Improving network lifetime and speed for 6LoWPAN networks using machine learning

Abstract

Wireless communication networks have an inherent optimisation problem of effectively routing data between nodes. This problem is multi-objective in nature, and covers optimisation of routing speed, the network lifetime, packet delivery ratio and overall network throughput. In this paper, a machine learning (ML)-based algorithm is proposed with an objective to minimise the network delay and increase network lifetime for 6LoWPAN networks based on RPL routing. The ML-based approach is compared with normal RPL routing in order to check the performance of the system when compared to recent routing protocols. It is observed that the proposed machine learning-based approach reduces the network delay by more than 20% and improves the network lifetime by more than 25% when compared to RPL-based 6LoWPAN networks. The machine learning approach also takes into account the link quality between the nodes, thereby improving the overall QoS of the communication system by selecting paths with minimal delay, minimal energy consumption and maximum link quality.