and wind strength, hours of sunshine, rainfall measurement, and humidity of the leaves. These sensory data are the basic component of a DSS that enables resource (e.g., water, herbicide, pesticide, fertilizer, _etc._) optimization [17], disease detection and development prediction, pest control, frost protection [1], avoiding environmental pollution, intruder detection, yield prediction, fire detection, _etc._  
The A-LLNs infrastructure of a PA system is normally composed of 20-50 devices which are typically interconnected using wireless technologies (e.g., ZigBee PRO or Wi-Fi protocols [2, 18, 19, 20]) with a backhaul network providing connectivity to \"command-and-control\" management software systems (e.g., DSS) at the data processing center of an experimental farm. Note that the current researches of PA system [18] are mainly dedicated to the needs of modern agricultural paradigm shift, remote monitoring/control, asset tracking and distance diagnosis. With the help of wireless connectivity and access from A-LLN nodes, users can collect significantly increased amounts of information and remotely manage a larger number of control points. One A-LLN is the smallest part in this scenario and it would contain and be equipped with three kinds of key nodes that are outlined below. Notice that the appellation of A-LLN sensor node is a substitution of WSN nodes. Its main purpose is also to offer measurement sensory information. Considering the deployment of A-LLN nodes, they will be manually deployed in a monitored field within defined topological constraints under the assistance of GPS. In addition, a number of trades-offs have to be considered: equipment maintenance costs, network density, network lifetime and existing IP network infrastructure [21]. It is usually the case that A-LLN traffic patterns are highly asymmetric, where the majority of the traffic volume generated by the A-LLN sensor nodes typically goes through the LBRs, and is directed to the data center servers, in a Multipoint-to-Point (MP2P) fashion.