As a natural consequence of Industry 4.0 demands related to industrial processes control digitization, the smart manufacturing (SM) concept was enriched by integrating advanced technologies of digital information processing. This paper presents an original solution for improving the most representative instance of SM, namely, supply chain management (SCM), by using the most recent approach in hierarchical networked control systems (HNCS), that is Edge Computing. The paper presents both the software and hardware architecture of an Edge Gateway device which offers, on one hand, dual communication both on vertical (between the network layers from the local, inferior, represented by IIoT, to the central, superior, represented by the Cloud) and on horizontal (between similar devices located on the same Edge level) and, on the other hand, can locally process data to take fast decisions which facilitate real-time process control. The validity of the proposed solution is supported by a case study performed on a flexible assembly/disassembly line (laboratory model).

This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management.

Encryption is a traditional method of scrambling data beyond recognition. For years, encryption has been widely used in various areas and domains where secrecy and confidentiality were required. However, there are certain encryption techniques that are not allowed in some countries. In these cases, steganography may come as a solution for hiding data using an apparently inoffensive and innocent carrier. The purpose of steganography is to deliver secret information from a sender to a receiver by scrambling the communication channel not the information itself. This way, it is very unlikely for someone to identify and extract the secret message without knowing exactly how it was embedded into the carrier. Moreover, this task is more difficult since most steganographic methods rely on file or packet header redundancy, lossy multimedia algorithms or insignificant content alteration. All of these are very hard to detect. In this paper, a combination of encryption with image based steganography is presented in order to offer similar robustness as offered by advanced encryption algorithms such as RSA, AES or DES but with lower resource needs. The most original contribution of this paper consists in how a secret message is embedded into a image carrier generating 3 different but very similar images. These images are sent to the destination using different channels and then used to recover the original (secret) message.

This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. Recognizing the critical security challenges posed by the rapid expansion of IoT networks, this work evaluates the proposed IDS model with a primary focus on optimizing training time without sacrificing detection accuracy. The paper begins with a comprehensive review of existing hybrid machine learning models for IDS, highlighting both their strengths and limitations. It then provides an overview of the technologies and methodologies implemented in this work, including the utilization of “Botnet IoT Traffic Dataset For Smart Buildings”, a newly released public dataset tailored for IoT threat detection. The hybrid IDS model is explained in detail, followed by a discussion of experimental results that assess the model’s performance in real-world conditions. Furthermore, the proposed IDS is evaluated for its effectiveness in enhancing IoT security within smart building environments, demonstrating how it can address unique challenges such as resource constraints and real-time threat detection at the edge. This work aims to contribute to the development of efficient, reliable, and scalable IDS solutions to protect IoT ecosystems from emerging security threats.

In this paper, a method of interaction with Wireless Sensors Networks (WSN) through cloud, called Wireless Sensor Network over Cloud (WSN-C), is proposed. The physical sensors are not considered stand-alone sensors but they can be monitored, controlled and updated (programmed) via cloud services. The Cloud can communicate with the sensors through the WSN access point, or Sensor Gateway. Thus, every sensor inside the network can be accessed, monitored and controlled from the Cloud. In this way, the user not only has access to the raw data from physical sensors, but also can create virtual sensors and can program and interact directly with the physical sensor, remotely via cloud services. In addition the sensing layer represented by the physical sensors, an actuating layer (based on mechanical, electrical or hydraulic actuators) is included. The Cloud can communicate with the actuators through the Actuator Gateway. There are many applications in which such architecture is needed, especially for monitoring physical sensors, allowing multiple users access to the data, providing higher security, scalability and a way of processing data and send back commands to the local node.