The Internet of Things (IoT) represents a mean to share resources (memory, storage computational power, data, etc.) between computers and mobile devices, as well as buildings, wearable devices, electrical grids, and automobiles, just to name few. The IoT is leading to the development of advanced information services that will require large storage and computational power, as well as real-time processing capabilities. The integration of IoT with emerging technologies such as Fog Computing can complement these requirements with pervasive and cost-effective services capable of processing largescale geo-distributed information. In any IoT application, communication availability is essential to deliver accurate and useful information, for instance, to take actions during dangerous situations, or to manage critical infrastructures. IoT components like gateways, also called Fog Nodes, face outstanding security challenges as the attack surface grows with the number of connected devices requesting communication services. These Fog nodes can be targeted by an attacker, preventing the nodes from delivering important information to the final users or to perform accurate automated actions. This paper introduces an Anomaly Behavior Analysis Methodology based on Artificial Neural Networks, to implement an adaptive Intrusion Detection System (IDS) capable of detecting when a Fog node has been compromised, and then take the required actions to ensure communication availability. The experimental results reveal that the proposed approach has the capability for characterizing the normal behavior of Fog Nodes despite its complexity due to the adaptive scheme, and also has the capability of detecting anomalies due to any kind of sources such as misuses, cyber-attacks or system glitches, with high detection rate and low false alarms. INDEX TERMS Anomaly behavior, cyber security, fog computing, IoT, neural networks.

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Magneto-rheological (MR) dampers are effective solutions in improving vehicle stability and passenger comfort. However, handling these dampers implies a strong effort in modeling and control. This research proposes an H 2 controller, based on a Takagi-Sugeno (T-S) fuzzy model, for a two-degrees-of-freedom (2-DOF) one-quarter vehicle semi-active suspension with an MR damper; a system with important applications in automotive industry. Regarding performance criteria (in frequency domain) handled herein, the developed controller considerably improves the passive suspension's efficiency. Moreover, nonlinear actuator dynamics usually avoided in reported work, is included in controller's synthesis; improving the relevance of research outcomes because the controller is synthesized from a closer-to-reality suspension model. Going further, outcomes of this research are compared (based on frequency domain performance criteria and a common time domain test) with reported work to highlight the outstanding results. H 2 controller is given in terms of quadratic Lyapunov stability theory and carried out by means of Linear Matrix Inequalities (LMI), and the command signal is applied via the Parallel Distributed Compensation (PDC) approach. A case of study, with real data, is developed and simulation work supports the results. The methodology applied herein can be extended to include other vehicle suspension's dynamics towards a general chassis control.

A model developed at the University of Tomsk, Russia, for high latitudes (over 55° N) is proposed and applied to the analysis and observation of the solar resource in the state of Sonora in the northwest of Mexico. This model utilizes satellite data and geographical coordinates as inputs. The objective of this research work is to provide a low-cost and reliable alternative to field meteorological stations and also to obtain a wide illustration of the distribution of solar power in the state to visualize opportunities for sustainable energy production and reduce its carbon footprint. The model is compared against real-time data from meteorological stations and satellite data, using statistical methods to scrutinize its accuracy at local latitudes (26–32° N), where a satisfactory performance was observed. An annual geographical view of available solar radiation against maximum and minimum temperatures for all the state municipalities is provided to identify the photovoltaic electricity generation potential. The outcomes are proof that the model is economically viable and could be employed by local governments to plan solar harvesting strategies. The results are generated from an open source model that allows calculating the available solar radiation over specific land areas, and the application potential for future planning of solar energy projects is evident.