An Adaptive Sampling Period Approach for Management of IoT Energy Consumption: Case Study Approach.

The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.

Blockchain-IoT Sensor (BIoTS): A Solution to IoT-Ecosystems Security Issues.

Sensor devices that act in the IoT architecture perception layer are characterized by low data processing and storage capacity. These reduced capabilities make the system ubiquitous and lightweight, but considerably reduce its security. The IoT-based Food Traceability Systems (FTS), aimed at ensuring food safety and quality, serve as a motivating scenario for BIoTS development and deployment; therefore, security challenges and gaps related with data integrity are analyzed from this perspective. This paper proposes the BIoTS hardware design that contains some modules built-in VHDL (SHA-256, PoW, and SD-Memory) and other peripheral electronic devices to provide capabilities to the perception layer by implementing the blockchain architecture's security requirements in an IoT device. The proposed hardware is implemented on FPGA Altera DE0-Nano. BIoTS can participate as a miner in the blockchain network through Smart Contracts and solve security issues related to data integrity and data traceability in an Blockchain-IoT system. Blockchain algorithms implemented in IoT hardware opens a path to IoT devices' security and ensures participation in data validation inside a food certification process.

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data.

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user's emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research's challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

Smart TV-Smartphone Multiscreen Interactive Middleware for Public Displays.

A new generation of public displays demands high interactive and multiscreen features to enrich people's experience in new pervasive environments. Traditionally, research on public display interaction has involved mobile devices as the main characters during the use of personal area network technologies such as Bluetooth or NFC. However, the emergent Smart TV model arises as an interesting alternative for the implementation of a new generation of public displays. This is due to its intrinsic connection capabilities with surrounding devices like smartphones or tablets. Nonetheless, the different approaches proposed by the most important vendors are still underdeveloped to support multiscreen and interaction capabilities for modern public displays, because most of them are intended for domestic environments. This research proposes multiscreen interactive middleware for public displays, which was developed from the principles of a loosely coupled interaction model, simplicity, stability, concurrency, low latency, and the usage of open standards and technologies. Moreover, a validation prototype is proposed in one of the most interesting public display scenarios: the advertising.

Polymethyl Methacrylate-Based Smart Microfluidic Point-of-Care Testing of Prothrombin Time and International Normalized Ratio through Optical Detection.

The mechanical heart valve is a crucial solution for many patients. However, it cannot function on the state of blood as human tissue valves. Thus, people with mechanical valves are put under anticoagulant therapy. A good measurement of the state of blood and how long it takes blood to form clots is the prothrombin time (PT); moreover, it is an indicator of how well the anticoagulant therapy is, and of whether the response of the patient to the drug is as needed. For a more specific standardized measurement of coagulation time, an international normalized ratio (INR) is established. Clinical testing of INR and PT is relatively easy. However, it requires the patient to visit the clinic for evaluation purposes. Many techniques are therefore being developed to provide PT and INR self-testing devices. Unfortunately, those solutions are either inaccurate, complex, or expensive. The present work approaches the design of an anticoagulation self-monitoring device that is easy to use, accurate, and relatively inexpensive. Hence, a two-channel polymethyl methacrylate-based microfluidic point-of-care (POC) smart device has been developed. The Arduino based lab-on-a-chip device applies optical properties to a small amount of blood. The achieved accuracy is 96.7%.

Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices.

In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. This article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. The algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. Adjustments are made to the data in terms of Pearson iterations, synchronization, filtering, and normalization, and DTW, linear regression, and error analysis are used to make the corresponding comparison of the two datasets. With the above, it is possible to determine if the cycles of the two signals coincide and how closely the learner's movements resemble those of the expert.