Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain

Unmanned aerial vehicles (UAVs) can support surveillance even in areas without network infrastructure. However, UAV networks raise security challenges because of its dynamic topology. This paper proposes a technique for maintaining security in UAV networks in the context of surveillance, by corroborating information about events from different sources. In this way, UAV networks can conform peer-to-peer information inspired by the principles of blockchain, and detect compromised UAVs based on trust policies. The proposed technique uses a secure asymmetric encryption with a pre-shared list of official UAVs. Using this technique, the wrong information can be detected when an official UAV is physically hijacked. The novel agent based simulator ABS-SecurityUAV is used to validate the proposed approach. In our experiments, around 90% of UAVs were able to corroborate information about a person walking in a controlled area, while none of the UAVs corroborated fake information coming from a hijacked UAV

Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods

The opacity of real-estate market involves some challenges in their agent-based simulation. While some real-estate Web sites provide the prices of a great amount of houses publicly, the prices of the rest are not available. The estimation of these prices is necessary for simulating their evolution from a complete initial set of houses. Additionally, this estimation could also be useful for other purposes such as appraising houses, letting buyers know which are the best offered prices (i.e., the lowest ones compared to the appraisals) and recommending the buyers to set an initial price. This work proposes combining dimensionality reduction methods with machine learning techniques to obtain the estimated prices. In particular, this work analyzes the use of nonnegative factorization, recursive feature elimination and feature selection with a variance threshold, as dimensionality reduction methods. It compares the application of linear regression, support vector regression, the k-nearest neighbors and a multilayer perceptron neural network, as machine learning techniques. This work has applied a tenfold cross-validation for comparing the estimations and errors and assessing the improvement over a basic estimator commonly used in the beginning of simulations. The developed software and the used dataset are freely available from a data research repository for the sake of reproducibility and the support to other researchers

EmotIoT: an IoT system to improve users’ wellbeing

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and anticipated. Since the study of related works indicates a lack of methodological approaches in designing IoT systems from the perspective of emotions and smart adaption rules, we introduce a methodology that can help design IoT systems quickly in this scenario, where the detection of users is valuable. In order to test the methodology presented, we apply the proposed stages to design an IoT smart recommender system named EmotIoT. The system allows anticipating and predicting future users’ emotions using parameters collected from IoT devices. It recommends new activities for the user in order to obtain a final state. Test results validate our recommender system as it has obtained more than 80% accuracy in predicting future user emotions

Fog computing for assisting and tracking elder patients with neurodegenerative diseases

U.S. hospitals transmit and manage great amounts of information with the avenue of Internet of things. This work departs from a real need detected by healthcare centers and hospitals in U.S., Spain and Ecuador. This work focuses on the application of fog computing for obtaining an app rich in visual content that will not overload U.S. hospital infrastructures even if it was used massively. The simulation results showed that the proposed fog-based approach could support a regular use (one day out of three on average) by 1% of patients of one of the most common neurodegenerative diseases in 14 states in U.S (i.e. 36, 400 patients in total) with only a traffic of 528 KB per day on average when using one hospital per state

Green communication for tracking heart rate with smartbands

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user

Head-mounted display-based application for cognitive training

Virtual Reality (VR) has had significant advances in rehabilitation, due to the gamification of cognitive activities that facilitate treatment. On the other hand, Immersive Virtual Reality (IVR) produces outstanding results due to the interactive features with the user. This work introduces a VR application for memory rehabilitation by walking through a maze and using the Oculus Go head-mounted display (HMD) technology. The mechanics of the game require memorizing geometric shapes while the player progresses in two modes, autonomous or manual, with two levels of difficulty depending on the number of elements to remember. The application is developed in the Unity 3D video game engine considering the optimization of computational resources to improve the performance in the processing and maintaining adequate benefits for the user, while the generated data is stored and sent to a remote server. The maze task was assessed with 29 subjects in a controlled environment. The obtained results show a significant correlation between participants’ response accuracy in both the maze task and a face–pair test. Thus, the proposed task is able to perform memory assessments

A virtual reality-based cognitive telerehabilitation system for use in the covid-19 pandemic

The COVID-19 pandemic has changed people’s lives and the way in which certain services are provided. Such changes are not uncommon in healthcare services and they will have to adapt to the new situation by increasing the number of services remotely offered. Limited mobility has resulted in interruption of treatments that traditionally have been administered through face-to-face modalities, especially those related to cognitive impairments. In this telerehabilitation approach, both the patient and the specialist physician enter a virtual reality (VR) environment where they can interact in real time through avatars. A spaced retrieval (SR) task is implemented in the system to analyze cognitive performance. An experimental group (n = 20) performed the SR task in telerehabilitation mode, whereas a control group (n = 20) performed the SR task through a traditional face-to-face mode. The obtained results showed that it is possible to carry out cognitive rehabilitation processes through a telerehabilitation modality in conjunction with VR. The costeffectiveness of the system will also contribute to making healthcare systems more efficient, overcoming both geographical and temporal limitations

Nuclear spin driven quantum relaxation in LiY_0.998Ho_0.002F_4

Staircase hysteresis loops of the magnetization of a LiY_0.998Ho_0.002F_4 single crystal are observed at subkelvin temperatures and low field sweep rates. This behavior results from quantum dynamics at avoided level crossings of the energy spectrum of single Ho^{3+} ions in the presence of hyperfine interactions. Enhanced quantum relaxation in constant transverse fields allows the study of the relative magnitude of tunnel splittings. At faster sweep rates, non-equilibrated spin-phonon and spin-spin transitions, mediated by weak dipolar interactions, lead to magnetization oscillations and additional steps.Comment: 5 pages, 5 eps figures, using RevTe