Optimizing network lifetime and QoS in 6LoWPANs using deep neural networks

Abstract

Internet Protocol version 6 (IPv6) over low power wireless personal area networks (6LoWPANs) forms a majority of traffic share in Internet of things (IoT) where quality of service (QoS) becomes obligatory for multitude of sensor inputs. 6LoWPANs are interference prone due to the fact that the data link and physical layers utilize the IEEE 802.15.4 standard for communication. Interference in 6LoWPANs results in poor QoS in terms of packet reception ratios and packet loss rates and also in poor network stability and reduced network lifetime. A deep neural network based routing algorithm is proposed which offers multiple solutions to the interference problem and selects the best solution in order to reduce interference. The proposed routing algorithm improves the network lifetime, delay and jitter on an average by 50%, 40%, and 25% respectively compared to the standard 6LoWPAN routing protocol. The signal to interference and noise ratio is also improved on an average by 18 decibel.