The IPv6 routing protocol for low power and lossy networks (RPL) was designed to satisfy the requirements of a wide range of Internet of Things (IoT) applications, including industrial and environmental monitoring. In most scenarios, different from an ordinary environment, the industrial monitoring system under emergency scenarios needs to not only periodically collect the information from the sensing region, but also respond rapidly to some unusual situations. In the monitoring system, particularly when an event occurs in the sensing region, a surge of data generated by the sensors may lead to congestion at parent node as data packets converge towards the root. Congestion problem degrades the network performance that has an impact on quality of service. To resolve this problem, we propose a congestion-aware routing protocol (CoAR) which utilizes the selection of an alternative parent to alleviate the congestion in the network. The proposed mechanism uses a multi-criteria decision-making approach to select the best alternative parent node within the congestion by combining the multiple routing metrics. Moreover, the neighborhood index is used as the tie-breaking metric during the parent selection process when the routing score is equal. In order to determine the congestion, CoAR adopts the adaptive congestion detection mechanism based on the current queue occupancy and observation of present and past traffic trends. The proposed protocol has been tested and evaluated in different scenarios in comparison with ECRM and RPL. The simulation results show that CoAR is capable of dealing successfully with congestion in LLNs while preserving the required characteristics of the IoT applications.

The rapid development of the Internet of Things (IoT) concept has promoted the presence of routing protocol for low power and lossy network (RPL). Unlike traditional applications, many applications envisioned for IoT networks may have different and sometimes conflicting requirements. In this context, the underlying routing protocol requires to provide quality of service (QoS) for multipurpose IoT and is inevitable. However, the routing approach in RPL is not efficient because default objective functions (OFs) rely on a single metric, which can result in a tradeoff in routing performance, particularly for multipurpose IoT that enchant different QoS requirements in the same network. Although RPL specification allows the use of multiple metrics for parent selection, however, no specific guideline is defined for metric combinations. Besides, many studies have revealed that RPL encounters severe problems in large scale networks as it was mainly designed for low data traffic network. To address these problems, in this paper, we primarily focus on QoS differentiation by exploiting the multi-topology routing feature of the RPL standard. For this, we propose different OFs, which ensures the QoS differentiation at the network level by splitting the physical network virtually into multiple RPL Instances. Each Instance can incorporate different traffic by associating with differing OFs, and routed it through the corresponding virtual network topology. We also present a new parent selection framework based on a multi-attribute decision-making approach that addresses the single routing metric problem in RPL. The extensive simulation results verify that our multi-topology routing approach can support the QoS provisioning and is suitable for large scale networks as compared with standard RPL.

Internet of Things (IoT) is expected to have a significant impact on city’s service provisioning and make a smart city more accessible and pragmatic since the deployment of heterogeneous smart devices in each infrastructure of cities is increasing. So far, the IPv6 routing protocol for low power and lossy networks (RPL) is considered to fit on IoT infrastructure for achieving the expected network requirements. While RPL meets the IoT network requirements quite well, there are some issues that need to be addressed, such as adaptability to network dynamics. This issue significantly limits the use of RPL in many smart city application scenarios, such as emergency alerts with high traffic flows. As part of a smart city vision, IoT applications are becoming more diverse, which requires context-awareness in routing protocols to support the behavior of the network. To address this issue, we design an objective function that performs the route selection based on fuzzy logic techniques while using contextual information from the application. For this, we present a new context-oriented objective function (COOF) that comprises both nodes as well as link metrics. Further, we suggest two new routing metrics, known as queue fluctuation index (QFI) and residual energy index (REI), which consider the status of queue utilization and remaining energy, respectively. The metrics used are designed to respond to the dynamic needs of the network. The proposed approach has been examined and evaluated in different scenarios when compared to other similar approach and default RPL objective functions. Simulation experiments are conducted in Cooja network simulator for Contiki OS. The evaluation results show that COOF can cope with network dynamics and IoT-based smart city application requirements.

The green industrial Internet of things (IIoT) is emerging as a new paradigm, which envisions the concept of connecting different devices and reducing energy consumption. In multi-hop low power and lossy network, a resource-constrained node should aware of its energy consumption while routing the data packets. As part of IoT, the routing protocol for low power and lossy network (RPL) is considered to be a default routing standard. Recently, RPL has gained a significant maturity, but still, energy optimization is one of the main issues, because the default objective function (OF), which makes routing decision mainly based on a single parameter, such as link quality, and ignores the energy cost. Therefore, this paper aims to consider the concept of green IIoT concerning how a routing approach can achieve energy efficiency in resource-constrained IoT networks. For this, we propose a resource aware and reliable OF (RAROF), which constructs an optimum routing path by exploiting the information regarding the duty cycle, link quality, energy condition, and resource availability of a node. In addition, we propose node vulnerability index (NVI), a new routing metric that identifies the vulnerable nodes in terms of energy. To deal with the diverse data traffic of the IIoT network, we implement a multi-queuing based traffic differentiation approach that ensures the application requirements. The extensive simulation results show that the proposed RAROF can effectively extend the lifetime of the network, enhance the energy efficiency, and achieve higher reliability than that of other OFs. In this way, RAROF makes a routing decision with the purpose of extending network lifetime and minimizing energy depletion, paving the way towards green IIoT.

The IPv6-based Routing Protocol for Low-Power and Lossy Networks (RPL) is a new routing protocol designed to satisfy constrained networks such as 6LoWPAN. The default route selection technique of RPL takes into account a single metric, which is unable to recognize the load balancing problem in the network. As a consequence, energy may drain rapidly from overloaded nodes, and the nodes that die first can cause a partition in the network. To resolve this problem, we propose a new RPL-compliant objective function named the Energy Balance Objective Function (EBOF). It combines two different routing metrics, which are the Expected Transmission Count (ETX) and the Child Count (CC). Then, the Destination Oriented Directed Acyclic Graph (DODAG) Information Object (DIO) message format is amended to disseminate the CC information among neighbor nodes. Two metrics are utilized to select the best path and to construct a balanced network. The proposed scheme is examined through simulation tests and evaluated against the Minimum Rank with Hysteresis Objective Function (MRHOF), the Load Balancing Model for RPL (ALABAMO), and Load Balanced Objective Function (LB-OF). The resulting analysis shows that the proposed EBOF significantly achieves the energy balance among intermediate nodes, which in turn prolongs the network lifetime.