Open AccessMulti-constraints based RPL objective function with adaptive stability for high traffic IoT applications
Author(s) -
Abdelhadi Eloudrhiri Hassani,
Aïcha Sahel,
Abdelmajid Badri,
Ilham El Mourabit
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v22.i1.pp407-418
Subject(s) - computer science , routing protocol , computer network , ipv6 , quality of service , network packet , network congestion , energy consumption , the internet , engineering , world wide web , electrical engineering
The internet of things technology is classified as a Low power and lossy network. These kinds of networks require a trustworthy routing protocol considered as the backbone for management and high quality of service achievements. IPv6 routing protocol for Low power and lossy network (RPL) was able to gain popularity compared to other routing protocols dedicated to IoT for its great flexibility through the objective function. Default objective functions implemented in the RPL core are based on a single metric. Consequently, the routing protocol can’t cope with different constraints and show congestion issues in high traffics. For that, we proposed in our paper multi-constraints-based objective function with adaptive stability (MCAS-OF), which uses novel strategies for Radio strength indicator, node energy consumption, hop count and a designed work-metric combination, new rank processing, and parent selection procedure. The network stability was also taken into account, since the multi constraints can lead to frequent parent changes, using an adaptive threshold. The proposal, evaluated under the COOJA emulator against standard-RPL and EC-OF, showed a packet delivery ratio improvement by 24% in high traffics, a decrease in the power consumption close to 44%, achieved less latency and DIO control messages, it also gives a good workload balancing by reducing the standard deviation of node’s power consumption.