Abstract

Abstract
This article presents the contribution of the Polytechnic Institute of Castelo Branco (IPCB) within the scope of PPS1 of the PRODUTECH SIF program - Solutions for the Future Industry, in terms of the definition of modules for scalability, adaptation, plug-and-play, with interoperability between processes and technologies inter/intra industrial plants. More specifically, its contribution for the definition, implementation and evaluation of OPC- UA device discovery services. Adopting the CompactRIO platform developed on PPS2 as a physical basis for a Smartbox, its development was extended during the present task with a view to incorporating and validating the adopted service discovery mechanisms in the program.

Abstract
Constant search for efficiency and productivity has led to innovation on the factory shop floor, representing an evolution of the current production systems combined with new technologies of industrial automation and information technology. This work presents a versatile gateway for experimental demonstration of Industrial IoT technologies in a loom machine, allowing sensing, monitoring and data acquisition that was not originally available. We have implemented an approach, based on the OPC UA communication protocol for real time applications, and OPC UA to MQTT conversion mechanism. Raspberry Pi's platform act as an OPC UA server. From the measurements, data stored in a cloud server can be accessed remotely with improved security and visualized from a computer dashboard. One of the conclusions that can be drawn is that the proposed gateway allows data to be stored and easily monitored from a smartphone application or a computer web interface. © 2020 Kassel University Press GmbH.

Abstract
The presence of Industrial IoT systems on the factory shop floor in recent years, are becoming an attractive solution with many advantages, including flexibility, low cost and easy deployment. As more and more devices are wirelessly connected, spectral noise level increases and consequently radio interference between IoT devices. In this paper, we present an agnostic methodology to assess radio interferences between different industrial IoT systems on the factory floor, using appropriate propagation models. Several interference scenarios are simulated, ranging from legacy systems to future communication standards implementations (5G). We highlight some of the challenges and open issues that still need to be addressed to decrease interference and make industrial wireless systems compatible. © 2020 IEEE.

Abstract
Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard-LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRaWAN networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SOTA), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Abstract
Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit -rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5% and 2% of DER, and a number of collisions 11 and 2.5 times smaller than equal-distribution, and random distribution, respectively.