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.

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Periodontal disease is progressive and episodic in nature, with tissue destruction resulting from the host response to bacterial antigens and irritants.This study aimed to evaluate the genotype of a certain group of pathogen agents. In order to to this we used a total of 45 patients with PAG included in the epidemiological analyzes with a mean age of 34.7 Four samples were collected from the subgingival plaque from each patient, a total of 224 samples of dental plaques were investigated. All samples were tested for the presence of Tannerella forsythensis, Porphyromonas gingivalis and Treponema denticola. We found that patients had a large number of pockets colonized by Tannerella forsythensis gingivalis (88.6%) Porphyromonas gingivalis (59%) and Aggregatibacter actinomycetemcomitans (25%). The data of this study support the evidence Tannerella forsythensis as a periodontopatogen and confirmed a strong association between Porphyromonas gingivalis and Tannerella forsythensis in aggressive periodontitis and support previous findings that generalized aggressive periodontitis are associated with more complex microbiota.

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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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