Different smart applications deploying the Internet of Things (IoT) can make communication decisions autonomously without human intervention. The routing layer protocols play a crucial role in providing interoperability for IoT devices.
The Routing Protocol for low power and Lossy networks (RPL) protocol assists the IoT infrastructure in operating in a controlled manner.
The RPL avoids unnecessary topology changes and communication failures. The RPL builds its topology using Objective Functions (OFs).
The IETF defines two types of RPL objective functions, Minimum Rank with Hysteresis Objective Function (MRHOF) and OF0. The first OFs accounts hop count metric, while the latter takes routing decisions as per the Expected Transmission Count (ETX) metric.
Another technique used in RPL is the Trickle algorithm, and it constructs topology such as DODAG in a controlled manner. Recent researchers mainly focus on improving the reliability and security of RPL by improving OFs and Trickle algorithm.
To increase the pervasiveness of RPL, it is crucial to understand and contribute to the design of OFs in RPL. Optimal OFs should make use of appropriate routing metrics to avoid unreliable links.
With the composite of multiple routing metrics, the IoT nodes can choose paths with a defined level of reliability. The selection of routing metrics depends on the IoT implantation needs.
Different IoT applications and their uneven deployment of sensor nodes in large areas load heavy workload on some sensor nodes, especially the nodes closer to the root node. Such unbalanced workload distribution tends to quick energy exhaustion, shortening the overall network lifetime.
Thus, an efficient scheme should be designed to balance the DODAG changes and balanced energy consumption.
The heuristic load distribution algorithm plays a crucial role in designing a multi-parent RPL routing and equalizes the network load among nodes.
The data is forwarded towards the sink node through different parent nodes at a certain probability, improves the energy balancing, and reduces the communication delay effectively.
The RPL routing metrics should be selected as per the application needs since the satisfaction of these requirements highly depends on the routing metrics. Each routing metric holds specific properties.
Without affecting their properties, they should be combined effectively during DODAG construction. Previously, different composition techniques are used, and they need further research to improve the RPL performance.
Most of the previous works focus on energy balancing and communication delay, regardless of the network traffic and congestion. However, network congestion may even tend to network lifetime reduction.
Recently, RPL researchers have focused on improving the congestion control schemes. Moreover, network traffic load balancing is relevant to the network lifetime and congestion.
There are two conflicting goals in improving RPL. First, the IoT needs to construct an efficient and reliable DODAG structure. The second aim is to maintain the structure cost-effectively.
To attain such conflicting aims, the RPL applies the Trickle algorithm, and recent researchers turn their focus on the Trickle algorithm for designing an optimal RPL for IoT applications.
Mobile portable embedded devices create the Mobile Internet of Things (IoT). Lack of mobility support in the RPL provides unreliable communications in mobile IoT applications.
To enhance the adaptability of RPL to network dynamics, the mobility-related routing metrics should be incorporated to establish long-lasting, reliable paths.
RPL is an extensible distance vector protocol, and it is the specific design for low power and lossy networks. It builds the topology structure based on certain metrics injected into the objective function (OF).
It is essential to investigate the performance of RPL in the dense and sparse network under various topologies e.g., grid, random, with/without mobility scenarios, under different network areas, and so on. The performance of RPL is examined using various metrics.
Scaling RPL to Dense and large area networks with limited battery and memory resources improves the RPL processes.
By maintaining a stable and reliable DODAG structure and appending a novel policy for managing the neighbor table, the RPL performance can be improved.
Default RPL accounts hop count, ETX, and other link reliability metrics. Under large-scale networks, RPL is affected by network congestion due to the lack of traffic load-related metrics.
Recently, Sigma-ETX has been used in RPL to address a problem in the OF with ETX. The Sum of all the ETX in the path and the standard deviation of ETX between each node improves the RPL performance.
Network congestion is the main issue that affects the RPL routing performance under IoT.
Most OF the exploits a simple parent selection mechanism and avoids the DODAG building with bad link quality, large hop count, or large ETX. However, they tend to have a ping-pong effect, i.e., frequent parent switching and poor routing performance.
To address the congestion problem between parent and child nodes and avoid the ping-pong effect on RPL, an efficient parent-change procedure using game theory strategy and multi-parent topology structure is needed.
Improving the quality of communication over unreliable links and improving packet delivery without a deadline while minimizing overhead and energy consumption using RPL is essential.
The QoS communication is achieved by extending RPL to opportunistic routing instead of single-parent based unreliable communication.
To create a load-balanced network via evenly distributed load and energy consumption among the nodes using fuzzy logic-based routing decisions.
It is essential to incorporate multiple load-related routing metrics in fuzzy logic based RPL routing protocol.
To improve the RPL performance in a scalable manner, novel routing metrics should be incorporated.
Packet Forwarding Index denotes the logarithmic product of the success rate of packet deliveries. Energy dispersion denotes the residual energy of a possible parent and the other nodes in the parent’s neighbor list and the Expected Lifetime Metric, which denotes the lifetime of RPL links.
Load balancing in IoT networks can be achieved using multiple metrics-based objective functions.
Multiple objectives can be scalable, reliable, and stable in DODAG structure construction and maintenance. The metrics related to such OFs are Hop count, energy, parent candidates count, link quality metrics, and mobility metrics.
Link lifetime is an important metric in designing smart, energy-efficient objective functions for RPL.
Link lifetime is affected by energy drain and node mobility. To reduce the impact of mobility on RPL efficiency, the mobility-related metrics should be incorporated into RPL OFs.
The lifecycle index can be used to measure efficient OFs and DODAG structure construction.
LCI metric estimation incorporates the completion cost of a packet transmission of a sender node, the average number of forwards, transmission time, and energy consumption.
Network dynamics due to node mobility and network traffic impacts the performance of RPL over unreliable RPL links.
An RPL and backpressure routing extension can effectively handle those issues due to dynamic network topology. The basic idea of backpressure-based RPL is to adaptively and smoothly switch between RPL and the backpressure routing as per the network conditions.
A dynamic Trickle algorithm needs to balance the mobility impact reduction on RPL performance and control overhead.
Doppler frequency estimates the speed of the moving node and signal strength. The enhanced Trickle algorithm enables the nodes to send a DIS message to neighboring router nodes per the dynamic topology.
Minimum degree RPL aims at constructing the minimum degree spanning tree and attempts to offer load balancing in RPL. It modifies the original tree formed by RPL to decrease its degree using hop length/hop count effectively.
It balances the energy consumption among nodes and improves the overall network lifetime.
