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Designing a context-aware model for RPL load balancing of low power and lossy networks in the internet of things - 2020

Designing A Context-Aware Model For RPL Load Balancing Of Low Power And Lossy Networks In The Internet Of Things

Research Area:  Internet of Things


The IPv6 routing protocol (RPL) for low power and lossy Networks (LLNs) was accepted as the standard routing protocol for the IoT by IETF in March 2012. Since then, it has been used for different IoT applications. Although the RPL deals considerably with IoT network requirements, there are still some open-ended problems to solve, for it was not initially designed for IoT applications. This paper addresses the RPL problems including load imbalance, which causes congestion in some nodes, significantly reduces the network performance, and decreases node energy and network lifetime. This paper proposes the automata-ant colony based multiple recursive RPL (AMRRPL), which is a modified version of the RPL for IoT networks, and uses a balancing model to avoid congestion. As a result, it will reduce network energy consumption, prolong the network lifetime, and reduce packet loss. The AMRRPL is evaluated in three steps. First, a multi-hop return objective function is presented based on the ant colony and computes the rank according to node context. The second step develops a new parent selection mechanism dynamically selected by stochastic automata and dynamic metrics for an optimal parent. General evaluation results show that this algorithm can make better decisions with regard to the optimal parent instead of making decisions simply based on the parents rank. The third step resolve bottlenecks and swarm problems by managing the moving nodes through the heuristic flabellum algorithm inspired by physical and biological behaviour of flabella in the sea. Finally, the proposed algorithm performance is evaluated through the Cooja simulator. The proposed algorithm shows significant improvements in packet delivery and network lifetime, energy and convergence.


Author(s) Name:  Zohreh Royaee,Hamid Mirvaziri,Amid Khatibi Bardsiri

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-020-02382-4

Volume Information:  volume 12, pages 2449–2468 (2021)