Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL

Abstract

In dynamic network conditions, most routing protocols periodically send additional control messages, which allow the network to quickly adjust itself to topological changes and keep the routing table up to date. In resource‐constrained devices, these communication overheads should be controlled in order to conserve battery. In this study, the authors focused mainly on routing protocol for low power and lossy network (RPL), IPv6 RPL networks, which is fully compatible with limited resources and Internet of things expectations. They proposed a dynamic method in order to extract the most suitable value for the frequency of executing the objective function which are rules governing how to construct a network graph. In this method, first nodal data is collected and aggregated along the path to the sink, and then a novel metric is used to determine the degree of the environmental changes and control the signalling overhead in RPL routing protocol. They evaluated their scheme with the Contiki operating system and Cooja simulator. They aimed to decrease the packet loss ratio, routing overhead and energy consumption. The simulation results showed the effectiveness of the proposed method.

Keywords

• adaptive timing model
• data aggregation
• Internet of things networks
• dynamic network conditions
• control messages
• resource‐constrained devices