Tomorrow's Smart Research Articles

Urban Odyssey: “Pioneering multimodal routes for Tomorrow's smart cities”

Getting around in modern cities has become a daily puzzle for both residents and travelers. As cities increasingly evolve into complex hubs of innovation and development, the demand for efficient transportation solutions has never been higher. In the multifaceted field of urban transportation, cost-effective and efficient mobility remains a high priority. This paper looks at how cities are planning better ways for people to travel and move within the growing scope of multimodal transportation, a paradigm shift beyond traditional single-mode transit systems. Our approach to improving urban transport revolves around a few key principles: integration, innovation, and collaboration. Integration means bringing together different modes of transportation – like buses, trains, bikes, and even new technologies like ride-sharing services to make it easier for people to switch between them. This transformative approach not only aims to reduce congestion and reduce environmental footprints but also prioritizes user experience while ensuring a harmonious blend of convenience, sustainability, and accessibility. We use new technology and ideas to ensure that travel is easy, quick, and good for the environment. Looking at new trends and examples from around the world, this overview shows how cities are shaping a better future for everyone's daily commute. In this paper, we use Lingo software to solve a numerical example related to multi-modal transportation, demonstrating practical solutions for real-world implementation. Through innovation, we can find creative solutions to urban transportation challenges. Additionally, public transportation enhancements, such as the introduction of synchronized bus routes and electric vehicle charging stations, underline the commitment to sustainability and inclusivity. In the dynamic urban transportation landscape, cities will revolutionize our approach to providing cost-effective and efficient mobility solutions.

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

Machine learning enabled learning based optimization algorithm in digital twin simulator for management of smart islanded solar-based microgrids

The key element of tomorrow's smart islanded microgrids (SMG) is Demand Side Management (DSM). Global energy awareness has led to an increase in SMGs' performance and peak balancing capabilities. DSMs are the control layout in these grids, and they aim at optimizing loads in various ways. The SMG includes batteries and distributed photovoltaics. This paper combines an Elephant Herding optimization algorithm (EHOA) and support sector machine (SVM) to the decision-making method in batteries for reducing the electric bill. EHOA could be an effective method for finding a nearly optimum solution to the load scheduling issue in order to reduce the consumers' expenses. Particularly, energy costs could be decreased if the consumer responds to prices that vary with the time of day. Therefore, The EHOA has been applied to assign the battery's optimum energy storage range. The SVM has been trained as a powerful machine learning method using optimal information from the EHOA. It has been employed to determine the amount of energy that has been transmitted into and out of batteries in order to take the lowest possible electric bills. In comparison to the current approach of 2.3 at the consumption of 8.2 kWh/day, the cost of the suggested method of mean generation oscillation (Gosc) has been equated to 2.27 dollars for the residential load. EHOA-SVM decreases 11.2% of energy costs, thereby assisting the decision-maker to balance the stability of demand-side measures.

Industrija 4.0 i njena primena u medicini i stomatologiji, kao i borbi protiv pandemije COVID-19

Basics: Industry 4.0 is a realized reality, which already today has a growing application in medicine and dentistry. So, at this moment, its application is especially important in the fight against covid-19. Aim of the paper: The purpose of this review paper is to understand the role of Industry 4.0 in the field of medicine and dentistry and to identify research directions, including future applications, especially for covid-19. Material and method: A detailed analysis of papers on Industry 4.0 in medicine, dentistry and related fields was performed and possible applications and directions of research were identified, especially for covid-19.Result: Through this review study, we identified different technologies, opportunities and research status of Industry 4.0 in the field of medicine, dentistry and covid-19 pandemic, through 19 areas of application. We can say that Industry 4.0 will become an essential part of tomorrow's smart factory of medical and dental devices. Conclusion: Industry 4.0 shows a wide range of possibilities for the production of newly adapted implants (medical and dental) and innovative tools and instruments for the field of medicine and dentistry. This model enables the creation of a digital clinic and digital office, with a complete system of medical and dental monitoring, which meets the individual requirements of the patient, thus creating the industry of medicine and dentistry with optimization of treatment time and costs. In the future, Industry 4.0 will create new capabilities and innovative protocols for the treatment and care of patients, both in medicine and dentistry.

Guest editorial: Artificial intelligence (AI)-driven spectrum management

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle (UAV) communication to smart cognitive networks and massive Internet of Things (IoT) systems. Enabling these emerging applications over the fifth generation (5G) of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management. In fact, most 5G applications will be highly reliant on intelligent spectrum management techniques, which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience (QoE). In this context, the use of artificial intelligence (AI) techniques that include deep learning, convolutional neural networks, and reinforcement learning, among many others, is expected to play a very important role in paving the way towards truly AI-driven spectrum management, thus enabling tomorrow's smart city services. Therefore, it has become imperative to investigate and apply AI techniques to solve emerging spectrum management problems in various wireless networks. This includes leveraging AI to address a wide range of wireless networking challenges ranging from network management to dynamic spectrum sharing and resource management.

Impact of additive manufacturing in different areas of Industry 4.0

Additive manufacturing (AM) technologies are becoming an essential part of tomorrow's smart factories, where software, technologies, and processes are being integrated to deliver world-class results. AM is to provide extensive inputs to smart manufacturing systems using digital design, and specialised software-based manufacturing technology. AM helps to create product innovation, thereby impacting different areas of Industry 4.0. The purpose of this study is to understand how AM can impact various areas of Industry 4.0. A large number of research papers on additive manufacturing in Industry 4.0 and other related articles in the manufacturing field have been studied to identify its fifteen significant impacts and subsequent research direction. In future, AM will create technological advancement in design, manufacturing and provide a maximum contribution in Industry 4.0 to the self-optimise production system. AM helps to reduce a considerable amount of unused inventory and helps fulfil the customised demand of consumer and market. AM creates any customised prototype for specific users in lesser time. It provides innovative product concepts by assessing market demand and minimise the supply chain cost.

Contour Based Tracking for Driveway Entrance Counting System

Managing vehicle in free-flow entrance is tiring to do manually by a guard control especially due to the increase in transportation demand. Providing an accurate vehicle counting approach is vital for traffic management and it will surely be an essential part in tomorrow's smart cities. Therefore, the main objective of this paper is to propose a more accurate vehicle counter by using the tracking and heuristic rules approaches. EzCam v1.0 is a vehicle surveillance system for a free-flow entrance where a module of vehicle counting based on proposed idea has been applied. The proposed method does not require high computational resources more than any relatively affordable non task specific hardware. It employs single threshold, contour extraction and sequential frame analysis and finally, vehicle counting process subsequently. The tracking-based method employs foreground object detection method and a mechanism for object filtering approach as compared to Chris Dahms approach which does not consider any object rejection and accept all contour information as relevant to be counted as vehicles. As a result, EzCam v1.0 which utilizes the exploited contour-based approach is able to achieve up to 94 percent of accuracy rate and outperforms the classic Chris Dahms method which obtained an accuracy of 88 percent. Therefore, the Exploited Contour based tracking method helps vehicle counting system to perform better accuracy in comparison to Chris Dahms approach.

Impact of additive manufacturing in different areas of Industry 4.0

Additive manufacturing (AM) technologies are becoming an essential part of tomorrow's smart factories, where software, technologies, and processes are being integrated to deliver world-class results. AM is to provide extensive inputs to smart manufacturing systems using digital design, and specialised software-based manufacturing technology. AM helps to create product innovation, thereby impacting different areas of Industry 4.0. The purpose of this study is to understand how AM can impact various areas of Industry 4.0. A large number of research papers on additive manufacturing in Industry 4.0 and other related articles in the manufacturing field have been studied to identify its fifteen significant impacts and subsequent research direction. In future, AM will create technological advancement in design, manufacturing and provide a maximum contribution in Industry 4.0 to the self-optimise production system. AM helps to reduce a considerable amount of unused inventory and helps fulfil the customised demand of consumer and market. AM creates any customised prototype for specific users in lesser time. It provides innovative product concepts by assessing market demand and minimise the supply chain cost.

Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview

Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultralow latency and reliable data analytics solutions that combine, in real time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this article, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level to overcome the ITS's latency and reliability challenges. With a higher capability of passengers' mobile devices and intravehicle processors, such a distributed edge computing architecture leverages deep-learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyberphysical security are investigated. Then, different deep-learning solutions for such challenges are revealed. The discussed deep-learning solutions enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the introduced edge analytics architecture, coupled with the power of deep-learning algorithms, provides a reliable, secure, and truly smart transportation environment.

Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance

Real-time traffic surveillance is essential in today's intelligent transportation systems and will surely play a vital role in tomorrow's smart cities. The work detailed in this paper reports on the development and implementation of a novel smart wireless sensor for traffic monitoring. Computationally efficient and reliable algorithms for vehicle detection, speed and length estimation, classification, and time-synchronization were fully developed, integrated, and evaluated. Comprehensive system evaluation and extensive data analysis were performed to tune and validate the system for a reliable and robust operation. Several field studies conducted on highway and urban roads for different scenarios and under various traffic conditions resulted in 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% length-based vehicle classification accuracy. The developed system is portable, reliable, and cost-effective. The system can also be used for short-term or long-term installment on surface of highway, roadway, and roadside. Implementation cost of a single node including enclosure is US $50.

Development of Smart Optical Gels with Highly Magnetically Responsive Bicelles.

Hydrogels delivering on-demand tailorable optical properties are formidable smart materials with promising perspectives in numerous fields, including the development of modern sensors and switches, the essential quality criterion being a defined and readily measured response to environmental changes. Lanthanide ion (Ln3+)-chelating bicelles are interesting building blocks for such materials because of their magnetic responsive nature. Imbedding these phospholipid-based nanodiscs in a magnetically aligned state in gelatin permits an orientation-dependent retardation of polarized light. The resulting tailorable anisotropy gives the gel a well-defined optical signature observed as a birefringence signal. These phenomena were only reported for a single bicelle-gelatin pair and required high magnetic field strengths of 8 T. Herein, we demonstrate the versatility and enhance the viability of this technology with a new generation of aminocholesterol (Chol-NH2)-doped bicelles imbedded in two different types of gelatin. The highly magnetically responsive nature of the bicelles allowed to gel the anisotropy at commercially viable magnetic field strengths between 1 and 3 T. Thermoreversible gels with a unique optical signature were generated by exposing the system to various temperature conditions and external magnetic field strengths. The resulting optical properties were a signature of the gel's environmental history, effectively acting as a sensor. Solutions containing the bicelles simultaneously aligning parallel and perpendicular to the magnetic field directions were obtained by mixing samples chelating Tm3+ and Dy3+. These systems were successfully gelled, providing a material with two distinct temperature-dependent optical characteristics. The high degree of tunability in the magnetic response of the bicelles enables encryption of the gel's optical properties. The proposed gels are viable candidates for temperature tracking of sensitive goods and provide numerous perspectives for future development of tomorrow's smart materials and technologies.

Meshing power quality and electromagnetic compatibility for tomorrow's smart grid

With the onset of the smart grid, electric utilities and their customers are engaged in millions of dollars of research on smart grid technologies. There are hundreds of smart grid topics being studied by dozens of organizations including the Electric Power Research Institute (EPRI). One of the topics in the electric utility industry that has been (and still is) of significant importance to utilities is power quality (PQ). The reliable and compatible performance of end-use equipment requires, in part, quality power at the power frequencies of 50 or 60 hertz up to about 3 kHz. Compatibility with the surrounding electromagnetic environment above 3 kHz (i.e., up to 10 GHz and beyond) is also a must in today's changing environment. Electromagnetic compatibility (EMC) is paramount if electronic loads - used to operate the grid and in customer facilities - are to interoperate with the power system and other loads on the grid. This article will help the reader to understand why EMC, bundled with PQ, must be co-addressed as the smart grid takes shape in the next few decades.

Next-Generation Monitoring, Analysis, and Control for the Future Smart Control Center

This paper proposes a vision of next-generation monitoring, analysis, and control functions for tomorrow's smart power system control centers. The paper first reviews the present control center technology and then presents the vision of the next-generation monitoring, analysis, and control functions. The paper also identifies the technology and infrastructure gaps that must be filled, and develops a roadmap to realize the proposed vision. This smart control center vision is expected to be a critical part of the future smart transmission grid.

An integrated strategy for training tomorrow's smart electric grid engineers in terms of electrical engineering

An integrated strategy for training tomorrow's smart electric grid engineers in terms of electrical engineering

How Today's Consumers Perceive Tomorrow's Smart Products*

This paper investigates consumer responses to new smart products. Due to the application of information technology, smart products are able to collect, process, and produce information and can be described as “thinking” for themselves. In this study, 184 consumers respond to smart products that are characterized by two different combinations of smartness dimensions. One group of products shows the smartness dimensions of autonomy, adaptability, and reactivity. Another group of smart products are multifunctional and able to cooperate with other products. Consumer responses to these smart products are measured in terms of the innovation attributes of relative advantage, compatibility, observability, complexity, and perceived risk. The study shows that products with higher levels of smartness are perceived to have both advantages and disadvantages. Higher levels of product smartness are mainly associated with higher levels of observability and perceived risk. The effects of product smartness on relative advantage, compatibility, and complexity vary across product smartness dimensions and across product categories. For example, higher levels of product autonomy are perceived as increasingly advantageous whereas a high level of multifunctionality is perceived disadvantageous. The paper discusses the advantages and pitfalls for each of the five product smartness dimensions and their implications for new product development and concludes with a discussion of the limitations of the study and suggestions for further research.