## Using MCS in Industry 4.0 Scenarios  
### Role of MCS in Industry 4.0  
One of the key concepts that recur when talking about Industry 4.0 is the management, transmission, analysis and storage of big amounts of data. Most of the complex processes described in the application domains of Section 3, as well as the three objectives of improving production, working conditions and customer satisfaction (see Section 4), are based on the processing and aggregation of data coming from several different sources. It is evident that data handling will be pivotal for the implementation of the industry-related complex processes.  
From the previous Sections, three main data sources can be identified:  
* assets and environmental monitoring. It is based on objective measurements performed by sensors, mainly static or with a limited mobility range;
* products and workers monitoring. It is based on objective measurements performed by sensors, mainly mobile and geolocated;
* workers and customers' feedback. It is based on the acquisition of subjective feedback, mainly mobile and geolocated.  
Most of the applications described in Section 3 require a multitude of data to be enabled, data that are later processed according to big data principles, with specific engines such as those described in [48; 49; 50]. In order to collect these different types of unstructured data coming from such heterogeneous sources, the MCS paradigm can be used. As introduced in Section 2.3, MCS includes both mobile crowdsensing and mobile crowdsourcing. Mobile crowdsensing makes use of mobile sensors to collect data from a crowd of different sources. In mobile crowdsourcing, users are required to complete a task, usually consisting of providing feedback [12]. As shown in Figure 3, MCS introduces a number of advantages in industrial applications with respect to traditional sensor networks [12; 51]: they make use of ubiquitous Internet access to provide _mobile and scalable measures_; they _do not need additional dedicated devices_ to extend the existing industrial sensing system, as they use data coming from any device, especially personal devices; they enable the acquisition of _subjective assessments_, in addition to objective measurements; they can _integrate human wisdom_ into machine intelligence; finally, industrial applications can benefit the _information-sharing and decision-making_ processes of the whole industrial society.  
Taking into account the type of data acquisition required for each industrial application analysed in Section 3, Table 1 compares MCS to traditional Industrial Sensor Networks (ISNs). MCS is based on mobile data acquisition, whether through objective acquisition made by sensors and subjective acquisition provided by users' personal devices. Therefore, while it is not always the best solution in asset utilization-related applications, it is particularly efficient, with respect to traditional ISNs, in the other applications, which are improved by mobile acquired data and subjective data. Furthermore, it enables the cost-effective acquisition of large amounts of data. This makes it not only the most efficient solution in quality control, supply chain management and workplace safety applications but also the only possible solution in some product monitoring applications, for which traditional solutions would be too complicated, expensive and slow to be implemented.