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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 (Time-Sensitive Networking), 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.

One of the main concerns of agricultural financing institutions is to make sure the loans they grant are used for the stated objective when the loan was requested. Specifically, when Banco Agrario de Colombia grants loans for crop farmers, it schedules verification visits to the cultivation sites to check if the crop stipulated in the loan agreement exists and assess its health. These visits are challenging to make due to the number of visits over vast areas that they need to schedule, lack of trained personnel, and difficulty of access. This article proposes a software tool, based on a machine learning model for processing free satellite imagery, to support the bank’s identification of non-compliant crops with the investment plan before making field visits, minimizing the loss of investment by focusing on those areas to prioritize the visits. Sugarcane along the department of Boyacá, Colombia was chosen as the case of study. Free access satellite imagery through the Colombian Data Cube (CDCol) was used and machine learning models were applied on them to classify the land and predict the presence of the crop, a Random Forest model achieved an overall F1-score of 91% using Landsat-8 imagery and a K-nearest Neighbors model achieved an overall F1-score of 98% using Sentinel-2 imagery.

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.