A Wireless Sensors Network for Monitoring the Carasau Bread Manufacturing Process

Abstract

This work copes with the design and implementation of a wireless sensors network architecture to automatically and continuously monitor, for the first time, the manufacturing process of Sardinian Carasau bread. The case of a traditional bakery company facing the challenge of the Food-Industry 4.0 competitiveness is investigated. The process was analyzed to identify the most relevant variables to be monitored during the product manufacturing. Then, a heterogeneous, multi-tier wireless sensors network was designed and realized to allow the real-time control and the data collection during the critical steps of dough production, sheeting, cutting and leavening. Commercial on-the-shelf and cost-effective integrated electronics were employed, making the proposed approach of interest for many practical cases. Finally, a user-friendly interface was provided to enhance the understanding, control and to favor the process monitoring. With the wireless senors network (WSN) we designed, it is possible to monitor environmental parameters (temperature, relative humidity, gas concentrations); cinematic quantities of the belts; and, through a dedicated image processing system, the morphological characteristics of the bread before the baking. The functioning of the WSN was demonstrated and a statistical analysis was performed on the variables monitored during different seasons. offered by Internet-of-Things (IoT) solutions to meet sustainability (or waste reduction) criteria [2]. The food industry is managing critical changes related to consumer needs, to health and safety concerns and to the demand of food products which should be differentiated and high-quality [5]. However, the quality of these products can change suddenly during the production process; thus, leading to the need for ad hoc, reliable and real-time strategies to monitor the manufacturing process [5,6]. Therefore, to satisfy customer demands, the digital monitoring of the supply chain is required to provide a deep knowledge of the crucial production steps, in order to detect the weaknesses of and permit the optimization of the whole process, to reduce the maintenance difficulties and lower the costs [5,6]. Moreover, this digitalization trend in food industry can favor the automatic data collection, the lowering of paperwork and the enabling the development of real-time, robust feedback strategies [2]. Furthermore, the challenge of ensuring acceptable adoption costs of new information and communication technologies (ICT) by small and medium size activities calls for a reasonable and effective answers [4].As a solution to these problems, the use of wireless senors networks (WSNs) was proposed [4]. WSNs are recognized as a relevant technology of the 21st century. A WSN can be defined as a low-cost platform which connects large networks of sensors [7][8][9]. They are systems which comprise radio-frequency (RF) transceivers, sensors, micro-controller or processor and power sources [5,6]. WSNs are a novel and interesting manifestation of the IoT technology [10][11][12]....

Robust Estimation of Carbon Monoxide Measurements

“…Likewise, it was also observed that the lowest values are reached in the third quarter of the year and that the highest values occur in the second and fourth quarters of the year. These results are in agreement with the general comments made in [ 28 ] about the CO concentration in Quito.…”

“…With the purpose of conducting the above-mentioned study, nonparametric tests such as the Kruskal-Wallis test and the Mann-Whitney U test [ 20 , 21 ] were used [ 19 ]. Other examples of recent publications in which nonparametric tests have been used to analyze measurements of air pollution variables can be found in [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques

“…Nonparametric statistical inference tools have also been used in the analysis of air pollution variables [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. This has been important, because if the data do not follow parametric distributions, then classical statistical inference is unfounded, which is the case of study in this research.…”

“…This has been important, because if the data do not follow parametric distributions, then classical statistical inference is unfounded, which is the case of study in this research. In this sense, in [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] nonparametric analysis tools have been used to study several variables of air pollution. Nevertheless, nonparametric analysis is much more sensitive to the influence of extreme values than robust analysis.…”

“…Finally, it is important to mention that nonparametric statistical methods have also been used to estimate the behavior of air pollution variables [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. However, the results obtained using nonparametric methods are inferior to those obtained using robust methods, in the sense that robust methods are practically immune to the influence of extreme values.…”