# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 2 Related Works
The authors in [19] describe the problems of the additive based metric composition method and a new, chaotic genetic algorithm-based approach has been proposed to solve these problems. The authors in [20] proposed an energy efficient and reliable composite metric for RPL networks. This composite metric takes both energy efficiency and reliability into account. Reliability is represented by the ETX metric, which aimed at selecting paths with good quality, and the energy metric aims to balance energy consumption between nodes in the network. The proposed composite metric is known as the lifetime and latency aggregateable metric (L2 AM) and it enables protocol finding optimized routes. L2 AM presents two methods for estimating the remaining energy of the nodes. However, for LLN networks with heterogeneous applications in terms of varied QoS requirement, it may not be adapted. Moreover, this approach focuses on distributed solutions to improve network lifetime, which might not be an obvious choice for heterogeneous networks under heavy and dynamic traffic scenarios, such as smart city networks. Besides, efficient resource utilization is an important aspect of heterogeneous networks, which is not included in L2 AM. The authors in [21] considered the unreliability and instability issues of RPL. The authors state that the instability in RPL might occur from the frequent route changes, which negatively impact on network performance. They further claimed that there are no metrics in the RPL scope, which indicates the stability of the nodes. Therefore, a new routing metric, called stability index (SI), has been proposed that takes advantage of the stability characteristics of nodes. SI relies on the control message transmission rate to estimate the stability of routing topology. Besides, this mechanism uses passive network layer to calculate the bi-directional ETX. However, this improvement is favorable for homogeneous networks and it does not consider heterogeneity character of the nodes, which is  
Figure 1: Taxonomy of Routing Protocol for Low Power and Lossy Networks (RPL) objective function enhancement mechanisms. usual in smart city network infrastructure. In addition, traffic estimation on nodes is only possible in networks with symmetrical and bi-directional links, but this is not always reasonable. In [22], the authors proposed a lexical composite based new energy-aware objective function, called (EAOF), for the RPL routing protocol. It selects the routes based on the combination of link ETX with residual energy to enhance the network lifetime. This study highlights the issue of energy efficiency and balance in RPL based biomedical wireless sensor networks. This issue is obvious, because the current RPL objective function does not use any energy-based routing metrics. Each node running EAOF selects the nodes that have lower ETX (reliable link) to the root (sink) from its neighbors. Subsequently, from this candidate parent subset, a node selects the best parent based on the maximum remaining energy. Even though EAOF attempt to address the energy consumption and reliability issue for medical application, it may not resolve the extensive resource constraints issue in heterogeneous network scenarios. It is because EAOF only considers the energy resource, which is not satisfactory for highly diverse and dynamic network scenarios, like the smart city concept. The authors in [23] present the solution of long single-hop problem in MRHOF and OF0. They report that, as the number of nodes in the network increases, both OFs introduce long-hops, causing bottlenecks that limit the network. To solve the long-hop problem in RPL, a new routing metric, called SIGMA-ETX, has been proposed and optimizes both MRHOF and OF0. A composite metric SIGMA-ETX is designed by combining the ETX and min hop count metrics while using the standard deviation of ETX among each node.