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Latency and Lifetime Enhancements in IWSN: a Q-Learning Approach for Graph Routing

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Publication details

JournalIndustrial Informatics, IEEE Transactions on
DateAccepted/In press - 9 Sep 2019
Original languageEnglish


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 the communications
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. 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

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