Weight Adjustments in a Routing Algorithm for Wireless Sensor and Actuator Networks Using Q-Learning ⁎ ⁎The authors thank the Federal Institute of Education, Science and Technology of Rio Grande do Sul - IFRS for financial support.

Kunzel, Gustavo; Cainelli, Gustavo; Müller, Ivan; Pereira, Carlos Eduardo

doi:10.1016/j.ifacol.2018.06.236

Cited by 16 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on the queue and channel state, the relay node makes decisions for packet transmission in cognitive IoT networks. In Reference 52, Q‐learning is implemented to create an overall balance between delay and network lifetime. Maddikunta et al 53 presented a moth flame optimization (MFO) model that increased the lifetime of an IoT network based on the energy consumption of the SNs.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Routing for Internet of Things Mesh Networks

Chakraborty

Das

Pradhan

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Internet of Things (IoT) is gaining popularity due to its complex network architecture, formed by the tremendous connection of objects. Sensors used in different IoT applications are installed in unfavorable terrains and conditions. Since each sensor node can sense, compute, and promote wireless communication, a novel intelligent routing algorithm is required, as the traditional ones do not fulfill the current network requirements. Reinforcement learning models can help overcome the wireless network's challenges faced during routing due to its dynamicity by selecting and adapting weights that optimize the paths based on the requirement of the applications and operating conditions. In this article, a routing agent with Q‐learning is proposed that adjusts the routing policy of a network based on local information to converge toward an optimal solution by maintaining the overall balance between latency and the network's lifetime. A reward is given to an agent that increases the network lifetime and reduces the average network latency. The evaluation of the proposed model was done using network simulators (NS‐2) on different network scenarios that showed improved results in terms of network lifetime compared to centralized minimum angle and distributed minimum angle.

show abstract

Section: Literature Reviewmentioning

confidence: 99%