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The adoption of dense wireless sensor networks in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction. These are needed to certify the quality (or reliability) of the network information flow in industrial scenarios which are typically characterized by harsh propagation conditions. Connectivity prediction must account for the possible coexistence of heterogeneous radio-access technologies, as part of the Industrial Internet of Things (IIoT) paradigm, and easily allow postlayout validation steps. The goal of this paper is to provide a practical evaluation of relevant coexistence problems that may occur between industrial networks employing standards such as WirelessHART IEC 62591, IEEE 802.15.4, and IEEE 802.11. A number of coexistence scenarios are experimentally tested using different radio platforms. For each case, experimental results are analyzed to assess tolerable interference levels and sensitivity thresholds for different configurations of channel overlapping. Finally, the problem of over-the-air spectrum sensing is investigated in real scenarios with heterogeneous industrial networks to enable a cognitive resource allocation that avoids intolerable interference conditions.

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Reliability and robustness are critical parameters in choosing a wireless protocol to be used in industry. Nowadays, WirelessHART is the most adopted wireless industrial protocol. However, there are only a few number of analysis tools for industrial networks, specially for WirelesHART, which implies in a lack of information about the health network to its end users. In this paper a software to obtain network data from a WirelessHART network is presented. The developed software allows to identify relevant issues to the verification and maintenance of WirelessHART networks. One of the advantages is the ability to customize the tests, being allowed to evaluate data of most interest for a particular purpose. In a case study, the obtained data allowed to identify the mostly used routes of the network which lead to the identification of bottlenecks.