Paper Info
Title
Improved Trickle Algorithm Toward Low Power and Better Route for the RPL Routing Protocol
Abstract
The IPv6 Routing Protocol for Low Power and Lossy networks (RPL) is a routing protocol standardized by the Internet Engineering Task Force. According to the specification of the protocol, the Trickle algorithm is adopted for the dissemination of route construction information among nodes in such networks. Since the algorithm is originally designed for code propagation and maintenance in Wireless Sensor Networks, when using the algorithm for the route formation of the network there exist some problems, such as the route convergence time, the fairness issue among nodes, and the amount of power consumption. Therefore, the paper proposes an improved Trickle algorithm, namely FI-Trickle, by taking a new approach to simultaneously reduce the unfairness among nodes and the power consumption and to improve the packet delivery ratio of the network. The performance of FI-Trickle is verified via simulation with extensive experiments over various network sizes, interference conditions, and network topologies. For comparison purposes, the extensive experiments are applied not only to FI-Trickle, but also to various Trickle variants such as Trickle, Trickle-F, I-Trickle, and Drizzle. The simulation results show that FI-Trickle can use less power to achieve a similar fairness level as compared to Trickle-F. Also, in terms of the packet delivery ratio, as the network size grows, FI-Trickle can increasingly outperform Trickle-F, Trickle, Drizzle, and I-Trickle by up to 1%, 3%, 3%, and 4%, respectively. This result implies that FI-Trickle can be a better Trickle candidate for the dissemination of RPL messages for large-scale networks.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3196693
IEEE ACCESS
Keywords
DocType
Volume
Routing protocols, Power demand, Convergence, Redundancy, Wireless sensor networks, Task analysis, Simulation, Low-power and lossy networks (LLNs), power consumption, packet delivery ratio, RPL, trickle algorithm
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Ssu-Ting Liu100.34
Sheng-De Wang272068.13