# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 1 Introduction
In addition, IoT is transforming smart objects from being traditionally intelligence by integrating diverse cutting-edge applications, standards, and protocols, which in turn augment data processing and management, actuation, and analytics. Consequently, with modern sensing and computing capabilities, the collected data are analyzed and processed in real-time to extract the actionable information, which will enhance the decision making of city authorities and citizens, and thus a city can be formed as instrumented, interconnected, and intelligent. Therefore, integrating the various technologies maximizes the benefits of using IoT, and opening a new perspective to applications that take context information into account, such as environment monitoring, emergency alert, etc. Here, the context typically refers to information collected about specific circumstances, and it can also include a place and events. In addition, the context might also contain different information that may be used to distinguish the state of affairs of the entity involved in the interaction between the application and the user. The ZigBee Alliance and the IETF have recently developed several standards to enable resource-constrained devices for interacting efficiently in IoT based networks. Routing over low power and lossy networks (ROLL) working group of IETF has focused on routing solution for the areas, such as smart building, connected home, and urban sensor networks, and developed the protocol specification, called the IPv6 routing protocol, for low-power and lossy networks (RPL) [5]. Resource-constrained embedded devices use RPL to communicate at the network level and easily connect to the Internet through various links, such as Bluetooth, IEEE 802.15.4, and power-line communication (PLC); RPL is therefore considered to be an IoT routing protocol. Basically, RPL has been standardized to address the low-power and lossy network (LLN) requirements for 6 LoWPAN networks that bring the concept of IoT into real life. It is considered to be an ideal candidate to deal with both diverse network characteristic as well as varied application requirements. In LLNs, the routing protocol is a key issue and RPL was intended to accommodate the unique characteristic of such kind of networks. Taking this into account, RPL allows the open design of different performance objectives in order to satisfy the requirements of different applications in the network. For instance, a LLN that is deployed for real-time reporting system under emergency scenarios should meet the requirement of maximizing reliability and minimizing delay, whereas a network that was deployed for the ordinary video-surveillance system could mainly emphasis on maximizing the network life by conserving the energy. However, this concept in RPL creates the challenges in defining the forwarding strategy as well as selecting routing metrics to fulfill the particular network and application requirements. Although RPL is independent of the rest of the protocol stack due to its self-healing, self-configuring topology structure, according to the evaluation of [6], the default RPL cannot meet the QoS requirements of modern smart city notion, such as network lifetime, resource limitations, topology adaptation, and resistance of network dynamics, etc. The inherent network services for LLNs must fulfill some requirements to support IoT-based smart city applications, such as adaptability to the dynamicity of data traffic [7]. These requirements are properly specified in IETF RFC 5548 [8]. However, the current RPL specification can perform poorly with regards to meeting these requirements, which limits the adaptation of RPL in many IoT-based smart city applications. This is because the directed acyclic graphs (DAGs) that were constructed by the default objective function (OF) might not utilize the network and node's resources efficiently. The sensor devices in LLN typically face resource shortages, where energy is one of the most important resource constraints.