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.
Deployment of Wireless Sensor Networks in realworld control and monitoring applications may reveal performance problems caused by several factors. To track down such problems, it is necessary to inspect the conditions of network and nodes after deployment. In this paper, we discuss a monitoring software architecture for inspection of WirelessHART networks. Capture of information is done in a passive way using sniffers deployed in the network area. Messages are decoded, decrypted, filtered and then visualized in a graphic interface. A WirelessHART network was deployed in a laboratory for performance evaluation using the developed tool.
Abstract. Wireless communication networks have received strong interest for applications in industrial environments. The use of wireless networks in automation systems introduces stringent requirements regarding real-time communication, reliability and security. The WirelessHART protocol aims to meet these requirements. In this protocol, a device known as Network Manager is responsible for the entire network configuration, including route definition and resource allocation for the communications. The route definition is a complex process, due to wireless networks characteristics, limited resources of devices and stringent application requirements. This work presents a tool that enables the evaluation of the topology and routes used in operational WirelessHART networks. By capturing packets at the physical layer, information of operating conditions is obtained, where anomalies in network topology and routes can be identified. In the case study, a WirelessHART network was deployed in a laboratory, and by the developed tool, important information about the network conditions was obtained, such as topology, routes, neighbors, superframes and links configured among devices.
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