In a technological landscape marked by the accelerated expansion of the Internet of Things (IoT), securing edge communications is becoming essential for ensuring trust, confidentiality and data integrity. The constant evolution of cyber attacks, combined with the resource limitations of IoT devices, requires the development of effective, adaptable and scalable security solutions. This paper provides a comprehensive review of recent advances and persistent challenges in securing edge communications in the IoT, with a focus on lightweight cryptographic algorithms (ASCON, PRESENT, SIMON, SPECK) and on authentication mechanisms adapted to resource-constrained devices. The trade-offs between performance and security are emphasized, besides the need for IoT-specific Anonymous Overlay Networks(AONs) and Anonymous Routing Protocols(ARPs) capable of providing protection against traffic analysis and user anonymity. The study includes a detailed classification of 18 ARPs protocols, along with a presentation of the metrics used in evaluation. It also exposes MQTT-SN protocol vulnerabilities and proposes the development of standardized security frameworks with built-in encryption and authentication for scalable IoT deployments. The main finding of the study highlights the need for further research in developing tailored security solutions for IoT devices, as well as enhancing the security of the MQTT-SN protocol.

Wireless Sensor Networks gradually became a mature technology that enabled the implementation of a large number of critical applications. However, the integration of such networks in much more complex systems, such as IoT, still poses a major challenge. In this paper we present a viable solution to accomplish such a difficult task by using MQTT-SN to integrate critical real-time WSNs into an IoT platform. The core of our work is a platform independent real-time driver for MQTT-SN along with a communication architecture to enable the integration of low end devices into an MQTT network using MQTT-SN. We also describe a comprehensive practical demonstration using real hardware modules that validates our claimed solution.

The security of Internet of Things (IoT) systems has consistently been a challenge, particularly in the context of critical infrastructure. One particular approach not yet employed in this domain is the unidirectional communication paradigm. This survey presents an analysis of the most prevalent unidirectional communication solutions, namely, data diodes, network pumps, unidirectional gateways, and unidirectional protocols. The objective of the survey is to present an analysis of the unidirectional communication methods that meet the requirements of IoT security. These methods are classified according to their implementation and operational mode. The survey analyzes the unidirectional communication solutions based on their performance, the level of security offered, the cost-effectiveness, and their cost of implementation. Additionally, it includes an analysis of the existing off-the-shelf unidirectional communication implementations found in the industry. Furthermore, it identifies some of the most important current issues and development directions.

This study investigates the use of quantum computing, particularly Grover's algorithm, to improve genetic diagnostics for DiGeorge syndrome compared to conventional computational techniques. We employed the IBM Qiskit framework to simulate Grover's algorithm for the rapid and precise identification of pathogenic gene sequences. Background: Conventional genetic diagnostic methods are laborious, delaying essential treatment decisions. Quantum computing, capable of swiftly processing large datasets, offers substantial improvements in diagnostic speed and precision. Materials and Methods: We executed Grover's algorithm using Qiskit, evaluating its performance relative to classical algorithms based on diagnostic time and accuracy. We visualized results using R Studio with the ggplot2 and dplyr libraries. Results: The quantum methodology significantly reduced diagnostic duration from 300 seconds to 45 seconds and improved accuracy from 85% to 98%, surpassing traditional techniques. Conclusions: Our findings indicate that quantum computing can transform genetic diagnostics by enabling faster and more accurate identification of genetic disorders, thus promoting earlier and more personalized treatments. Future research should focus on improving the scalability of quantum computers and incorporating effective quantum algorithms into clinical workflows.