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.

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.

With the rapid advancement of Internet of Things networks and its significant cybersecurity challenges, the proposal of models capable of studying malware propagation within these structures has become highly relevant. This paper aims to formulate and implement an SEIRS-NIMFA model to analyze the dissemination of malware infections with a latency period. To accomplish this, we mathematically articulated an SEIRS epidemiological model using an individual-based approach and implemented it using Python. In addition, this paper examines how varying the network size and density, the initially infected device, and several model parameters influence the propagation dynamics. Moreover, to address the Markov chain approach’s high temporal and spatial complexity, we use the n-intertwined mean-field approximation method. Our findings demonstrate that our proposal can effectively aid decision-making in implementing security measures in real-world situations. Finally, our proposal and its implementation are open to further enhancements, broadening their potential applications.

Applications with periodic data generation for Wireless Sensor Networks (WSN) require the maximum lifetime of the network in order to provide a constant and reliable service. Therefore, a minimal consumption for sensing and communication must be considered in order to extend the lifetime of the network. In this sense, the network must be capable for sending data from targets to sinks in a reliable way taking account limited capacities involved in a WSN. Consequently, data from targets must be specially treated to guarantee reliability at minimum energy consumption to extend lifetime of the network. For this reason, in this paper we propose a mathematical formulation and a heuristic to support connectivity, flow conservation constraints, data capacity per link, constraints related with communication and sensing range of sensors, and splitting constraints in order to balance the energy consumption to prolong the sensors lifetime. The results show improvements in terms of number of transmissions per sensor prolonging the lifetime of the network and assuring communication paths between targets and sinks. With this approach, we aim to use efficiently the energy resources of the network and, then, extend the lifetime of the network in comparison with a single path scheme in a WSN.

Energy Optimization in Mobile Wireless Sensor Networks with Mobile Targets Achieving Efficient Coverage for Critical Applications