IoT

Technological progress has recently led to the emergence of various technological resources and means that are improving specific aspects of society. An example of this can be found in the “internet of things” and “edge computing”. The present study aims at knowing and analyzing the scientific literature of the set of terms formed by “edge computing” and “internet of things”, called from now on ECIT. In order to carry out the research, a study has been developed, based on bibliometrics, by means of scientific mapping. In this case, different production indicators have been taken into account, as well as the structural and dynamic development of the terms and authors extracted from the publications through the programs Analyze Results, Creation Citation Report and SciMAT. The results indicate that the study theme “edge computing” and “internet of things” is of recent creation, given that its beginnings date back to 2014. Since then the level of production has been dizzying, increasing considerably in the past two years. It can be concluded that the field of study of ECIT is of recent creation, with a solid research base based on the “internet of things”. Furthermore, the themes “big data”, “energy” and “framework” can be considered as the future lines of research on ECIT. Full article

Time (or clock) synchronization is a large and vital field of research, as synchronization is a precondition for many applications. A few example applications are distributed data acquisition, distributed databases, and real-time communication. First, this survey paper introduces the research area of time synchronization and emphasizes its relation to other research areas. Second, we give an overview of the state-of-the-art of time synchronization. Herein, we discuss both established protocol and research approaches. We analyze all techniques according to three criteria: used estimation algorithm, achievable synchronization accuracy, and the experimental conditions. In our opinion, this analysis highlights potential improvements. The most important question in this survey is as follows: which estimation method can be used to achieve which accuracies under which conditions? The intention behind this is to identify estimation methods that are particularly worth considering, as these already achieve good results in the wireless area but have not yet been examined in the wired area (and vice versa). This survey paper differs from other surveys in particular through the consideration of wireless and wired synchronization and the focus on estimation algorithms and their achievable accuracy. Full article

Due to the proliferation of extended travel range electric vehicles (EVs), these will travel through different networks that might be served by different utility companies. Therefore, we propose an architecture capable of offering a charging service to roaming vehicles. Furthermore, although the energy internet supports both the flow of energy and information, it does not support seamless EV roaming service, because it is based on a centralized architecture. The blockchain technology that is based on a decentralized system has the potential to support a secure billing platform for charging the EVs roaming through different electrical jurisdictions. Furthermore, the integration of artificial intelligence (AI) ensures that the participating players get a fair portion of the revenue. Thus, the objective of this paper is to develop an AI and blockchain integrated billing architecture that would offer a charging service to the “roaming” EVs and present a fair and unified billing solution. Full article

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing. Full article

Climate change significantly affects viticulture by reducing the production yield and the quality characteristics of its final products. In some observed cases, the consequences of climate outages such as droughts, hail and floods are absolutely devastating for the farmers and the sustained local economies. Hence, it is essential to develop new in implementation monitoring solutions that offer remote real-time surveillance, alert triggering, minimum maintenance and automated generation of incident alerts with precision responses. This paper presents a new framework and a system for vine stress monitoring called Vity-stress. The Vity-stress framework combines field measurements with precise viticulture suggestions and stress avoidance planning. The key points of the proposed framework’s system are that it is easy to develop, easy to maintain and cheap to implement applicability. Focusing on the Mediterranean cultivated table grape varieties that are strongly affected by climate change, we propose a new stress conditions monitoring system to support our framework. The proposition includes distributed field located sensors and a novel camera module implementing deep neural network algorithms to detect stress indicators. Additionally, a new wireless sensor network supported by the iBeacon protocol has been developed. The results of the sensory measurements’ data logging and imposed image detection process’s evaluation shows that the proposed system can successfully detect different stress levels in vineyards, which in turn can allow producers to identify specific areas for irrigation, thereby saving water, energy and time. Full article

In recent years, the ultra-wideband (UWB) radar technology has shown great potential in monitoring activities of daily living (ADLs) for smart homes. In this paper, we investigate the significance of using non-wearable UWB sensors for developing non-intrusive, unobtrusive, and privacy-preserving monitoring of elderly ADLs. A controlled experiment was setup, implementing multiple non-wearable sensors in a smart home Lab setting. A total of nine (n = 9) participants were involved in conducting predefined scenarios of ADLs- cooking, eating, resting, sleeping and mobility. We employed the UWB sensing prototype and conventional implementation technologies, and the sensed data of both systems were stored, analysed and their performances were compared. The result shows that the performance of the non-wearable UWB technology is as good as that of the conventional ones. Furthermore, we provided a proof-of-concept solution for the real-time detection of abnormal behaviour based on excessive activity levels, and a model for automatic alerts to caregivers for timely medical assistance on-demand. Full article

With the ongoing trend toward miniaturization via system-on-chip (SoC), both radio-frequency (RF) SoCs and on-chip multi-sensory systems are gaining significance. This paper compares the inductance of a miniaturized on-chip near field communication (NFC) antenna versus the conventional screen-printed on-substrate ones that have been used for the transfer of sensory data from a chip to a cell phone reader. Furthermore, the transferred power efficiency in a coupled NFC system is calculated for various chip coil geometries and the results are compared. The proposed NFC antenna was fabricated via a lithography process for an application-specific integrated circuit (ASIC) chip. The chip had a small area of 2.4 × 2.4 mm², therefore a miniaturized NFC antenna was designed, whereas the screen-printed on-substrate antennas had an area of 35 × 51 mm². This paper investigates the effects of different parameters such as conductor thickness and materials, double layering, and employing ferrite layers with different thicknesses on the performance of the on-chip antennas using full-wave simulations. The presence of a ferrite layer to increase the inductance of the antenna and mitigate the interactions with backplates has proven useful. The best performance was obtained via double-layering of the coils, which was similar to on-substrate antennas, while a size reduction of 99.68% was gained. Consequently, the coupling factors and maximum achievable power transmission efficiency of the on-chip antenna and on-substrate antenna were studied and compared. Full article

In a context of increased environmental awareness, the Internet of Things has allowed individuals or entities to build their own connected devices to share data about the environment. These data are often obtained from widely available low-cost sensors. Some companies are also selling low-cost sensing kits for in-house or outdoor use. The work described in this paper evaluated, in the short term, the performance of a set of low-cost sensors for temperature, relative humidity, atmospheric pressure and carbon dioxide, commonly used in these platforms. The research challenge addressed with this work was assessing how trustable the raw data obtained from these sensors are. The experiments made use of 18 climatic sensors from six different models, and they were evaluated in a controlled climatic chamber that reproduced controlled situations for temperature and humidity. Four CO₂ sensors from two different models were analysed through exposure to different gas concentrations in an indoor environment. Our results revealed temperature sensors with a very high positive coefficient of determination (r² ≥ 0.99), as well as the presence of bias and almost zero random error; the humidity sensors demonstrated a very high positive correlation (r² ≥ 0.98), significant bias and small-yet-relevant random error; the atmospheric pressure sensors presented good reproducibility, but further studies are required to evaluate their accuracy and precision. For carbon dioxide, the non-dispersive infra-red sensors demonstrated very satisfactory results (r² ≥ 0.97, with a minimum root mean squared error (RMSE) value of 26 ppm); the metal oxide sensors, despite their moderate results (minimum RMSE equal to 40 ppm and r² of 0.8–0.96), presented hysteresis, environmental dependence and even positioning interference. The results suggest that most of the evaluated low-cost sensors can provide a good sense of reality at a very good cost–benefit ratio in certain situations. Full article

The recent surge in Internet of Things (IoT) deployment has increased the pace of integration and extended the reach of the Internet from computers, tablets and phones to a myriad of devices in our physical world. Driven by the IoT, with each passing day, the Internet becomes more integrated with everyday life. While IoT devices provide endless new capabilities and make life more convenient, they also vastly increase the opportunity for nefarious individuals, criminal organizations and even state actors to spy on, and interfere with, unsuspecting users of IoT systems. As this looming crisis continues to grow, calls for data science approaches to address these problems have increased, and current research shows that predictive models trained with machine learning algorithms hold great potential to mitigate some of these issues. In this paper, we first carry out an analytics approach to review security risks associated with IoT systems, and then propose a machine learning-based solution to characterize and detect IoT attacks. We use a real-world IoT system with secured gate access as a platform, and introduce the IoT system in detail, including features to capture security threats/attacks to the system. By using data collected from a nine month period as our testbed, we evaluate the efficacy of predictive models trained by means of machine learning, and propose design principles and a loose framework for implementing secure IoT systems. Full article

Pervasive computing applications deal with the intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users. One example of infrastructure that can host intelligent pervasive services is the Edge Computing (EC) ecosystem. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT). In this paper, we propose an intelligent, proactive tasks management model based on demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest in them. This information is combined with historical observations of and support for a decision making scheme to conclude which tasks that are offloaded due to limited interest in them. We have to recognise that, in our decision making, we also take into consideration the load that every task may add to the processing node where it will be allocated. The description of our model is accompanied by a large set of experimental simulations for evaluating the proposed mechanism. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly, while concluding the most efficient decisions. Full article

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97. Full article

The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular. Full article

This paper is focused on the design and development of a smart and compact electronic control unit (ECU) for the monitoring of a bus fleet. The ECU system is able to extract all vehicle data by the on-board diagnostics-(ODB)-II and SAE J1939 standards. The integrated system Internet of Things (IoT) system, is interconnected in the cloud by an artificial intelligence engine implementing multilayer perceptron artificial neural network (MLP-ANN) and is able to predict maintenance of each vehicle by classifying the driver behavior. The key performance indicator (KPI) of the driver behavior has been estimated by data mining k-means algorithm. The MLP-ANN model has been tested by means of a dataset found in literature by allowing the correct choice of the calculus parameters. A low means square error (MSE) of the order of 10⁻³ is checked thus proving the correct use of MLP-ANN. Based on the analysis of the results, are defined methodologies of key performance indicators (KPIs), correlating driver behavior with the engine stress defining the bus maintenance plan criteria. All the results are joined into a cloud platform showing fleet efficiency dashboards. The proposed topic has been developed within the framework of an industry research project collaborating with a company managing bus fleet. Full article

In smart cities, the progress of technology has allowed the implementation of sensors, originating the Internet of Things (IoT) and making cities safer and more sustainable. Hence, the presence of elements that generate visual and artistic effects of IoT technology can make a great contribution to the provision of information that the urbanite needs. The aim of this study is to analyze worldwide research on the visual and artistic effects of IoT in smart cities. Bibliometric techniques were utilized on 1278 articles on this subject matter for the period of 2010–2019 to achieve results on activity production. This has increased yearly, where in the last triennium, it has accumulated 85.21% of documents. Computer science and engineering were the most prominent subject areas where the articles were classified. The lines of research in the development of this research topic have been detected. Furthermore, the main directions for future research have also been identified. This study aims to contribute to highlighting the drivers of this field of research, in addition to providing the available information and future directions to improve academic and scientific discussion. Full article

A spread spectrum sound-based local positioning system (SSSLPS) has been developed for indoor agricultural robots by our research group. Such an SSSLPS has several advantages, including effective propagation, low cost, and ease of use. When using sound velocity for field position measurements in a greenhouse, spatial and temporal variations in temperature during the day can have a major effect on sound velocity and subsequent positioning accuracy. In this research, a temperature-compensated sound velocity positioning was proposed and evaluated in comparison to a conventional temperature sensor method. Results indicate that this new proposed method has a positioning accuracy to within 20 mm in a 3 m × 9 m ridged greenhouse. It has the potential to replace the current system of using the temperature sensors in a greenhouse. Full article

In this age of electronic communication, checking the physical mailbox is still part of our daily life. Mailboxes are placed several meters away from the house, and sometimes across the opposite side of a street. It is annoying to walk to the mailbox each day and realize that the mailbox is empty. In this paper, an Internet of Things (IoT)-connected smart mailbox is developed that automatically sends a notification to the smartphone whenever new mail arrives. This notification removes the frustration of an unnecessary trip to the mailbox. The proposed mailbox contains a low power device comprising of sensors, a system-on-chip microcontroller with Bluetooth Low Energy (BLE), and a rechargeable battery that charges with a solar panel. Whenever the mailbox door is closed, it checks the presence of mail and then sends the data to a hub using BLE. The hub is placed inside the house and sends a push notification to the smartphone using the home Wi-Fi. A smart speaker, Google Home, is also interfaced with the system, which can verbally say the status of the mailbox when asked a customized question. A prototype of the smart mailbox, the hub, and the smartphone app is developed and tested successfully. Full article

Chipless radio frequency identification (RFID) is a wireless technology that has the potential for many industrial applications, including the internet of things (IoT) applications, in which identification, sensing, and tracking are required. This technology has been improved during the last century. However, the processing of the backscattered signal in a chipless RFID system is still a challenge because the encoded data are embedded in the backscattered signal of a passive tag. The reader hardware, antennas, and the wireless channel have their own response in the received signal, which contains the tag ID information. The tag also produces a response, which is a combination of responses from different resonators, substrate, and copper reflection in a tag. In this paper, the reflection from a typical chipless RFID tag is analyzed, and all components of the backscattered signal are separated in both time and frequency domains. In addition, an equivalent circuit model for a backscattered chipless RFID tag is proposed, and the model is verified based on the actual performance of the resonator. This study has some important implications for future research. Full article

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes. Full article