# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 2 Related Work  
According to [16], RPL makes use of metrics specified in the RFC 6551 which are suitable for IPv6 over Low Power Wireless Personal Area Networks (6 LoWPAN) environments, considering the number of hops; latency; delivery ratio, node energy; throughput; level of link quality; and transmission reliability. In [17], a standard objective function, called Objective Function Zero (F0), was proposed for RPL. It was designed to enable interoperability among different implementations of RPL. The operation of OF0 is simplified and does not use any routing metrics for the definition of the rank (position in the routing tree). A device chooses its preferred node considering the neighbor within those that presents the lowest rating. The Minimum Rank with Hysteresis Objective Function (MRHOF) it is presented in [6]. This OF is only suitable for the metrics specified in RFC 6719. The selection of the favorite \"parents\" is made taking into account the adopted metrics, where the routes that minimize the cost associated with the metrics are preferable. As standard, MRHOF uses the Expected Transmission Count (ETX) metric, from [14], which assesses the quality of the link and aims to maximize the leakage of data. The ETX is defined according to Equation (1). \[\text{ETX}\;=\;\frac{1}{D_{f}\cdot D_{r}} \tag{1}\]  
where:  
* \(D_{f}\) is the probability of the package being received by the neighbor; and
* \(D_{r}\) is the probability of the ACK being received successfully. The authors of [18] proposed a context-aware approach that changes the functioning of a sensor node based on the phenomena collected from the environment, such as temperature, humidity, pressure, etc. This contribution called Situation-Aware Adaptation Approach for Energy Conservation in Wireless Sensor Network (SA-A-WSN), aims to monitoring the way the sensor node works in the environment in order to reduce the network's power consumption. According to [13], context-sensitive computing has been gaining more market and relevance since it is currently being considered an important component of IoT, mainly because context-sensitive systems are able to adapt their behavior to current conditions with no explicit intervention from users. A context-sensitive objective function, called Context Aware Objective Function (CAOF), proposed by [14], is based on residual resources and on changing the sensor node's status over time. The function proposed by [15] performs a weighted sum of the following metrics: degree of the node connectivity, level of battery power and position of the node in the routing tree regarding the parent node. The ultimate goal of the function proposed in [15] is to find a probability of delivery to each sensor node. The use of fuzzy logic to calculate the objective function for RPL protocol is proposed in [19]. The authors describe that the objective function is the component used to select the paths by identifying a parent node among many existing nodes. The objective function, called QoS-aware fuzzy logic objective function (OF-FL), associates parameters with linguistic variables which are combined with fuzzy rules in order to identify the route to be selected. The parameters considered in OF-FL are hop numbers, end-to-end delay, packet loss rates and default route change rate. In [20], a Scalable Context-Aware Objective Function (SCAOF) is proposed. It adapts the RPL protocol to the environmental monitoring within the area of agriculture with a scalable context. The performance of the SCAOF was assessed both through simulation and field testing. The experimental results obtained confirm that SCAOF can extend the network's service life and improve the Quality of Service (QoS) in the different agriculture-oriented simulation scenarios. The work included in [6; 7; 8] represents recent research work considering objective functions in the route selection process. In [6], the performance of two RPL objective functions (the Minimum Classification with Hysteresis Objective Function (MRHOF) and the Function Zero Objective (OFO)) are analyzed.