Latest revision as of 23:09, 1 February 2021

Abstract

In Low Power and Lossy Networks (LLNs) sensor nodes are deployed in various traffic load conditions such as, regular and heavy traffic load. The adoption of Internet-of-Things enabled devices in the form of wearables and ubiquitous sensors and actuators has demanded LLNs to handle burst traffic load, which is an event required by myriad IoT devices in a shared LLN. In the large events, burst traffic load requires a new radical approach of load balancing, this scenario causes congestion increases and packet drops relatively when frequent traffic burst load rises in comparison with regular and heavy loads. In this paper, we introduced a new efficient load balance mechanism for traffic congestion in IPv6 Routing Protocol for Low Power and Lossy Network (RPL). To measure the communication quality and optimize the lifetime of the network, we have chosen packet delivery ratio (PDR) and power consumption (PC) as our metrics. We proposed a traffic-aware metric that utilizes ETX and parent count metrics (ETXPC), where communication quality for LLNs with RPL routing protocol are playing an important role in traffic engineering. In addition, we provided analytical results to quantify the impact of Minimum Rank with Hysteresis Objective on Function (MRHOF) and Objective Function zero (OF0) to the packet delivery, reliability and power consumption in LLNs. The simulation results pragmatically show that the proposed load balancing approach has increased packet delivery ratio with less power consumption.

Original document

The different versions of the original document can be found in:

• https://myresearchspace.uws.ac.uk/ws/files/11133293/2019_02_04_Altwassi_RPL.pdf

• http://xplorestaging.ieee.org/ielx7/8627909/8631511/08631520.pdf?arnumber=8631520,

http://dx.doi.org/10.1109/skima.2018.8631520

• https://research-portal.uws.ac.uk/en/publications/the-rpl-load-balancing-in-iot-network-with-burst-traffic-scenario,

https://doi.org/10.1109/SKIMA.2018.8631520,

https://dblp.uni-trier.de/db/conf/skima/skima2018.html#AltwassiPDG18,

https://academic.microsoft.com/#/detail/2912333953