Multi-Topology Based QoS-Differentiation in RPL for Internet of Things Applications

Abstract

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.INDEX TERMS Internet of Things, quality of service, low power and lossy networks, routing metrics, objective function, 6LoWPAN, border router.

Proposed RPL routing protocol in the IoT applications

“…This parent selection process may result in the flocking effect, which attracts nodes quickly and disrupts network balancing. 6 Consider the network in Figure 1 to have a better understanding of the situation. Nodes D and E, which have rank values of 10 and 11, respectively, are candidate parents of multiple nodes in this topology.…”

“…According to the RPL MRHOF objective function, each node chooses their preferred parent based on the lower rank which is based on a lower ETX value. This parent selection process may result in the flocking effect, which attracts nodes quickly and disrupts network balancing 6 . Consider the network in Figure 1 to have a better understanding of the situation.…”

Improved RPL Protocol for Low-Power and Lossy Network for IoT Environment

“…The current energy consumption can be calculated using Eq. (1) [28] where E con is the energy consumed by node (a), T mode is the time duration at which the node spends in each operating mode (i.e., T les , T Tx , T CPU , T sleep ), and P mode represents the power consumed in the corresponding mode at a given time (i.e., P les , P Tx , P CPU , P sleep ).…”

“…simulator that allows developers to create a powerful simulation environment that enables them to use completely emulated hardware devices on different scale networks to run their applications [28].…”

A QoS Optimization Technique with Deep Reinforcement Learning in SDN-Based IoT

“…They propose a deep-reinforcementlearning-based quality-of-service (QoS)-aware secure routing protocol (DQSP) that can extract knowledge from past traffic demands and optimize the routing policy dynamically with guaranteeing QoS. Authors in [12] focus on QoS differentiation by exploiting a multitopology routing feature in RPL. They propose different objective functions to ensure the QoS differentiation at the network level by virtualizing the physical network into several RPL instances.…”