Smart Scenario Research Articles

NextGen Training for Medical First Responders: Advancing Mass-Casualty Incident Preparedness through Mixed Reality Technology

Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.

A 3D simulation framework with ray-tracing propagation for LoRaWAN communication

The adoption of Low-Power Wide-Area Networks (LP-WAN) for interconnecting remote wireless sensors has become a reality in smart scenarios, covering communications needs of large Internet of Things (IoT) deployments. The correct operation and expected performance of such network scenarios, which can range hundreds or thousands of nodes and tens of squared kilometres, should be assessed before carrying out the deployment to save installation and maintenance costs. Common network planning tools can help to roughly study potential coverage, but network simulation offers fine-grained information about network performance. Nevertheless, current simulation frameworks include limited propagation models based on statistical and empirical measurements that do not consider scenario particularities, such as terrain elevation, buildings or vegetation. This is critical in urban settings. In this line, this paper presents a simulation framework including a network simulator, a 3D engine and a ray-tracing tool, which models realistically the performance of Long-Range Wide-Area Network (LoRaWAN) communication technology. We have evaluated the performance of the solution taking as reference experimental campaigns in the city of Cartagena (Spain), comparing data obtained when simulating with the commonly employed propagation models such as Okumura–Hata or path loss. Results indicate that our framework, set-up with data from open geographical information systems, accurately fits experimental values, reporting improvements between 10% and 50% in the error committed when estimating signal strength in challenging urban streets with signal obstruction, as compared with the better performing classical model, Okumura–Hata.

Smart city and smart destination planning: Examining instruments and perceived impacts in Spain

The impact of technology on tourist cities and destinations has led to the emergence of renewed management approaches that seek to adapt the planning processes to new challenges and opportunities derived from the smart scenario. The smart city and smart tourist destination approaches are aimed at improving efficiency in management, the quality of life of the residents and the tourist experiences. However, little is known about how these ideas are being translated into real policies and whether they are having a real impact. The objective of this paper is to understand how the smart approach is being deployed in the planning processes of Spanish tourist cities and destinations, and its implications in terms of the governance, sustainability and data-driven public management. The planning instruments that guide the smart strategies of different Spanish cities are identified and analysed. This is complemented with a questionnaire administered among managers of the smart city and smart destination initiatives. The findings reveal the diversity of smart initiatives, their benefits and limitations. The results contribute to generating a necessary debate on the implications of the smart discourse for urban and tourism planning and enrich the international debate around this approach.

Regional Electric Vehicle Energy Consumption and Carbon Emissions in Great Britain

This work presents the regional differences in electric vehicle (EV) real-world energy consumption and associated carbon emissions during charging in Great Britain (GB). A model was developed considering the variability in road traffic, ambient temperature, and electricity grid profile between the GB regions on EV carbon emissions under uncontrolled and smart scenarios. The results show the variations in EV energy consumption and carbon emissions impacted by where, when, and how an EV is driven and charged. Carbon emission reduction varies from 5% to 33% between the regions when switching to delayed smart charging, shifting the charging process outside peak hours. An optimised smart charging that moves the charging events to periods of low grid carbon intensity reduces carbon emissions from 6% to 55%, affected by region grid carbon intensity and energy consumption.

Edge, fog, and cloud computing in IoT-Significance and security concerns

Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.

Creative human capital and assessment of its manifestation in organizational behavior in the context of digitalization higher education

Problem and aim. In the context of the digitalization organization management human creativity and its properties and evaluation have become the leading topic of modern research in the field of organizational behaviour and organizational development. This problem is multi-vector, it is considered in different branches of knowledge. Human capital (HC) is the most valuable resource of the digital society, and the creativity of HC is the driving force of scientific progress. This is especially relevant for the higher education system, since it is here that the foundations of professional competencies and personal characteristics of future labour market actors are laid. The purpose of the study is to identify the features of creative human capital and to consider the possibilities of assessing its manifestation in organizational behaviour in the context of digitalization of higher education. Research methods. It was used the methods of monographic and statistical analysis, as well as the author's methodology based on a modified system of indicators (R&D potential; innovation potential; creative potential). The methodology uses dynamic and equity coefficients in order to universalize the method and the possibility of evaluating creative HC for different objects and different conditions. The authors’ methodology uses a modified system of indicators, which are presented in national statistical systems and which group dynamic and share coefficients according to their functional purpose. Results. The study is both theoretical and applied, as it contributes equally to the theory and practice of organizational behavior. In the theoretical part, the authors considered four key issues: 1) human capital and creativity; 2) the concept of creative HC in the digital economy; 3) key trends in the development of higher education in the global economy; 4) increasing role of creative HC in organizational behavior in the context of digitalization higher education and methods for its assessment. In the practical part, the authors set and realized the goal of developing a universal methodology for assessing creative HC. The calculations of creative HC use the example of the higher education sector in Russia and Belarus. Conclusion. The authors have developed a methodology that can be used can be used for different subjects (countries, supranational entities, regions, organizations) and in different conditions (for example, digitalization higher education). Based on the analysis of scientific works on modern forms of HC reproduction, the authors propose main directions for smart scenarios of the HC development, especially relevant for the higher education system.

FARDA: A fog-based anonymous reward data aggregation security scheme in smart buildings

Nowadays the Internet of Things (IoT) for smart city is prevalent around the world, named as City IoT. Since building is the key facility in the city, smart building (SB) plays an important role in smart city. With the development of new AI technology and expansion of clients’ demand, smart buildings spread gradually in cities. Thereby, amounts of edge devices are accessed to the City IoT leading to the computation resources explosion and security issues in the smart environment. Here, we study the security of the data sensing in smart IoT and propose a novel data aggregation scheme, named Fog-based Anonymous Reward Data Aggregation scheme (later referred to as FARDA), to preserve the user privacy for developing a sustainable City IoT system. Specifically, this proposed scheme merges the AI based blockchain for enhancing the security on the edge devices and fog computing for the energy sustainability. More, FADRA attracts mobile sensing users and fog nodes by an anonymous reward mechanism in the blockchain framework. The framework leverages the provable Monero’s blockchain to endow two privacy requirements: (i) unlinkability between the payer’s address and the payee’s address and (ii) the non-traceability of the transaction sources. Moreover, two PKCs in the blockchain guarantee the benefits of sensing data participants theoretically. We verify the security properties of FARDA in theory and evaluate the efficiency for data sensing in smart environments on smartphones by experiments. Our study paves a way for broad researches on sustainable IoT in smart scenarios.

Impacts of electric vehicle fast charging under dynamic temperature and humidity: Experimental and theoretically validated model analyses

Toward automobile electrification and automation, a smart scenario of DC-charging plug-in electric vehicles (PEVs) at any parking lot equipped with chargers is proposed. In this paper, this scenario is composed of four main stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process of the PEVs is implemented using the constant current-constant voltage (CC-CV) protocol. This was followed by a novel PEV classification model under the impacts of various ambient circumstances. Then an estimation of the charging characteristic parameters at the corresponding conditions is obtained. Finally, the model identification of the battery dynamic behaviour is sufficiently proposed. The feedforward backpropagation neural network (FFBP-NN) as a supervised classification algorithm supported by the statistical analysis of an instant charging current sample is used, which achieves an accuracy of 83.2%. In addition, the FFBP-NN perfectly estimated the charging current, terminal voltage, and charging interval time with a maximum error of 1%. Eventually, a sufficient identification model of the battery dynamic behaviour based on the Hammerstein-Wiener (HW) model is introduced with the best fit of 89.62% and an error of 1.1876%. The experimental and simulated results are within 1%error with the preceding research literature.

Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream

Deep neural network-based computer vision applications have exploded and are widely used in intelligent services for IoT devices. Due to the computationally intensive nature of DNNs, the deployment and execution of intelligent applications in smart scenarios face the challenge of limited device resources. Existing job scheduling strategies are single-focused and have limited support for large-scale end-device scenarios. In this paper, we present ADDP, an adaptive distributed DNN partition method that supports video analysis on large-scale smart cameras. ADDP applies to the commonly used DNN models for computer vision and contains a feature-map layer partition module (FLP) supporting edge-to-end collaborative model partition and a feature-map size partition (FSP) module supporting multidevice parallel inference. Based on the inference delay minimization objective, FLP and FSP achieve a tradeoff between the arithmetic and communication resources of different devices. We validate ADDP on heterogeneous devices and show that both the FLP module and the FSP module outperform existing approaches and reduce single-frame response latency by 10–25% compared to the pure on-device processing.

Multifunctional biomimetic tactile system via a stick-slip sensing strategy for human–machine interactions

A tactile sensor system enables natural interaction between humans and machines; this interaction is crucial for dexterous robotic hands, interactive entertainment, and other smart scenarios. However, the lack of sliding friction detection significantly limits the accuracy and scope of interactions due to the absence of sophisticated information, such as slippage, material and roughness of held objects. Here, inspired by the stick-slip phenomena in the sliding process, we have developed a multifunctional biomimetic tactile system based on the stick-slip sensing strategy, which is a universal method to detect slippage and estimate the surface properties of objects by sliding. This system consists of a flexible fingertip-inspired tactile sensor, a read-out circuit and a machine-learning module. Based on the stick-slip sensing strategy, our system was endowed with high recognition rates for slippage detection (100.0%), material classification (93.3%) and roughness discrimination (92.8%). Moreover, robotic hand manipulation, interactive games and object classification are demonstrated with this multifunctional system for comprehensive and promising human–machine interactions.

Smart Speech Recognition System for Chinese Language Learning Enhancement

With the expansion of teaching scale and the rapid development of educational information, a smart classroom management system based on speech recognition has been proposed and developed to improve the information level of smart classroom management. This paper discusses the application of multimedia equipment control. The system relies on the mature campus network in the way of cloud and local speech database for speech recognition. The application of the system proves that the smart classroom management system based on speech recognition in cloud architecture has more advantages than the traditional multimedia classroom management system and also has definite expansibility. It facilitates the unified management of the school, improves the efficiency of administrators, saves a lot of human and financial resources, and greatly promotes the development of school information construction. The smart classroom becomes a new direction in the application of information technology in the field of teaching and learning and is a smart learning scenario for improving the learning and teaching scenario. This paper focuses on the use of smart classroom as a supplementary teaching tool to improve Chinese language teaching and learning and illustrates the optimization of the teaching environment by smart classroom in terms of vocabulary learning and listening and speaking training.

Transparent absorber composed of two stacked ultrathin metal films perforated with small holes.

A transparent absorber refers to the device which can absorb light strongly within a narrow frequency range but transmit light efficiently outside that range. Because of the contradiction between absorption and transmission, however, the performances of the transparent absorbers are usually compromised. In this work, we propose a transparent absorber based on a sandwiched metal-insulator-metal (MIM) structure, i.e., two perforated ultrathin metal films separated by a central dielectric layer. This structure has the advantage that the narrow-band absorption can be greatly enhanced because of the cooperation of surface-plasmon polariton (SPP) mode and multiple reflections in the dielectric cavity. Moreover, the ultrathin thickness of the stacked metal films enables high transmission when the wavelength of incident light deviates from the SPP resonance. A semi-analytical Fabry-Perot model has been employed to describe the optical properties, which agrees well with the simulation. The dependence of optical properties on the structural parameters has also been studied systematically. In addition, by covering the transparent absorber with an antireflection layer, highly efficient absorption of red (∼87% @ 629 nm), green (∼89% @ 524 nm), or blue (∼68% @ 472 nm) light and high transmission (∼80%) in the transparent region have been suggested. With its excellent visible-wavelength selective absorption, polarization independence, high angle-tolerance, and structural simplicity, the proposed MIM transparent absorber may have potential applications in the display technology and other smart scenarios.

Blockchain-based solution for energy demand-side management of residential buildings

Smart homes, connected through a network, can optimize the energy consumption and general load shape of their area. In this work, a blockchain-based smart solution is presented for demand-side management of residential buildings in a neighborhood to improve Peaks to Average Ratios (PAR) of power load, reduce energy consumption, and increase the thermal comfort of occupants by modeling heating, illumination, and appliance systems. For real-time power and temperature monitoring of the neighborhood, a transient numerical physical model has been developed. The simulator has been validated with data measured from a building in Northern Italy. Then, a neighborhood with 2,000 households has been modeled for different occupancy patterns, initial values, and boundary conditions. Two different control scenarios, namely basic and smart, have been considered. In the basic scenario, everything is managed by occupants except the boiler, which is controlled by the indoor temperature of the home. Instead, in the smart scenario, a blockchain-based network has been introduced for buildings to exchange a parameter called the Probability of the Next Hour (PNH). Ethereum Solidity has been deployed for smart contract development in the blockchain. The results show that using blockchain-connected smart controllers aimed at demand-side management can improve PAR, comfort level, and energy efficiency of buildings, which can bring about CO2 reduction on an urban and even global scale.

Research on Guilin Smart Cultural Tourism Service Scenario based on Digital Twin Technology

In recent years, with the emergence of new technologies such as cloud computing, the Internet of Things, big data, and artificial intelligence, the country has deeply promoted the application of science and technology in the cultural and tourism industries, targeting smart cultural tourism, digital cultural industries, online and offline integrated development, and scenic spots. Multiple service scenarios such as smart supervision put forward new requirements. The purpose is to use digital twin technology to build a digital, online, and intelligent cultural tourism industry chain, optimize Guilin tourism service processes, improve service efficiency, and make full use of the city's smart cultural tourism development to promote the improvement of the city's image and promote Guilin The new form of global tourism forms the city's endogenous development capabilities, and provides theoretical and practical guidance for the construction of smart cultural tourism in other cities. By analyzing the current status of smart cultural tourism and application requirements, it discusses the design and construction of Guilin smart cultural tourism service scenarios based on digital twin technology, and how to strengthen the realization of future-oriented smart interactive scenarios in the innovation of service models.

A Clonal Selection Optimization System for Multiparty Secure Computing

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

Performance Analysis of the IEEE 802.15.4 Protocol for Smart Environments under Jamming Attacks.

Jamming attacks in wireless sensor networks (WSNs) scenarios are detrimental to the performance of these networks and affect the security and stability of the service perceived by users. Therefore, the evaluation of the effectiveness of smart environment platforms based on WSNs has to consider the system performance when data collection is executed under jamming attacks. In this work, we propose an experimental testbed to analyze the performance of a WSN using the IEEE 802.15.4 CSMA/CA unslotted mode under jamming attacks in terms of goodput, packet receive rate (PRR), and energy consumption to assess the risk for users and the network in the smart scenario. The experimental results show that constant and reactive jamming strategies severely impact the evaluated performance metrics and the variance’ of the received signal strength (RSS) for some signal-to-interference-plus-noise ratio (SINR) ranges. The measurements obtained using the experimental testbed were correlated with analytical models. The results show that in the presence of one interferer, for SINR values higher than dB, the PRR is almost , and the goodput Kbps, but the system performance is significantly degraded when the amount of interferers increases. Additionally, the energy efficiency associated with reactive strategies is superior to the constant attack strategy. Finally, based on the evaluated metrics and with the proposed experimental testbed, our findings offer a better understanding of jamming attacks on the sensor devices in real smart scenarios.

Guest Editorial: IoT and the Environment

The Internet of Things (IoT) is having a tremendous impact on all sectors of industry, affecting global society. IoT deployments provide industrial settings with sensing and control capabilities at high resolution, hence enabling new levels of resource optimization and efficiency. IoT technology is also one of the core technologies enabling various smart scenarios, such as smart homes, smart cities, and smart regions, aiming to meet the needs of modern citizens more effectively and efficiently. IoT is already embedded within cars, trucks, locomotives, and aircraft to reduce maintenance costs and improve transportation safety. On a personal level, IoT-based devices provide customized health and fitness monitoring, letting users keep track of their physical activities and estimating caloric needs based on the latter.

Optical-transparent metasurface for flexible manipulation and analog information modulation.

Recently, optically-transparent metasurface based on indium tin oxide (ITO) film has attracted wide attention due to its remarkable optical and electromagnetic characteristics. However, most previous researches on the ITO film mainly focus on the absorption because of its prominent loss-resistance property, but neglecting the further exploration on programmable functions. Here, we present a programmable metasurface based on an optically-transparent ITO glass, on which varactors are integrated to achieve flexible amplitude manipulation range of about 25 dB. More importantly, the presented programmable design can be applied for direct modulation on the carrier incident wave with the desired pre-designed analog wave-form. Within the 10 MHz modulation speed, both programmable amplitude manipulation and analog information modulation are demonstrated in the measurements, showing good agreement with theoretical analysis and simulations. Combining both optical transparency and programmable modulation capability, the presented metasurface will promote the potential applications in wireless communication, internet of things and other smart scenarios.

An Optimal Energy-Saving Home Energy Management Supporting User Comfort and Electricity Selling With Different Prices

In this study, we investigate the operation of an optimal home energy management system (HEMS) with integrated renewable energy system (RES) and energy storage system (ESS) supporting electricity selling functions. A multi-objective mixed integer nonlinear programming model, including RES, ESS, home appliances and the main grid, is proposed to optimize different and conflicting objectives which are energy cost, user comfort and PAR. The effect of different selling prices on the objectives is also considered in detail. We further develop a formula for the lower bound of energy cost to help residents or engineers quickly choose best parameters of RES and ESS for their homes during the installation process. The performance of our system is verified through extensive simulations under three different scenarios of normal, economic, and smart with different selling prices using real data, and simulation results are compared in terms of daily energy cost, PAR, user's convenience and consecutive waiting time to use appliances. Numerical results clearly show that the economic scenario achieves 51.6% reduction of daily energy cost compared to the normal scenario while sacrificing the user's convenience, PAR, and consecutive waiting time by 49%, 132%, and 1 hour, respectively. On the other hand, the smart scenario shows only slight degradation of user's convenience and PAR by 2% and 18%, respectively while achieving 46.4% reduction of daily energy cost and the same level of consecutive waiting time. Furthermore, our simulation results show that a decrease of selling prices has tiny impacts on PAR and user comfort even though the daily energy cost increases.

A Framework for Off-Line Operation of Smart and Traditional Devices of IoT Services.

Recently, with the continuous evolution of information technology, various products such as Building Information, Internet of Things (IoT), Big Data, Cloud Computing and Machine Learning have been developed and have created a lifestyle change. A smart Internet of Things (IoT) system is formed by combining the communication capabilities of the internet with control, monitoring and identification services to integrate people, things and objects. However, in some IoT environments that have a weak signal, such as remote areas, warehouses or basements, the network may become unstable, meaning that the IoT system is unable to provide efficient services. This paper therefore presents a framework that ensures the reliability of IoT system services so that even if the IoT system cannot connect to the network, the system can provide the services offline. To avoid increasing the installation cost or replacing existing traditional devices with modern smart devices, this framework can also be used to control traditional devices. The system operation is convenient because users can operate all their smart and traditional devices under the IoT system through voice commands and/or a handheld microcontroller, thus reducing the manual operation of the user. The framework proposed in this paper can be applied to various smart scenarios, including smart warehouses, smart restaurants, smart homes, smart farms and smart factories, to improve people’s quality of life and convenience, and create a humane and comfortable smart living environment.