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As drilling of new oil and gas wells increase to meet energy demands, it is essential to optimize processes to ensure the health and safety of the crew as well as the protection of the environment. Drilling operations represent a dynamic and challenging environment with natural and mechanical factors that need to be closely managed. Well control refers to the technique employed while drilling for balancing the hydrostatic and formation pressures to prevent the influx of water, gas, or hydrocarbons that would ultimately result in an uncontrolled flow to the surface. In the event of a well control incident, the crew must take proper and prompt actions to mitigate the risks and shut-in the well. In this study, we introduce the Well Control Space Out technology, an internet-of-things (IoT) environment that couples cameras and an edge server to implement state-of-the-art deep-learning models for the real-time processing of video images recording the drillstring. The computational models automatically perform object detection to keep track of key drilling rig components. The results from the video analysis are displayed on a dashboard describing the state and steps to follow in a well control incident without the need for any time-consuming, manual calculations. The internet-of-things edge foundation laid in drilling can be seamlessly expanded to other upstream sectors, where time-sensitive, critical decisions can be made in real-time, in the field, closer to operations. Finally, this technology can be seamlessly integrated with the current technologies to develop an automated closed-loop control system.

Electric Vehicles (EVs), accompanied with the use of Renewable Energy Sources (RES), are the solution for the decarbonization of the transport sector and are undoubtedly on the rise. Although EVs have been in the limelight over the last decade, little effort has been made towards the proper use of the vehicle’s battery. Therefore, a better understanding of Lithium-ion (Li-ion) batteries, since they represent the heart of the majority of electric cars, during the discharging and charging procedure is crucial. The present study, that was experimentally conducted under real-world driving conditions, quantitatively analyzes the energy losses that take place during the charging of a Battery Electric Vehicle (BEV), focusing especially in the previously unexplored 80%–100% State of Charge (SoC) area. The results show that losses, during charging within the abovementioned area, are almost double compared to the 20%–80% SoC area and vehicle’s average specific real energy consumption is almost 2 kWh/100 km more, compared to what the driver sees on the EV’s dashboard. Furthermore, it is not for the driver’s benefit to exceed 80% of SoC during charging, considering the required charging time, the distance that each SoC area provides and the life expectancy of the battery itself. Based on these results and after a thorough literature review, the authors suggest the optimum SoC range within which drivers should operate the EV’s battery.

XYZ is a company engaged in the port sector. To support the company’s business processes, XYZ uses two applications to carry out operational activities, namely the CARTOS application to manage invoices and the Finance application to record company costs and revenues. To produce a cost accounting report, XYZ is still processing and visualizing it manually with data sources from the two applications mentioned earlier. This resulted in quite a long time processing data into information. So that reporting to management cannot be done in real time. Therefore XYZ needs a system that can help management to analyze and manage data into information in real time. The Business Intelligence (BI) method is one of the solutions for company needs, especially in analyzing and providing access to data to help make better decisions. This study discusses the design and implementation of business intelligence solutions ranging from architecture, data warehouse, ETL processes and visualization in the form of a dashboard in accordance with the needs of XYZ. The method used in developing business intelligence dashboards refers to the executive information system life cycle method which consists of justification, planning, business analysis, design, construction, and dissemination. The results of this research are dashboard visualization using the Power BI tool that displays information and knowledge needed in the monitoring process and becomes material to produce management decisions related to cost accounting reports.

Recent advances in computer vision have led to a resurgence of interest in visual data analytics. Researchers are developing systems for effectively and efficiently analyzing visual data at scale. A significant challenge that these systems encounter lies in the drift in real-world visual data. For instance, a model for self-driving vehicles that is not trained on images containing snow does not work well when it encounters them in practice. This drift phenomenon limits the accuracy of models employed for visual data analytics. In this paper, we present a visual data analytics system, called Odin, that automatically detects and recovers from drift. Odin uses adversarial autoencoders to learn the distribution of high-dimensional images. We present an unsupervised algorithm for detecting drift by comparing the distributions of the given data against that of previously seen data. When Odin detects drift, it invokes a drift recovery algorithm to deploy specialized models tailored towards the novel data points. These specialized models outperform their non-specialized counterpart on accuracy, performance, and memory footprint. Lastly, we present a model selection algorithm for picking an ensemble of best-fit specialized models to process a given input. We evaluate the efficacy and efficiency of Odin on high-resolution dashboard camera videos captured under diverse environments from the Berkeley DeepDrive dataset. We demonstrate that Odin's models deliver 6X higher throughput, 2X higher accuracy, and 6X smaller memory footprint compared to a baseline system without automated drift detection and recovery.

Nowadays large amounts of movement data is available. This makes it important not only to be aware of how to collect and store this data, but also how to visually represent the information to get insights and “read” the story behind data. When visualising origin-destination data, the traditional flow map is the solution most often selected. A single flow map, however, does not necessarily show all the available attribute variables and also tends too clutter quickly.A more appropriate solution is a dashboard. It provides users with summaries of the represented information. Despite the dashboard suitability to support getting insights, current dashboards have some limitations regarding the flexibility of the layout. To overcome these limitations, we introduce adaptability in dashboards. In our case adaptability ensures that users get insights into the component of interest (space, time, or attribute) on 3 levels of detail. Adaptability is initiated by user tasks to resulting in changes in the visualizations of represented information and dashboard interfaces. We illustrate the concept of an adaptable dashboard with two case studies.

This article presents the development of a platform for real-time monitoring of multi-microgrids. A small-scale platform has been developed and implemented as a prototype, which takes data from various types of devices located at a distance from each other. The monitoring platform is interoperable, as it allows several protocols to coexist. While the developed prototype is tested on small-scale distributed energy resources (DERs), it is done in a way to extend the concept for monitoring several microgrids in real scales. Monitoring strategies were developed for DERs by making a customized two-way communication channel between the microgrids and the monitoring center using a long-range bridged wireless local area network (WLAN). In addition, an informative and easy-to-use software dashboard was developed. The dashboard shows real-time information and measurements from the DERs—providing the user with a holistic view of the status of the DERs. The proposed system is scalable, modular, facilitates the interoperability of various types of inverters, and communicates data over a secure communication channel. All these features along with its relatively low cost make the developed real-time monitoring platform very useful for online monitoring of smart microgrids.

Automobiles approaching a controlled intersection need to brake and come to a full stop when the signal transitions from green to red, and the vehicle must later accelerate to normal speeds after the signal changes back to green. These stops and starts associated with normal signal changes lead to unnecessary energy consumption and vehicle emissions. Previous studies have revealed that the optimization of traffic intersections' signal controls and coordination facilitates smoother traffic flows with reduced stop-and-go driving, which can significantly reduce traffic congestion and unnecessary fuel waste. This paper presents an interactive visual analytics dashboard that allows transportation planners to explore and analyze energy consumption patterns resulting from temporally varying traffic signal phases at multiple intersections along a major transportation corridor using traffic simulation outputs. The visual dashboard is implemented as an accessible and responsive web application and employs a combination of visualization techniques to cover multiple aspects of vehicle acceleration and braking at multiple adjacent intersections along a corridor. The paper presents a case study of a simulated traffic scenario on the Shallowford Road traffic corridor located in Chattanooga, Tennessee to demonstrate the capability of the visual dashboard.

One of the most challenging topics in the field of intelligent transportation systems is the automatic interpretation of the driver’s behavior. This research investigates distracted driver posture recognition as a part of the human action recognition framework. Numerous car accidents have been reported that were caused by distracted drivers. Our aim was to improve the performance of detecting drivers’ distracted actions. The developed system involves a dashboard camera capable of detecting distracted drivers through 2D camera images. We use a combination of three of the most advanced techniques in deep learning, namely the inception module with a residual block and a hierarchical recurrent neural network to enhance the performance of detecting the distracted behaviors of drivers. The proposed method yields very good results. The distracted driver behaviors include texting, talking on the phone, operating the radio, drinking, reaching behind, fixing hair and makeup, and talking to the passenger.

Short- and long-range wireless communication is imperative for many remote monitoring applications such as industrial automation, digital health, and smart cities. Remote monitoring is one of the components of complex systems that can be optimized with the use of machine-learning-based techniques. In this article, an intelligent parking system is presented. The system exploits the benefits of a synergy between licensed and unlicensed wireless technologies, IoT, computer vision, and artificial intelligence. We discuss how an end user can interact with the system and manage it with a user interface. The user interface provides a range of dashboard facilities that provide various statistics about car park utilization and optimization facilities. Our proposed solution comprises a range of sensors including high-resolution cameras, which are distributed throughout a car park. The cameras are connected using different types of wireless communication. The data is processed in real time by the sensors and sent to a cloud server, where it is analyzed, stored, and presented to the end user. The object detection, tracking, and optical character recognition system are executed by the vision sensor, which utilizes a combination of convolutional neural networks and a proprietary deep-learning-based object detection algorithm. This solution has achieved high levels of classification accuracy (99.97 percent) and high image processing speeds (66 ms).

In this study, a systematic, low-cost, sensor-less solution for generating random alarm to distracted drivers/ nurses is proposed and designed to save them from accidents. A simple Finite State Machine (FSM) based algorithm is proposed for this work which has been implemented using an ATmega 328P – PU microcontroller. For testing the status of the driver/nurse, Linear Feedback Shift Register (LFSR) has been used to generate pseudo-random events at arbitrary intervals. In addition to this, a Register Transfer Level (RTL) diagram has also been investigated, and system output has been verified through Field Programmable Gate Arrays (FPGA) simulation. The prototype system has been deployed in a real-life environment by placing it in dashboard of a driver in a running vehicle or by putting the system on desk of a nurse on duty. The effective alertness of the driver and the nurse is found to be 76% and 87% respectively.

Real-time information of the underground environment plays a vital role for underground coal mines. For this purpose, a number of systems have been developed by earlier researchers. However, the application of IoT is very limited due to the nonexistence of TCP/IP in underground coal mines. Therefore, attempts have been made to develop an IP based multimode sensing platform for realizing the vision of IoT in underground coal mines. In the present development, a Zigbee based wireless sensor network (WSN) has been initially established and extended to IoT with IP enabled gateway. In addition, the data discard mechanism has been used to address the life span of the sensor network. A dashboard has been designed to access the data at the confluence of WSN and the gateway. Further, an android app has been developed for retrieving the data using the corresponding IP address. The data have also been visualized by the web browser in the same fashion. Finally, the full-scale IoT system has been tested and it is evident from the obtained results that the response of the system is in agreement.

Maintaining an adequate separation from the vehicle in front is key to safe driving. While LIDAR and RADAR sensors could be used for ranging, cost considerations and the huge installed base of vehicles that lack these sensors, especially in developing regions, call for a low-cost yet robust alternative. To this end, we present FarSight, a system that performs vehicle ranging using a smartphone mounted on the windshield or the dashboard. FarSight uses the smartphone's camera to identify and draw a bounding box around vehicles in front, based on which ranging is performed. Unlike prior smartphone-based work, FarSight does not depend on any infrastructure support such as standard-width lane markers and works with a heterogeneous mix of vehicles, both of which are characteristics of developing regions. We develop a novel hybrid approach for vehicle detection and tracking, which balances accuracy and speed by combining deep neural network based vehicle detection with vision-based object tracking in a pipelined manner. We also devise data augmentation techniques to improve the effectiveness of vehicle detection, thereby increasing the ranging distance. We show that FarSight can range accurately in both daytime and nighttime conditions and up to distances of 90 m. We have implemented FarSight as an Android-app and tested it across various phones. Further, we present two ranging-based applications built on FarSight.

Background The COVID-19 pandemic occurred and rapidly spread around the world. Some online dashboards have included essential features on a world map. However, only transforming data into visualizations for countries/regions is insufficient for the public need. This study aims to (1) develop an algorithm for classifying countries/regions into four quadrants inn GSM and (2) design an app for a better understanding of the COVID-19 situation. Methods We downloaded COVID-19 outbreak numbers daily from the Github website, including 189 countries/regions. A four-quadrant diagram was applied to present the classification of each country/region using Google Maps run on dashboards. A novel presentation scheme was used to identify the most struck entities by observing (1) the multiply infection rate (MIR) and (2) the growth trend in the recent 7 days. Four clusters of the COVID-19 outbreak were dynamically classified. An app based on a dashboard aimed at public understanding of the outbreak types and visualizing of the COVID-19 pandemic with Google Maps run on dashboards. The absolute advantage coefficient (AAC) was used to measure the damage hit by COVID-19 referred to the next two countries severely hit by COVID-19. Results We found that the two hypotheses were supported: India (i) is in the increasing status as of April 28, 2021; (ii) has a substantially higher ACC(= 0.81 > 0.70), and (iii) has a substantially higher ACC(= 0.66 Conclusion Four clusters of the COVID-19 outbreak were dynamically classified online on an app making the public understand the outbreak types of COVID-19 pandemic shown on dashboards. The app with GSM and AAC is recommended for researchers in other disease outbreaks, not just limited to COVID-19.

Background: Medical residents comprise a large, but unique, subset of the physician workforce They serve as front-line staff, but are trainees, regulated by duty hour and supervision restrictions Heightened oversight is necessary to ensure resident supervision and safety whilst mobilizing this important workforce during crisis This manuscript describes the development and implementation of an institutional crisis dashboard to facilitate timely information gathering and decision-making regarding resident activities during the COVID-19 pandemic Objective: The purpose of the study was to develop an intelligent graduate medical education dashboard to centralize and integrate data to support accurate, timely decisions in several areas: (1) track redeployment;(2) ensure adherence to supervision and duty hour regulations;and (3) monitor infection control and safety measures Methods: The dashboard is a live Microsoft Excel database saved to a shared fileserver All existing databases within the education department were reviewed to form a foundational template A monitoring section provides at-a-glance information on trainee assignments Embedded color-coded flags attached to specific responses immediately highlight areas of deficiency Results: The dashboard facilitated the efficient mobilization of trainees, while ensuring assignments followed education regulations It allowed the education office to centralize and proactively arrange mass scrubs orders, facemask fitting, and personal protective equipment training It was a monitoring system that enabled recognition of safety concerns in real-time, including identifying areas where residents were most exposed or infected Conclusion: The dashboard improved the efficiency of crisis response, while prioritizing resident safety It is feasible, low cost and easily accessible, even during Internet disruption It can be used by hospitals worldwide, including low resource settings © 2021 Kellie L Kiernan, Maha Al Fahim, Thana Harhara, Satish Chandrasekhar Nair, Halah Ibrahim

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

In Indonesia and other developing countries, motorcycles are popular means of transportation. With the growing number of motorcyclists, it becomes harder for law enforcement to monitor motorcyclists who do not use a helmet. In this research, we propose a new system to detect motorcyclist that does not wear a helmet from a dashboard camera footage. We use a convolutional neural network to detect motorcyclists from the footage and two different residual network types to count the number of passengers and number of helmets, respectively. Our best motorcyclist detection model can achieve 100% accuracy, while our passenger and helmet detection models can achieve F1 Scores of 0.99 and 0.97, respectively. Our system can achieve 0,93 in video testing accuracy.

Developments in Internet of Things (IoT) have enabled innovations in smart home and industrial automation, providing possibilities for devices in homes to be monitored and controlled remotely. Such solutions have resulted in energy efficiency and cost savings, as appliances are monitored and controlled by small, resource constrained embedded devices. The paper presents a design of an ESP8266 NodeMCU smart home solution, using Message Queuing Telemetry Transport (MQTT) and Node-RED. The smart home solution design utilises an MQTT mosquito broker on raspberry Pi $3\mathrm{B} +$, a single board computer development board. A DHT 22 sensor is interfaced with the ESP8266 micro-controller to collect sensor data for temperature and humidity, with the raspberry Pi performing functions of MQTT broker to relay sensor data information to a Node-RED dashboard.

Today, there is a significant trend of blending optimized waste management strategies with low power, low-cost Internet of Things (IoT) solutions. This paper discusses and investigates Garbage Zero (Garb0), an indigenous product developed for outdoor garbage bins. Garb0 aims at developing a power efficient IoT based real-time solid waste monitoring solution that helps cities to optimize their waste collection and maintain clean and green environment. The Garb0 sensor module can be installed within public garbage bins to provide real time fill level data. The related information regarding the current fill level of the garbage bin will be transmitted to a cloud using the Low Power Wide Area Networks (LPWAN) based Long Range (LoRa) technology [1][2], which will be further processed and forwarded to garbage collector’s mobile application and to the municipal dashboard for updating the status. Optimized schedule and route for collecting the garbage from the garbage bins will be generated on the garbage collector’s app and accordingly directs the driver to follow the most efficient route. The main focus of this paper is on the hardware and software architecture implemented to achieve low cost, low power consumption and long battery life.

The efficient maintenance and service system is crucial to ensure the vehicle always in perfect condition. Conventional periodic maintenance has been proven to lack in term of efficiency and could cause major damage inside the vehicle system. An approach of predictive maintenance is proposed in this project. This project focuses on the logistics industry because transportation is very crucial in their environment. They must ensure their process of supplying the product run smoothly without any problems. A prototype of the Development of IoT Based Logistic Vehicle Maintenance system has been developed using both hardware and software. Two main microcontrollers which are Arduino UNO and NodeMCU will be used to collect the sensor reading from DHT11 sensor and two pressure transducer sensors and then send the data to the MySQL database. There will be two methods of displaying the data which are through a private webpage that acts as the data log and through a visualization method by using Grafana dashboard. The prototype system was implemented successfully for the intended application

Groundwater is one of the major sources of drinking water in most of the countries. Dumping of waste in landfill leads to the contamination of the underground water, which is termed as Leachate, which contains harmful organic and inorganic matters which makes the underground water unusable for consumption. This project proposes an IoT based water quality monitoring system and alerts the concerned authorities if the consumed groundwater is polluted. The proposed system uses various sensors, Node MCU and is integrated with cloud infrastructure for database storage and for real-time dashboard maintenance of the measured parameters.

This paper is about monitoring people movements in Emilia-Romagna : the region of Italy in which our company, Lepida ScpA, is located. The unfortunate event of the 2020 pandemic triggered by the Covid-19 virus gave us the chance to exploit our BIGDATA infrastructure to provide information to the regional Public Administration (PA) in order to take strategic decisions to face the emergency. This goal actually corresponds to the aim with which we started last year to design and implement our infrastructure. Our monitoring project is grounded on our regional WiFi infrastructure. This WiFi access is provided for free to anyone and it is available in all major cities and municipalities over Emilia-Romagna. We describe the challenges we faced and the choices we made during the process and the final results we achieved.

The purpose of this paper is to introduce a useful online interactive dashboard (https://mahdisalehi.shinyapps.io/Covid19Dashboard/) that visualize and follow confirmed cases of COVID-19 in real-time. The dashboard was made publicly available on 6 April 2020 to illustrate the counts of confirmed cases, deaths, and recoveries of COVID-19 at the level of country or continent. This dashboard is intended as a user-friendly dashboard for researchers as well as the general public to track the COVID-19 pandemic, and is generated from trusted data sources and built in open-source R software (Shiny in particular); ensuring a high sense of transparency and reproducibility. The R Shiny framework serves as a platform for visualization and analysis of the data, as well as an advance to capitalize on existing data curation to support and enable open science. Coded analysis here includes logistic and Gompertz growth models, as two mathematical tools for predicting the future of the COVID-19 pandemic, as well as the Moran's index metric, which gives a spatial perspective via heat maps that may assist in the identification of latent responses and behavioral patterns. This analysis provides real-time statistical application aiming to make sense to academic- and public consumers of the large amount of data that is being accumulated due to the COVID-19 pandemic.

The increase in population and rapid industrial development of a country, especially in urban areas, contributes to various urbanization issues such as housing, food demands, education, health, poverty, etc. An alternative method of farming utilizing minimized land areas with large crop produce is highly sought. This farming method is expected to be operated at a lower cost with easy monitoring of essential plantation necessities such as light, water level, temperature, and humidity. This paper discusses the design and development of an automatic monitoring of an indoor vertical hydroponic system. Three main components will be designed and developed to realize the system. Firstly, an indoor hydroponic module with IoT technology. Secondly, a dashboard for monitoring and managing the operation of the system. Finally, a two-factor authentication mechanism to enhance the security elements of the system. Preliminary results obtained showed promising progress and opportunity for the designed system.

Air pollution is always a problem that continuous to grow every time. Not only affects the environment, air pollution also affects human health so it needs to be dealt with quickly. Air quality in environment especially indoor area becomes something that is rarely considered. Even though we spend more time indoors, so it's also important to know the air we breathe. In today's modern era, air monitoring can utilize applications on our technology and internet devices, it can be an interesting and efficient thing to monitor. In this Internet of things project the monitoring system built by integrating software and hardware with device microcontroller and low-cost sensor MQ 135 and DHT 11. Node sensor produces data and processed with a microcontroller. The resulting data then immediately transmitted to the cloud by internet network using MQTT and combine with Node.js MQTT open source clients used to publish messages and subscribe to topics on brokers. Wireless Sensor Network also applied so sensor nodes can be placed anywhere and data can be acquired into one dashboard display. A web-based dashboard was built to facilitate access of information from existing air quality in different room.

Structural Health Monitoring (SHM) is concern with the safety of building which relies on sensor that can be strategically placed in building to monitor and record structural data. Wireless Sensor Network (WSN) is one of the supporting and attractive sensing technologies for SHM as it gives accurate, reliable, and healthier information to the users. In this paper, WSN communication is applied to monitor temperature, humidity, and structural parameter which includes vibration, force, and the sensor data stored in MySQL database. These stored data are visualized on dashboard. The original problem of SHM is to find the building damaged by monitoring the measured data and to reduce the cost without compromising the demand of safety.

With the widespread of research in the field of autonomous vehicles the value and impact of various simulators increase dramatically as they allow for quick and safe experimentation with the design of a vehicle, environment and driving scenarios. In this paper, the authors demonstrate how autonomous vehicle research and development can be facilitated by open-source robot simulators based on the experience gained from a robotized KAMAZ NEO truck industrial project. In particular, the Webots robot simulator was applied for 3D reconstruction of the experimental test-site for vehicle motion simulation and development of a web-based dashboard for controlling and monitoring the autonomous vehicle both in the simulation and the real-world.

Identifying interactions of vehicles on the road is important for accident analysis and driving behavior assessment. Our interactions include those with passing/passed, cut-in, crossing, frontal, on-coming, parallel driving vehicles, and ego-vehicle actions to change lane, stop, turn, and speeding. We use visual motion recorded in driving video taken by a dashboard camera to identify such interaction. Motion profiles from videos are filtered at critical positions, which reduces the complexity from object detection, depth sensing, target tracking, and motion estimation. The results are obtained efficiently, and the accuracy is also acceptable. The results can be used in driving video mining, traffic analysis, driver behavior understanding, etc.

International audience; When learning activities are enhanced by technologies, data can be collected in real time and can be represented in the form of dashboards on which teachers can rely for decision-making in the orchestration of their interventions. This paper presents Git4School, a dashboard for teachers offering visualizations based on data extracted from students’ Git repositories combined with complementary temporal contextual information. Based on a case study, a qualitative analysis shows how Git4School was used by teachers to guide their decision-making during face to face courses.

Water-logging in urban area is a phenomenon of water catastrophe because of rainstorm or continuous rainfall which exceeds the urban's drainage capacity. At present, the monitoring and detection on urban's water-logging rely a great deal on climate information. In this paper, we propose an IoT based water monitoring system that estimates water level progressively. Our system will operate with the water level sensor to indicate water level and GPS Module to specify which area of the city is water-logged due to heavy rainfall. This model depends on thought that the degree of the water can be a significant parameter with regards to the water-logging events particularly in a fiasco inclined place. In this paper, the method of water level monitoring and prediction is the use of wireless sensors powered by the Internet of Things (IoT) technology. A water level sensor and an Ultrasonic sensor is utilized to distinguish the ideal parameter, and if the water level arrives at the parameter, the sign will be a channel in real-time to a website that has been created. A cloud server was arranged as an information storehouse. The estimation of the water levels is shown in the remote dashboard and also in google map, the location of the streets will be pointed. The system has been developed to build up a smart street for the urban city for smooth working of the human day by day existence with forestalling wastage of vitality in a low budget.

No contexto atual da Indústria 4.0 e do controlo metrológico de produtos pré-embalados, procura-se que os dados provenientes de chão de fábrica sejam recolhidos de forma automática e que diferentes tipos de dispositivos digitais tenham acesso aos mesmos dados de forma ubíqua. Pretendeu-se desenvolver e integrar aplicações Web no sistema ACCEPT, sistema que permite a recolha, armazenamento, visualização e análise de dados de chão de fábrica. Neste artigo é descrito o processo de desenvolvimento das aplicações ACCEPT Quality Hub e ACCEPT Analytics e a sua integração neste sistema. info:eu-repo/semantics/publishedVersion

Clean Cooking is essential to maintain a healthy lifestyle. However, many people in developing economies do not have access to clean cooking. To promote clean cooking, first, we need to understand the cooking patterns in the household, and second, design interventions over those patterns. We also need to understand the grid and power supply readiness to support electricity-based clean cooking initiatives. In this paper, we provide an affordable and scalable energy monitoring system solution to instrument the cooking pattern in peri-urban Nepal. Our design consists of off-the-shelf power meters, minor changes in sockets/wiring at homes, data upload using cellular radio, and standard dashboard and analysis in the cloud. We deployed the system in 35 households in peri-urban Nepal and collected data from early August until the middle of October 2019. Our preliminary study indicates: 1) Cellular data access is a viable way to upload instrumentation data to the Internet in studies of this nature. 2) Data integrity and reliability are closely coupled with user behaviors and cellular reliability. 3) Deployment can be centralized instead of distributed, and cost can be affordable. 4) Continuous data collection from about three months shows poor power quality in the area.

Health information systems have been developed to help hospital managers steer daily operations, including key performance indicators (KPIs) for monitoring on a time-aggregated basis. Yet, current literature lacks in proposals of productivity dashboards to assist hospitals stakeholders. This research focuses on two related problems: (1) hospital organizations need access to productivity information to improve access to services; and (2) managers need productivity information to optimize resource allocation. This research consists in the development of dashboards to monitor information obtained from a hospital organization to support decision makers. To develop and evaluate the productivity dashboard, the Design Science Research (DSR) methodology was adopted. The dashboard was evaluated by stakeholders of a large Portuguese hospital who contributed to iteratively improving its design toward a useful decision support tool. Additionally, it was ascertained that monitoring productivity needs more study and that the dashboards on these themes are valuable assets at a monitoring level and subsequent decision-making process.

The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge&mdash, experience, skills, insights, intuition, judgment and know-how&mdash, into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain.

The population is getting old, and the use of technology has improved the quality of life of the senior population. This is confirmed by the increasing number of solutions targeting healthy and active ageing. Such solutions keep track of the daily routine of the elderly and combine it with other relevant information (e.g., biosignals, physical activity, social activity, nutrition) to help identify early signs of decline. Caregivers and elders use this information to improve their routine, focusing on improving the current condition. With that in mind, we have developed a software platform to support My-AHA, which is composed of a multi-platform middleware, a decision support system (DSS), and a dashboard. The middleware seamlessly merges data coming from multiple platforms targeting health and active ageing, the DSS performs an intelligent computation on top of the collected data, and the dashboard provides a user&rsquo, s interaction with the whole system. To show the potential of the proposed My-AHA software platform, we introduce the My Personal Dashboard web-based application over a frailty use case to illustrate how senior well-being can benefit from the use of technology.

International audience; This paper deals with energy efficiency in buildings in order to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some software exist to typically record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts, as well as adapted reports to the different stakeholders. The system designed in the sandfox project has for objective to fill this gap. To improve the energy management, an innovative system should aim at visualizing the streaming data, editing reports, and detecting anomalies, for different stakeholders, such as policy makers, energy managers , researchers, technical staff or end-users of these buildings. The paper presents the User-Centred Design approach that was used to collect the required needs from different stakeholders. The developed AI system is called sandman (semi-Supervised ANomaly Detection with Multi-AgeNt systems). It processes data in a time constrained manner to detect anomalies as early as possible. sandman is based on the paradigm of self-adaptive multi-agent systems. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.

With the increase of vehicles in large urban centers, there is also an increase in the number of traffic jams and accidents on public roads. The development of a proper Intelligent Transport System (ITS) could help to alleviate these problems by assisting the drivers on route selections to avoid the most congested road sections. Therefore, to improve on this issue, this work proposes an architecture to aid an ITS to detect, analyze, and classify the traffic flow conditions in real time. This architecture also provides a control room dashboard to visualize the information and notify the users about the live traffic conditions. To this end, the proposed solution takes advantage of computer vision concepts to extract the maximum information about the roads to better assess and keep the drivers posted about the traffic conditions on selected highways. The main contribution of the proposed architecture is to perform the detection and classification of the flow of vehicles regardless of the luminosity conditions. In order to evaluate the efficiency of the proposed solution, a testbed was designed. The obtained results show that the accuracy of the traffic classification rate is up to 90% in daylight environments and up to 70% in low light environments when compared with the related literature.

There are software solutions to solve most of the problems related to information management in any company or institutions, but still, there is a problem for transforming information into knowledge. Technological ecosystems emerge as a solution to combine existing tools and human resources to solve different problems of knowledge management. In particular, when the ecosystem is focused on learning processes associated with knowledge are named learning ecosystems. The learning ecosystem metamodel defined in previous works solves several problems related to the definition and implementation of these solutions. However, there are still challenges associated with improving the analysis and visualization of information as a way to discover knowledge and support decision making processes. On the other hand, there is a metamodel proposal to define customized dashboards for supporting decision-making processes. This proposal aims to integrate both metamodels as a way to improve the definition of learning ecosystems.

The passenger airbag (PAB) requires a large volume and fast deployment because of the large distance between the dashboard and the passenger. And various shapes and sizes of the PAB are required depending on the type of vehicle. However, since the PAB modeling process for each design change is complicated and time consuming, the design parameters of the PAB could not be well investigated. In this study, a unique feature-based CAD system has been proposed that easily constructs PAB CAD model and then generates PAB FE model for collision analysis. Main keypoints and widths of PAB that determine the shape and size have been extracted by analyzing the geometric-feature of airbag. The PAB CAD model can be easily constructed by inputting keypoints and widths information. Then, from the constructed PAB CAD model, the PAB FE model is automatically generated. Finally, the generated PAB FE model can be directly employed for collision analysis, thereby reducing the modeling time of the PAB and enabling efficient parametric studies on design.

Introduction To develop lifelong learning skills, students need feedback, access to performance data, and coaching. A new medical curriculum incorporated infrastructural supports based on self-regulated learning theory and the Master Adaptive Learner framework to engage students in reflection and learning planning. This study examines students’ experience with a performance dashboard, longitudinal coaching, and structured time for goal-setting. Methods Focus groups with first-year medical students explored performance dashboard usage, coaching and learning planning. We analyzed findings using thematic analysis. Results informed development of a 29-item survey rated strongly disagree (1) to strongly agree (5) to investigate experience with the dashboard, coaching and learning goals program. The survey was distributed to one first-year medical student class. We performed descriptive statistics and factor analysis. Results In three focus groups with 21 participants, students endorsed using the dashboard to access performance information but had trouble interpreting and integrating information. They valued coaches as sources of advice but varied in their perceptions of the value of discussing learning planning. Of 152 students, 114 (75%) completed the survey. Exploratory factor analysis yielded 5 factors explaining 57% of the variance: learning goals development (α = 0.88; mean 3.25 (standard deviation 0.91)), dashboard usage (α = 0.82; 3.36 (0.64)), coaching (α = 0.71; 3.72 (0.64)), employment of learning strategies (α = 0.81; 3.67 (0.79)), and reflection (α = 0.63; 3.68 (0.64)). Discussion The student performance dashboard provides efficient feedback access, yet students’ use of this information to guide learning is variable. These results can inform other programs seeking to foster lifelong learning skills. Electronic supplementary material The online version of this article (10.1007/s40037-018-0449-2) contains supplementary material, which is available to authorized users.

Road sign recognition is part of the automatic driver assistance systems implemented on the dashboard of vehicles. The recognition task is often carried out based on a classification procedure manipulating the detected signs. Classification tasks can be resolved by the use of multilayer artificial neural network systems. This article proposes an optimized real-time on-chip hardware implementation of multilayer perceptron system used for road sign classification. Four architectural approaches were described: on the one hand, the classic and the serial optimized architectures that offer a very significant reduction in hardware resources, and, on the other hand, the parallel and the optimized architectures, which offer a much reduced, time execution. In order to benefit from the advantages of the allocated hardware resources and the classification of the runtime process, these four architectures have been implemented on field programmable gate array Virtex-6 devices and their performances were quantified and evaluated according to a cost criterion. The energy dissipated by each of these architectures was measured; the achieved results have allowed us to conclude that the serial optimized architecture is the optimal solution, since it creates a tradeoff between the low cost, and the energy efficiency, and still real-time for the considered application.

We present an implementation of streaming telemetry of optical metrics within open hardware reconfigurable add/drop multiplexers (ROADMs). Our key achievements are sub-second updates of high-resolution spectrum scans, and we demonstrate a sustained telemetry stream of the full C-band with a sub-GHz resolution. The telemetry streaming is implemented over a standard, Internet Engineering Task Force (IETF)-defined protocol (YANG Push) in collaboration with an open-source YANG software stack and device-specific code. As the telemetry collector, we used a common time series database (TSDB) along with a visualization dashboard. We also extended the Open Network Operating System (ONOS) software-defined network (SDN) controller to act as a telemetry receiver.

Monitoring air quality is set to become more important at home, in the workplace and at social venues, particularly regarding promoting wellness and safeguarding social interaction. We present a didactic approach to implementing indoor air quality monitoring using an Internet of Things (IoT) solution, based upon low cost air quality sensors and edge computing nodes. We provide a tutorial that allows this solution to be replicated; similar solutions could have widespread use. Our test implementation monitored kitchen and study, each equipped with a Bosch BME680 sensor connected to a microcontroller for data transmission to a local server for storage on a database. A web based dashboard allowed for the feedback of sensor data. Two, 2-week data collection periods were undertaken to demonstrate the proof of concept. The first period was in the summer 2020 and the second in the autumn 2020 (during coronavirus ‘lockdown’ conditions). Analysis of the data showed a strong relationship between humidity and air quality (correlation coefficients of −0.624 in summer and −0.692 in autumn), with air quality degrading in the autumn. As humidity increases, air quality decreases; temperature has a weaker relationship with air quality. Further analysis showed that cleaning products can adversely impact on the air quality. Poor air quality can be mitigated by opening a window to speed up the dissipation rate of pollutants. The quantification of indoor air quality can inform activities such as cooking, heating, usage of disinfectants and monitoring of ventilation, which can potentially benefit of people with respiratory illness.

Objective: The aim of this study is to offer appropriate criteria to evaluate effective dashboards for healthcare organizations. Method: In a Delphi study, a team of information technology consultants were asked to determine a set of user interface requirements for evaluating, building or selecting the dashboard. In the first round, a list of main features or criteria to be used was determined based on the panel’s rating,. Results: In this study, it was revealed that a set of key criteria for evaluating the dashboards can be used for all types of dashboards. These criteria were classified into 7 main categories including user customization, knowledge discovery, security, information delivery, alerting, visual design, and integration and system connectivity. Conclusion: Choosing good criteria for selecting effective dashboards for healthcare organizations is very critical because these organizations follow a data-intensive and technology-driven environment. This study revealed the importance of key criteria which can guarantee development of an evaluation checklist.

Purpose The purpose of this paper is to draw attention to the potential of “small data” to complement research in learning analytics (LA) and to share some of the insights learned from this approach. Design/methodology/approach This study demonstrates an approach inspired by design science research, making a dashboard available to n=1,905 students in 11 study programs (used by n=887) to learn how it is being used and to gather student feedback. Findings Students react positively to the LA dashboard, but usage and feedback differ depending on study success. Research limitations/implications More research is needed to explore the expectations of a high-performing student with regards to LA dashboards. Originality/value This publication demonstrates how a small data approach to LA contributes to building a better understanding.

This paper aims to explain how the paired-cell overlapping loops of cards with authorisation (POLCA) system can be an efficient practice to coordinate the production flow in supply chains (SCs). For this purpose, it describes a case study using POLCA between connections in an automobile industry SC. For adequate functioning of the POLCA system, it is proposed the combined use of such system with electronic data interchange and just in sequence. The methodology used in this research was the Case Study. Thus, a proposal was developed based on literature and field research. After that, the proposal has been validated through simulation. The simulation shown in the present study reveals that the proposal has the potential to allow the company maintaining its supply with a significant inventory reduction, therefore with a better production flow coordination with its dashboard supplier. The paper revealed a new and very promising mechanism for a company coordinates its production flow: the POLCA/EDI/JIS logic. This research has shown that POLCA system logic can also be used to coordinate the production flow between a company and its suppliers. To the best of our knowledge, any paper in literature presented this use for the POLCA system.

Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.

This paper presents UXmood, a tool that provides quantitative and qualitative information to assist researchers and practitioners in the evaluation of user experience and usability. The tool uses and combines data from video, audio, interaction logs and eye trackers, presenting them in a configurable dashboard on the web. The UXmood works analogously to a media player, in which evaluators can review the entire user interaction process, fast-forwarding irrelevant sections and rewinding specific interactions to repeat them if necessary. Besides, sentiment analysis techniques are applied to video, audio and transcribed text content to obtain insights on the user experience of participants. The main motivations to develop UXmood are to support joint analysis of usability and user experience, to use sentiment analysis for supporting qualitative analysis, to synchronize different types of data in the same dashboard and to allow the analysis of user interactions from any device with a web browser. We conducted a user study to assess the data communication efficiency of the visualizations, which provided insights on how to improve the dashboard.

A cruise ship is a concentrate of technologies aimed at providing passengers with the best leisure experience. As tourism in the cruise sector increases, ship owners turned their attention towards novel Internet of things solutions able, from one hand, to provide passengers with personalized and comfortable new services and, from the other hand, to enable energy saving behaviors and a smart management of the vessel equipment. This paper introduces the E-Cabin system, a software architecture that leverages sensor networks and reasoning techniques and allows a customized cabin indoor comfort. The E-Cabin architecture is scalable and easily extendible, sensor networks can be added or removed, rules can be added to/changed in the reasoner software, and new services can be supported based on the analysis of the collected data, without altering the system architecture. The system also allows the ship manager to monitor each cabin status though a simple and intuitive dashboard, thus providing useful insights enabling a smart scheduling of maintenance activities, energy saving, and security issues detection. This work delves into the E-Cabin&rsquo, s system architecture and provides some usability tests to measure the dashboard&rsquo, s efficacy.

A modern city is characterized by many buildings for commercial and residential purposes. Disaster mitigation such as building fires, earthquakes, and so on should be taken into account in city management. Mitigation seeks to reduce the level of risks when a disaster occurs. A proper mitigation requires an early warning system to alert the city authority in order to handle a disaster effectively. This paper describes the prototype of a building monitoring system using an internet-of-things platform for supporting the disaster mitigation in the city. It employs a temperature sensor, a smoke sensor, and a vibration sensor to detect the condition of temperature, smoke concentration, and the presence or absence of vibrations as an indication of an earthquake respectively. All sensor data is pooled in XBee transmitter module to broadcast into XBee receiver module. The data is sent continuously through the ESP-8266 module such that an internet-of-things platform called Cayenne will proceed the data by displaying the condition of building and alerting a warning message. If there is an indication of a fire or earthquake, then such message will be displayed in the Cayenne dashboard and sent to the registered mobile phone number. Experimental results show the Cayenne dashboard can display data related to the condition of the building at any time and a warning message can be displayed whenever indications of a fire and earthquake.