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Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing

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JournalIndustrial Informatics, IEEE Transactions on
DateAccepted/In press - 9 Sep 2019
DateE-pub ahead of print - 16 Sep 2019
DatePublished (current) - Aug 2020
Issue number8
Volume16
Pages (from-to)5617-5625
Early online date16/09/19
Original languageEnglish

Abstract

Industrial wireless sensor networks usually have a centralized management approach, where a device known as network manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms.

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