The uses of wireless sensor networks have increased to be applicable in many different areas, such as military applications, ecology, and health applications. These applications often include the management of confidential information, making the issue of security one of the most important aspects to consider. In this aspect, a user authentication mechanism that allows only legitimate users to access the network data becomes critical for maintaining the confidentiality and integrity of the network information. In this paper, we describe and cryptoanalyze previous works in user authentication to illustrate their vulnerabilities and security flaws. We then propose a robust user authentication scheme that solves the identified limitations. Additionally, we describe how the proposed protocol is more suitable for a secure sensor network implementation by analysis in terms of security and performance.

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

The New Urban Agenda (Agenda 2030) adopted at the United Nations Conference related to Sustainable Urban Development (Habitat III) in the year 2016 has the goal of prompting cities to achieve the identified Sustainable Development Goals by the year 2030. In this context, cities can experiment strategies of circular economy for the optimization of resources, waste reduction, reuse, and recycling. The data generated by the components of an Internet of Things (IoT) ecosystem can contribute in two relevant ways to a smart city model: (1) by the generation of a circular economy and (2) by the creation of intelligence to improve the decision-making processes by citizens or city managers. In this context, it is in our interest to understand the most relevant axes of the research related to IoT, particularly those based on the LoRa technology. LoRa has attracted the interest of researchers because it is an open standard and contributes to the development of sustainable smart cities, since they are linked to the concepts of a circular economy. Additionally, the intention of this work is to identify the technological or practical barriers that hamper the development of solutions, find possible future trends that could exist in the context of smart cities and IoT, and understand how they could be exploited by the industry and academy.

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The development and high growth of the Internet of Things (IoT) have improved quality of life and strengthened different areas in society. Many cities worldwide are looking forward to becoming smart. One of the most popular use cases in smart cities is the implementation of smart parking solutions, as they allow people to optimize time, reduce fuel consumption, and carbon dioxide emissions. Smart parking solutions have a defined architecture with particular components (sensors, communication protocols, and software solutions). Although there are only three components that compose a smart parking solution, it is important to mention that each component has many types that can be used in the deployment of these solutions. This paper identifies the most used types of every component and highlights usage trends in the established analysis period. It provides a complementary perspective and represents a very useful source of information. The scientific community could use this information to decide regarding the selection of types of components to implement a smart parking solution. For this purpose, herein we review several works related to smart parking solutions deployment. To achieve this goal, a semi-cyclic adaptation of the action research methodology combined with a systematic review is used to select papers related to the subject of study. The most relevant papers were reviewed to identify subcategories for each component; these classifications are presented in tables to mark the relevance of each paper accordingly. Trends of usage in terms of sensors, protocols and software solutions are analyzed and discussed in every section. In addition to the trends of usage, this paper determines a guide of complementary features from the type of components that should be considered when implementing a smart parking solution.

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This paper proposes an emotion elicitation method to develop our Stock-Emotion dataset: a collection of the participants' electroencephalogram (EEG) signals who paper-traded using real stock market data, virtual money, and outcomes that emotionally affected them. A system for emotion recognition using this dataset was tested. The system extracted from the EEG signals the following features: five frequency bands, Differential Entropy (DE), Differential Asymmetry (DASM), and Rational Asymmetry (RASM), for each band. Our system then carried out feature selection using a filter method (Mutual Information Matrix), combined with a wrapper process (Chi-Square statistics) and alternatively using the embedded algorithms in a Deep Learning classifier. Finally, this work classified emotions in four quadrants of the circumplex model using Random Forest and Deep Learning algorithms. Our findings show that 1) the proposed emotion elicitation method is useful to provoke affective states associated with trading, 2) the proposed feature selection process improved the classification performance of our emotion recognition system, and 3) classifier performance of the system can recognize trading related emotions and has results comparable with the state of the art research corresponding to a similar number of output classes.