An adaptive learning approach for fault-tolerant routing in Internet of Things

Internet of Things (IOT) is a wireless ad-hoc network of everyday objects collaborating and cooperating with one other in order to accomplish some shared objectives. The envisioned high degrees of association of humans with IOT nodes require equally high degrees of reliability of the network. In order to render this reliability to IOT networks, it is necessary to make them tolerant to faults. In this paper, we propose mixed cross-layered and learning automata (LA)-based fault-tolerant routing protocol for IOTs, which assures successful delivery of packets even in the presence of faults between a pair of source and destination nodes. As this work concerns IOT, the algorithm designed should be highly scalable and should be able to deliver high degrees of performance in a heterogeneous environment. The LA and cross-layer concepts adopted in the proposed approach endow this flexibility to the algorithm so that the same standard can be used across the network. It dynamically adopts itself to the changing environment and, hence, chooses the optimal action. Since energy is a major concern in IOTs, the algorithm performs energy-aware fault-tolerant routing. To save on energy, all the nodes lying in the unused path are put to sleep. Again this sleep scheduling is dynamic and adaptive. The simulation results of the proposed strategy shows an increase in the overall energy-efficiency of the network and decrease in overhead, as compared to the existing protocols we have considered as benchmarks in this study.