Nowadays, governments and companies are looking for solutions to increase the collection level of various waste types by using new technologies and devices such as smart sensors, Internet of Things (IoT), cloud platforms etc. In order to fulfil this need, this paper presents solutions provided by a research project involving the design, development and implementation of fully automated waste collection systems with an increased usage degree, productivity and storage capacity. The paper will focus on the main results of this research project in turning the automated waste collection system into a smart system so that it can be easily integrated in any smart city infrastructure. For this purpose, the Internet of Things platform for the automated waste collection system provided by the project will allow real time monitoring and communication with central systems. Details about each module are sent to the central systems: various modules' statuses (working, blocked, needs repairs or maintenance etc.); equipment status; storage systems status (allowing full reports for all waste types); the amount of waste for each module, allowing optimal discharging; route optimization for waste discharging etc. To do that, we describe here an IoT cloud solution integrating device connection, data processing, analytics and management.

Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them to cloud applications that allow real-time data monitoring and processing. Securing and protecting the transmitted data represents a big issue to be discussed and resolved. In the paper, we propose a new method of data encoding and encryption for cloud applications using PNG format images. The proposed method is described in comparison with one of the classical methods of data encoding and transmission used currently. The paper includes a case study in which the proposed method was used to collect and transmit data from an automated waste collection system. The results show that the proposed method represents a secure, fast and efficient way to send and store the data in the cloud compared to the methods currently used. The proposed method is not limited to being used only in waste management but can be used successfully for any type of manufacturing system from smart factories.Sustainability 2020, 12, 1839 2 of 15 the production of the physical object, because operating a smart, connected manufacturing device requires a supporting cloud-based system [6]. The digital twin of the manufacturing architecture is related with all the above-mentioned technologies used in this paradigm.Big data is one of the nine pillars involved in achieving Industry 4.0. A huge amount of data are available in manufacturing systems, waste management systems, etc., but the data and information should be properly collected, encoded and transmitted to the cloud application in order to be processed and to allow a real-time monitoring of equipment and systems. The major challenges are represented by: data acquisition from smart sensors, actuators and PLCs; data conversion; data security and privacy; data encoding, encryption and decryption [7][8][9][10].Software architectures of the big data processing platforms are analyzed in the literature, including solutions for job scheduling for big data applications [11], solutions which offer a flexible co-programming architecture able to support the life cycle of time-critical cloud native applications [12], mobility-driven cloud-fog-edge collaborative real-time framework solution, which has IoT, Edge, Fog and Cloud layers and which exploits the mobility dynamics of the moving agent [13].Several data encoding and encryption solutions for cloud applications are presented in the literature [14][15][16][17][18][19][20][21][22], but without including detailed case studies to present identified problems, propose solutions, and implement results.The main problem is the identification of the right solution that will ensure the data transmission and processing in a secure way, quickly, efficiently and with low costs. The proposed solution presented in Chapter 2 can be used both in smart factories and for smart city applications incl...

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By proposing an optimization model for a new automated liquid penetrant inspection (LPI) system in order to increase its productivity, the paper tries to identify the best algorithm to solve this case study. The architecture of the system is dictated by the successive stages of the inspection process and the available conditions in the work shop. As a novelty in the field, the authors developed such a fully automated LPI system for inspecting different parts, which eliminates the need of the visual inspection made by operator, using instead dedicated software solution for processing the digital images of the inspected parts and for giving the pass/fail verdict. In the present case study, the attention was focused on optimizing the new LPI system architecture. Simulations in different working scenarios are run with the purpose to increase productivity by optimizing the critical waiting times within the system and by establishing the best order for inspecting parts belonging to three families subjected to LPI method. Moreover, the results of the simulation are used for programming the system by setting the optimal values of the functional parameters of system's equipment in order to avoid running a large number of tests which are expensive and time consuming.

The main goal of the paper is to present how CAD and the simulation results of a virtual model were used to develop and adapt an educational platform to various manufacturing scenarios in an Industrial Logistics laboratory optimizing the performances in terms of productivity. Our paper is divided in two parts. The first part describes the design and development of an educational platform containing an AS/RS (automated storage/retrieval system) system and a RGV (Rail-Guided Vehicle). All the phases of the platform development are presented, starting with 3D modelling, and ending with the platform testing and its integration in a manufacturing cell. The second part demonstrates the platform performance diagnosis and optimization in different functional scenarios using material flow simulation. The problems that occurred (when the platform becomes part of different types of manufacturing architectures) are analysed using the simulation reports diagnosis and a new simulation validates the optimization solutions.

The first part of the article presents fundamentals of the Material Flow Theory (MFT) centered on five major topics: material flow and generic associate architecture definitions and classifications; major characteristics of discrete, continuous and hybrid material flow; mathematical and virtual modeling of material flow; material flow simulating algorithms (including a general algorithm for optimizing the associated architecture determining the flow trajectory), as well as major MFT applications in different fields. In the second part, the article focuses on the specific applications of MFT in industrial engineering material flow management (MFM). The accent is put on exploring different possibilities to increase productivity and profit in manufacturing architectures using the MFM approach based on virtual modeling and simulation. The proposed main topics of this section are: definition and characteristics of diffused and concentrated manufacturing architectures, virtual modeling of the manufacturing architecture structural elements, MFM simulation algorithms, diagnosis and optimization for manufacturing architectures in industrial engineering using MFM.

The main problem treated in this paper is that if we want to optimize a network of manufacturing systems design using discrete material flow management we need a new algorithm different from the one used for a single manufacturing system. For a single manufacturing system we usually use discrete material flow simulation to identify and eliminate bottlenecks where the flow is slowed down or blocked in order to increase the productivity. For a network of manufacturing systems material flow concentrators could be the bottlenecks found in one of the manufacturing systems using this classical discrete material flow simulation but it also could be a new different one. We focus in this paper on the algorithm we propose to solve this problem of identifying and eliminating not the bottlenecks of each manufacturing system but of the entire network. A case study of multipolar synchronous simulation (as we named our proposed algorithm) is presented in order to illustrate across a tree nodes manufacturing network how this new algorithm works.

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