A Weighted Broadcast Routing Algorithm for WirelessHART Networks

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

doi:10.1109/sbesc.2017.32

Cited by 10 publications

(11 citation statements)

References 8 publications

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“…Quality of service is achieved by estimating the reliability and delay using link quality information [16]. A greedy algorithm builds graphs where nodes and edges are selected through a weighted cost equation that uses the number of hops, RSL, number of neighbors, and node power source type [11]. Primary and redundant paths are chosen based on the residual energy of neighbors to balance energy consumption in [17], but the configuration of a preferred neighbor for a node is not available in the WH standard.…”

Section: B Graph Routing Algorithmsmentioning

confidence: 99%

“…The greedy algorithms in [9]- [11] are used as a baseline for QLRR-WA, where nodes and edges are iteratively added to the uplink graph and selected through a weighted cost equation.…”

Section: B Uplink Graph Constructionmentioning

confidence: 99%

“…Increasing w p will make nodes avoid choosing battery-powered nodes as successors [10]. Increasing w s will make nodes connect to successors with greater RSL, thus reducing the probability of packet transmission failures [11], [27].…”

Section: B Uplink Graph Constructionmentioning

confidence: 99%

“…Downlink graphs were scheduled first to reduce reconfiguration, because they rarely change during the simulations. We compared QLRR- WA with the following baseline uplink algorithms: Han [9], which builds graphs trying to reduce the number of hops from the gateway; ELHFR [12], which chooses edges based on the RSL of neighbors; Künzel [11], where weights were set w h = 0, w p = 1, w s = 0, w n = 0 on all related equations trying to build graphs that avoid battery-powered nodes as routers; and Künzel [10], which uses RL to adjust the weight of the power type of nodes in a cost equation, where we used α = 0.1, γ = 0.8, ε = 0.3 and s f = 0.25. We ran several repetitions of the simulation for each algorithm for each topology to get statistics of ANL, ENL, PDR, and PRN.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…During the startup of the network (from 0 to 4 hours), the ANL increased because nodes were joining the network, exchanging commands and sending process data. The ANL stabilized in the Han, ELHFR, and Künzel [11] after the join process because the topology stopped changing. Künzel [10] continued to reduce ANL as it was exploring, but it had an average performance in these simulations.…”

Section: A Simulation Setupmentioning

confidence: 99%

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

Kunzel

Indrusiak

Pereira

2020

IEEE Trans. Ind. Inf.

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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-ofthe-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 algorithms.

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Section: B Graph Routing Algorithmsmentioning

confidence: 99%

“…The greedy algorithms in [9]- [11] are used as a baseline for QLRR-WA, where nodes and edges are iteratively added to the uplink graph and selected through a weighted cost equation.…”

Section: B Uplink Graph Constructionmentioning

confidence: 99%

Section: B Uplink Graph Constructionmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

See 3 more Smart Citations

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

Kunzel

Indrusiak

Pereira

2020

IEEE Trans. Ind. Inf.

Self Cite

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An Intelligent Routing for Internet of Things Mesh Networks

Chakraborty

Das

Pradhan

2022

Trans Emerging Tel Tech

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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.

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