Increasing the reliability of multihop industrial wireless sensor networks (IWSNs) is an important challenge to enable continuous data collection in industrial processes. One approach for increasing reliability is multiconnectivity (MC), which enables simultaneous data transmission through two independent network links. In practice, MC can be implemented on all OSI layers, resulting in different parts of the network stack being duplicated. In this article, we compare the implementation of MC above the MAC layer and above the network layer, corresponding to an implementation on a per-hop basis or an end-to-end basis in multihop networks. For a comprehensive consideration, the approaches are discussed analytically, simulated and deployed in a real-world scenario to provide foundational knowledge about the effects of different layer MC on reliability. We show that the per-hop approach offers higher reliability than the end-to-end approach in homogeneous networks, while both approaches outperform the single-connectivity baseline. In comparison, the end-to-end approach outperforms the per-hop approach and the baseline depending on the network topology in heterogeneous networks. Apart from the topology and the link quality, we reveal the influence of fragmentation on both approaches. All influencing factors, in combination with external factors introduced by a real-world deployment, led to a decreased data loss rate of up to 56% compared to the single-connectivity baseline.

In this work, we present the design, usage and evaluation of Industrial Wireless Sensor Networks (IWSNs) to enable the demand-driven operation of HVAC systems. We present a wireless sensor node in combination with a low-cost 3D airflow sensor, which uses differential pressure readings to calculate the airflow. We show how multi-connectivity can be used to increase the reliability of the data transmission between the sensor nodes and, therefore, create a reliable monitoring system for environmental conditions inside factories. Finally, we show how the collected data can be used to implement the automated and demand-driven operation of HVAC systems.

In industry 4.0 many industrial processes are enhanced by using the information continuously collected through connected sensors. While many use cases have been discussed and simulated, the transfer into real-world deployments and the discussion of associated challenges have been rather sparse.In this field report, we present our experiences on developing and deploying a versatile Wireless Sensor Network (WSN) for the monitoring of temperature, particle concentration, CO2concentration and other important environmental data. The WSN is accompanied by its backend and testbed infrastructure, which allows the continuous management and monitoring of the deployments. We describe the challenges faced and experiences gained during the deployment of 70 nodes at five different industrial scenarios and recap the lessons we learned.

Smart Farming has the goal to support farmers in their various activities, i.e., achieve best results in quality and quantity of their products, with low and efficient resource usage, working in sustainable manners etc. For that, a lot of information is needed, e.g., about the current state of the monitored agricultural area. Typically this requires a large number of sensors which collect important sensor data at predefined intervals. For further evaluation such data is usually processed in a central place, for which it must first be transferred from the field to a data sink at the farm premises. Cellular networks could be used for such data transmissions, but the network deployment in rural areas is costly with less likely revenues. Thus, mobile operators are not eager to roll out their networks in rural areas so that they are often not well covered. In this paper we introduce PotatoScanner, a mobile Tolerant Wireless Sensor Node (DTWSN) that closes this gap and acts with the help of a field sprayer as a data mule for the collected data. In addition, PotatoScanner itself has sensors to obtain further information about the cultivated field. The paper provides an overview of the overall system and analyses the measurement data obtained in real operation over the past two years 2017 and 2018.