Internet of Things (IoT) networks have been integrated into industrial infrastructure schemes, positioning themselves as devices that communicate highly classified information for the most critical companies of world nations. Currently, and in order to look for alternatives to mitigate this risk, solutions based on Blockchain algorithms and Machine Learning techniques have been implemented separately with the aim of mitigating potential threats in IIoT networks. In this paper, we sought to integrate the previous solutions to create an integral protection mechanism for IoT device networks, which would allow the identification of threats, activate secure information transfer mechanisms, and it would be adapted to the computational capabilities of industrial IoT. The proposed solution achieved the proposed objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network. In some cases, it overcomes traditional detection mechanisms such as an IDS.

The Internet of Things (IoT) is growing rapidly, enabling interconnected devices to communicate and share data seamlessly. While this expansion drives significant advancements across industries, it also introduces critical challenges in ensuring secure device communication. IoT devices often operate under strict constraints in processing power, memory, and energy consumption, complicating the implementation of robust security measures. Lightweight cryptographic algorithms have emerged to address these challenges, with ASCON gaining recognition for its robustness and efficiency. Selected as the winner of the National Institute of Standards and Technology (NIST) competition for lightweight encryption algorithms, ASCON ensures confidentiality and integrity through authenticated encryption with associated data (AEAD) tailored for resource-constrained environments. This paper presents the implementation and performance analysis of ASCON on IoT devices. Using the CupCarbon simulator framework, optimized for modeling smart cities and wireless sensor networks, we evaluate its effectiveness under resource-limited conditions. Performance metrics are analyzed, including latency, response times, and resource utilization. Additionally, ASCON is implemented on Raspberry Pi devices to validate its feasibility in real-world scenarios. Results demonstrate that ASCON enables efficient and secure communications while minimizing computational overhead, making it a viable solution for IoT environments with constrained resources. These findings highlight ASCON’s potential to address critical security needs in the rapidly evolving IoT landscape.

In recent years, there has been an exponential growth of IoT (Internet of Things) devices in the market and increasingly, they are being part of critical mission functionalities for many industries. For this reason, processes associated with these devices are generating new requirements at the level of interconnectivity, security, and scalability. Due to the large volume of devices, this growth process has tested the existing communications infrastructure, deployment architecture, different support services, technical requirements and the intrinsic characteristics of the services offered. In this sense, the use of Edge Computing schemes and TSN networks (Time Sensitive Networks), is proposed as a tool for supporting IIoT (Industrial Internet of Things) services to achieve optimization and assurance processes of latency, scalability and interoperability parameters required by critical mission services such as health, transportation and production. In this sense, we propose several strategies for assuring low latency, scalability and interoperability in Edge Computing and TSN networks for critical IIoT services. These strategies are evaluated considering different levels of congestion and considering different schemes such as best effort, TSN and Edge Computing.

In Mobile Wireless Sensor Networks there could be scenarios where absolutely all network nodes (including the base station) are mobile, becoming a very hard task to find a communication path between a sensor node and the base station due to many network variables are changing at each moment. In addition, there are delay-sensitive applications that require establishing communication paths as soon as possible to mitigate low network performance in terms of end-to-end delay, reducing, at the same time, the energy consumption of the network. For this reason, we propose a multiobjective mathematical optimization model for finding the optimal communication path between a source node and a sink (base station) considering hard scenarios where all network nodes are mobile and minimizing end-to-end delay and energy consumption. This mathematical model would offer significant advantages to evaluate new algorithms due to we could know how far or close are the algorithm results from the optimal values given by the mathematical model. In addition, we propose a prediction distributed routing algorithm based on Markov Chains that takes into account the network mobility in order to find as fast as possible a communication path between a source node and a sink with minimal energy consumption. We also propose a deep learning approach to predict future nodes’ distances in a mobile network to determine if future movements of nodes will cause communication disruptions in paths. Significant findings were obtained when the Markov Chains and Deep Learning approaches were compared in terms of predicting nodes mobility and reducing the delay and the energy consumption in the network. The performance of our prediction algorithms (Markov Chains and Deep Learning approaches) is evaluated against the mathematical model to determine how good it is. Finally, to analyze our prediction algorithms considering real online scenarios, we compared it against typical routing algorithms, obtaining promising results in terms of delay and energy consumption in all mobile node scenarios.

A Multi-objective Routing Algorithm for Wireless Mesh Network in a Smart Cities Environment

The user's loyalty is a key component to be considered by the mobile telecommunication operator into a highly competitive environment with a continuous growth of mobile internet services. For this reason, we propose a methodology to support the network performance decision to provide mobile internet services based in the users perception. The correlation between the Quality of Service parameters and the Quality of Experience is the base of this methodology; we use two different neural network models to obtain the final correlation model. The model is proved gathering information of five different cells using a 3G modem and a 3G mobile phone (Android OS). In this work we are validating the model obtained only with gathering network performance parameters.