Congestion aware and stability based routing with cross-layer optimization for 6LowPAN

Abstract

Zero Touch network requires no human involvement other than high level implementation and declarative independent intents. Machine and human interaction learning help machines to achieve the human objective efficiently and enhances the security domain. At the different places of the network, computation and intelligence decisions with cloud environment will enhance the performance of the network. Scalability and heterogeneity in IoT framework are important parameters for performance of 6LoWPAN network. Protocol stack of NGN is a key part of protocol for supporting limited processing capabilities, low memory and limited power constrained power supply devices to Internet. Heavy network traffic causes congestion in the network which degrades the network performance and influences the QoS aspects goodput, throughput, E2E delay, jitter, Energy consumption, reliability and latency. Numerous congestion control heuristics are discussed based on traffic modeling, queue management and hybrid algorithms. This paper represents the queue management in the heterogeneous ad hoc network. Impact of minimum and maximum value of queue size on goodput and with respect of packet size is discussed in this paper. Throughput, delay and Jitter for different objective functions are compared and burst rate in case of 6LoWPAN is represented. For IoT application requirements, a novel traffic modeling based on congestion for future work is summarized.