Full Text:
<5130>
Summary: 
<1074>
CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-11-08
Cited: 0
Clicked: 5801
Citations: Bibtex RefMan EndNote GB/T7714
A novel context-aware RPL algorithm based on a triangle module operator
| ||||||||||||||
Yanan Cao, Hao Yuan. A novel context-aware RPL algorithm based on a triangle module operator[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1583-1597.
@article{title="A novel context-aware RPL algorithm based on a triangle module operator",
author="Yanan Cao, Hao Yuan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="12",
pages="1583-1597",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000658"
}
%0 Journal Article
%T A novel context-aware RPL algorithm based on a triangle module operator
%A Yanan Cao
%A Hao Yuan
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 12
%P 1583-1597
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000658
TY - JOUR
T1 - A novel context-aware RPL algorithm based on a triangle module operator
A1 - Yanan Cao
A1 - Hao Yuan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 12
SP - 1583
EP - 1597
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000658
Abstract: For the use in low-power and lossy networks (LLNs) under complex and harsh communication conditions, the routing protocol for LLNs (RPL) standardized by the Internet Engineering Task Force is specially designed. To improve the performance of LLNs, we propose a novel context-aware RPL algorithm based on a triangle module operator (CAR-TMO). A novel composite context-aware routing metric (CA-RM) is designed, which synchronously evaluates the residual energy index, buffer occupancy ratio of a node, expected transmission count (ETX), delay, and hop count from a candidate parent to the root. CA-RM considers the residual energy index and buffer occupancy ratio of the candidate parent and its preferred parent in a recursive manner to reduce the effect of upstream parents, since farther paths are considered. CA-RM comprehensively uses the sum, mean, and standard deviation values of ETX and delay of links in a path to ensure a better performance. Moreover, in CAR-TMO, the membership function of each routing metric is designed. Then, a comprehensive membership function is constructed based on a triangle module operator, the membership function of each routing metric, and a comprehensive context-aware objective function. A novel mechanism for calculating the node rank and the mechanisms for preferred parent selection are proposed. Finally, theoretical analysis and simulation results show that CAR-TMO outperforms several state-of-the-art RPL algorithms in terms of the packet delivery ratio and energy efficiency.
[1]Alishahi M, Moghaddam MHY, Pourreza HR, 2018. Multi-class routing protocol using virtualization and SDN-enabled architecture for smart grid. Peer-to-Peer Netw Appl, 11(3):380-396.
[2]Al-Kashoash HAA, Al-Nidawi Y, Kemp AH, 2016. Congestion-aware RPL for 6L0WPAN networks. Proc Wireless Telecommunications Symp, p.1-6.
[3]Araújo HDS, Filho RH, Rodrigues JJPC, et al., 2018. A proposal for IoT dynamic routes selection based on contextual information. Sensors, 18(2):353.
[4]Bhandari KS, Hosen ASMS, Cho GH, 2018. CoAR: congestion-aware routing protocol for low power and lossy networks for IoT applications. Sensors, 18(11):3838.
[5]Cao YN, Wu MQ, 2018. RPL based on triangle module operator for AMI networks. China Commun, 15(5):162-172.
[6]Ganesh DR, Patil KK, Suresh L, 2019. Q-FRPML: QoS-centric fault-resilient routing protocol for mobile-WSN based low power lossy networks. Wirel Pers Commun, 105(1):267-292.
[7]Gao L, Zheng ZW, Huo MM, 2018. Improvement of RPL protocol algorithm for smart grid. Proc IEEE 18th Int Conf on Communication Technology, p.927-930.
[8]Gnawali O, Levis P, 2012. The minimum rank with hysteresis objective function. Proc Internet Engineering Task Force, RFC 6719.
[9]Hassan A, Alshomrani S, Altalhi A, et al., 2016. Improved routing metrics for energy constrained interconnected devices in low-power and lossy networks. J Commun Netw, 18(3):327-332.
[10]Huynh TT, Lin CM, Le TL, et al., 2020. A new self-organizing fuzzy cerebellar model articulation controller for uncertain nonlinear systems using overlapped Gaussian membership functions. IEEE Trans Ind Electron, 67(11):9671-9682.
[11]Karkazis P, Trakadas P, Leligou HC, et al., 2013. Evaluating routing metric composition approaches for QoS differentiation in low power and lossy networks. Wirel Netw, 19(6):1269-1284.
[12]Kheaksong A, Srisomboon K, Prayote A, et al., 2018. Multi-criteria parent selection using cognitive radio for RPL in smart grid network. Wirel Commun Mob Comput, 2018: 9590576.
[13]Kim HS, Kim H, Paek J, et al., 2017. Load balancing under heavy traffic in RPL routing protocol for low power and lossy networks. IEEE Trans Mob Comput, 16(4):964-979.
[14]Lamaazi H, Benamar N, 2018. OF-EC: a novel energy consumption aware objective function for RPL based on fuzzy logic. J Netw Comput Appl, 117:42-58.
[15]Memon RA, Li JP, Ahmed J, et al., 2020. Cloud-based vs. blockchain-based IoT: a comparative survey and way forward. Front Inform Technol Electron Eng, 21(4):563-586.
[16]Monowar MM, Basheri M, 2020. Corrigendum to “on providing differentiated service exploiting multi-instance RPL for industrial low-power and lossy networks”. Wirel Commun Mob Comput, 2020:2896561.
[17]Nassar J, Berthomé M, Dubrulle J, et al., 2018. Multiple instances QoS routing in RPL: application to smart grids. Sensors, 18(8):2472.
[18]Nayak P, Devulapalli A, 2016. A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sens J, 16(1):137-144.
[19]Nikolić M, Šelmić M, Macura D, et al., 2020. Bee colony optimization metaheuristic for fuzzy membership functions tuning. Exp Syst Appl, 158:113601.
[20]Pereira H, Moritz GL, Souza RD, et al., 2020. Increased network lifetime and load balancing based on network interface average power metric for RPL. IEEE Access, 8:48686-48696.
[21]Sanmartin P, Rojas A, Fernandez L, et al., 2018. Sigma routing metric for RPL protocol. Sensors, 18(4):1277.
[22]Seyfollahi A, Ghaffari A, 2020. A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks. Comput Netw, 179: 107368.
[23]Solapure SS, Kenchannavar HH, 2020. Design and analysis of RPL objective functions using variant routing metrics for IoT applications. Wirel Netw, 26(6):4637-4656.
[24]Taghizadeh S, Bobarshad H, Elbiaze H, 2018. CLRPL: context-aware and load balancing RPL for IoT networks under heavy and highly dynamic load. IEEE Access, 6:23277-23291.
[25]Thubert P, 2012. Objective function zero for the routing protocol for low-power and lossy networks (RPL). Proc Internet Engineering Task Force, RFC 6552.
[26]Velivasaki THN, Karkazis P, Zahariadis TV, et al., 2014. Trust-aware and link-reliable routing metric composition for wireless sensor networks. Trans Emerg Telecommun Technol, 25(5):539-554.
[27]Wadhaj I, Ghaleb B, Thomson C, et al., 2020. Mitigation mechanisms against the DAO attack on the routing protocol for low power and lossy networks (RPL). IEEE Access, 8:43665-43675.
[28]Winter T, Thubert P, Brandt A, et al., 2012. RPL: IPv6 routing protocol for low-power and lossy networks. Proc Internet Engineering Task Force, RFC 6550.
[29]Zahariadis T, Trakadas P, 2012. Design guidelines for routing metrics composition in LLN. Internet Engineering Task Force, Draft. http://www.ietf.org/1id-abstracts.html
ORCID: 
Open peer comments: Debate/Discuss/Question/Opinion
<1>