# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 3 Related Works
### RPL Optimization for Resource-Constrained Networks  
In the RPL scope, route selection is primarily based on a single metric, which may lead to an unbalanced network load. The overloaded node may consume more energy, thus draining their battery quickly than others, which eventually causes a network partition; this results in a significant impact on overall network performance and QoS requirements. Hence, an energy-oriented routing approach for QoS support is one of the key challenges in the resource-constrained network like IIoT. In this regard, some energy optimization approaches for RPL implementation have been presented by the research community in the recent years. Most of the work focuses on designing the OF that includes new energy related routing metrics. The authors in [38] proposed an energy efficient and reliable composite metric for RPL organized networks. The proposed composite metric considers both energy and reliability parameters to select the optimum path. It takes into account the reliability through link metric, i.e., expected transmission count (ETX), and balances the network energy consumption through considering the nodes' energy availability. To make the energy-efficient and reliable RPL, the proposed metric named lifetime and latency agreeable metric (L\({}^{2}\)AM) allows the nodes to select the best parent by avoiding the congested one by estimating the traffic. However, it ignores the duty cycle status of a node during the parent selection process. In addition, the congestion alleviation process can cause frequent parent change in the network, making the network unstable, which is not appropriate for emergency monitoring IIoT network. The authors in [39] proposed a virtual version of the RPL protocol, referred to as optimized multi-class RPL (OMC-RPL), based on a software-defined networking approach and virtualization. OMC-RPL focused on designing the enhanced OF with a composite routing metric. For this, it combines four weighted metrics to support multi-class traffic using software-defined networking (SDN)techniques. The weights of the metric are determined using the particle swarm optimization algorithm. Out of four metrics, the energy metric is considered as a secondary metric (optional) and therefore used whenever required. Even though the OMC-RPL considers different metrics, including the energy parameter, this virtual version of RPL may not fulfill the IIoT application requirements because it limits the energy profile of the nodes. Recently, a new mobility-aware and energy-efficient parent selection method for LLNs has been proposed in [40]. The parent selection scheme select the optimum parent based on different routing metrics, such as ETX, expected lifetime, received signal strength indicator (RSSI), and distance of the mobile node and the parent node. Additionally, the authors proposed an optimized version of the Trickle timer called dynamic Trickle. This parent selection method mainly focuses on the issue of switching from one parent to another due to mobility. To prolong the network lifetime and minimize the energy use in smart grid networks, the authors in [41] proposed the new routing mechanism. For this, an efficient routing metric named energy and congestion-aware routing metric (ECRM) has been designed. ECRM considers the nodes' queue and residual energy information to select the next-hop node towards the root. To improve overall network performance, the node simply selects a parent node with a less queue occupancy and high remaining energy. However, finding such a node is not always be feasible. Different metric composition techniques have been presented in the literature to enhance the OF implementations in RPL and optimize the network resources. In this regard, the authors in [42] presented a new objective function named context-oriented OF (COOF) to utilize the network resources efficiently. COOF selects the best route based on the fuzzy logic technique using contextual information in which the node's queue, energy, and link status are considered as resource context.