Research Area:  Internet of Things
Wireless sensor networks (WSNs) are very important to realize the Internet of Things by connecting physical objects to the Internet. RPL (Routing Protocol for Low Power and Lossy Networks) has been provided by the IETF (Internet Engineering Task Force) for routing on wireless sensor networks. RPL uses the Trickle algorithm to schedule the transmission of control messages. The environment of WSNs is often highly variable and the environmental conditions for nodes are different. If network nodes are equipped with a learning automaton, network convergence time can be reduced. The use of learning automata allows each network node to adjust its parameters to environmental conditions by receiving environmental feedback to perform better. The main aim of this article is to reduce the network convergence time. We equipped the Trickle algorithm with a learning automaton, which determines how many times the algorithm is repeated with the minimum interval to resolve the inconsistency, based on environmental conditions. Since repeating the Trickle algorithm with the minimum interval increases the local repair speed and reduces the network convergence time. We simulated a large number of networks of different sizes and densities. According to the simulation results, we observed that in the proposed method, the network convergence time was reduced compared with the other methods. Also, due to fewer changes in network topology, energy consumption for network reconstruction was reduced.
Author(s) Name:  Abdollah Aghaei, Javad Akbari Torkestani, Hamidreza Kermajani, Abbas Karimi
Journal name:  Computer Networks
Publisher name:  ELSEVIER
Volume Information:  Volume 196, 4 September 2021, 108241