SMART-1 Research Articles

Transforming smart homes via P2P energy trading using robust forecasting and scheduling framework

With the advent of smart grids, advanced information infrastructures, advanced metering facilities, bidirectional exchange of information, and battery storage home area networks have all transformed the electricity consumption and energy efficiency. There is a significant shift in the energy management structure from the traditional centralized infrastructure to the flexible demand side driven cyber-physical power systems with clean energy and energy storage system. These changes have significantly evolved the home energy management (HEM) space. Consequently, stakeholders must define their responsibilities, create efficient regulatory frameworks, and test out novel commercial strategies. P2P energy trading appears to be a feasible solution in these circumstances, allowing users to trade electricity with one another before becoming completely reliant on the utility. P2P energy trading offers a more stable platform for energy trade by facilitating the exchange of energy between prosumers and consumers. This research proposes a novel demand and generation prediction techniques of P2P HEMS for optimal energy using the Multi-Objective Optimization model. An enhanced Wild Horse Optimization technique was first used to summarize historical records' qualities. Then, the Bi-LSTM is used to predict the demand and generation values. Furthermore, a Grasshopper optimization (GHO) approach is employed to fine-tune the model's hyperparameters. The P2P HEMS demand and generation prediction framework is offered with a probabilistic and fault evaluation that upholds load flow balance between need and supply for continuous operations. It results in an intelligent community transforming cities into smart ones, opening new avenues for scientific research in terms of technological developments.

RETRACTED: Enhancing urban energy storage and optimization through advanced EV charging and vehicle-to-grid integration in a renewable-based zero-energy city

The variable natures of renewable power sources and the additional demand from EV (electric vehicle)-charging present unique challenges to electrical grids. This paper explores the potential for optimized energy synchronization at an urban scale in a city striving for net-zero energy status, utilizing wind and solar sources. It examines three scenarios for EV charging: smart, opportunistic, and V2G (vehicle-to-grid), with a specific city as the case study. The research aims to decline the discrepancy among energy supply and demand through intelligent charging and V2G strategies, formulated within the framework of quadratic programming. The analysis indicates that V2G integration, in conjunction with a balanced mix of wind and solar power, significantly enhances load matching in a zero-energy urban context. The transition from basic opportunistic charging to smart mode and then to V2G progressively improves the energy alignment. Within the ideal NZES (net-zero energy system), load matching-efficiency enhances from 67.6 % with opportunistic plan to 72.5 % with smart ones, and significantly to 83.4 % with V2G integration. Further, the full utilization of EV flexibility, particularly through an integrating a battery for EV (2400 MWh) in V2G framework as an alternating ESS (energy storage system), was equivalent to having a 1412 MWh stationary ESS in improving load-matching at an urban scale. The study also highlights the comparative effectiveness of EV batteries in a V2G configuration versus stationary ESSs in optimizing urban-scale energy-matching. These insights demonstrate the substantial potential of EVs to introduce flexibility and resilience into urban energy systems, facilitating the transition to sustainable urban living.

Internet of Things (IoT) in Healthcare

In this modern era, we have seen increased number of Heart disease Asthma, Diabetes and Mental health patients. So, in these conditions, IOT play a crucial role in this type of disease. Through the integration of sensors, software, and actuators for tracking and monitoring purposes into patients and their medications, smart healthcare plays a vital part in healthcare applications. Through the use of sensors, clinical care uses the Internet of Things to monitor patients' physiological status. Data is collected, evaluated, and sent remotely to processing centers so that appropriate actions can be taken. People's lifestyles are being significantly impacted by IOT devices. This growth is mostly being driven by interest in health and wellbeing. This approach has the potential to expedite the transformation of hospitals into smart ones and help physicians identify and recommend appropriate wearable IOT devices. This is not only for patients, it also useful for normal people to check own health status by using wearable IOT devices. The design and development of dependable and reasonably priced wireless sensing device based on the Node MCU microcontroller for the real-time collection of health vital signs including heart rate and body temperature. their engagement in healthcare benefit conveyance.

A Design of Digital Bag for Pilgrim's Safety

The study aimed to design digital bags that comply with the functional requirements of Hajj and Umrah pilgrims in the light of ergonomics and then to identify the differences between specialists in achieving design patterns of bags made for pilgrims' safety. It also aimed to measure the extent to which Hajj and Umrah pilgrims are satisfied with the proposed designs. The research employed a mixed-methods approach, with a study sample of (301) individuals who had participated in Hajj and Umrah pilgrimages. The study's findings indicated statistically significant variations, at a significance level of (0.01) among the six suggested designs for smart bags, based on the feedback from Hajj and Umrah pilgrims. When testing the direction of significance, the two researchers found that design no. 1 was the best among all designs, followed by design no. 3, then design no. 6. Results also showed statistically significant differences at the level of (0.01) among the six suggested designs for smart bags based on the experts' assessments. When testing the direction of significance, the researchers found that design no. 1 stood out as the optimal choice, followed by design no. 6, then design no. 2. The study concluded that the ergonomic standards should be applied in the smart design, taking into consideration the safety factor, as the two researchers followed the ergonomic standards within the framework of designing smart bags through several elements, namely locating the bag, combating thefts, and enabling bags with RFID technology to identify the user's identity. The study provided an overview of recent trends in the ergonomic field to improve human performance and achieve product development. It resulted in a practical guide to understanding the ergonomic guidelines in smart design. The two researchers recommended considering the ergonomic aspects of fashion design and its supplements, especially the smart ones. They also recommended conducting several studies dealing with the impact of ergonomic considerations on fashion design and its supplements.

An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

Fusion of Environmental Sensors for Occupancy Detection in a Real Construction Site

Internet-of-Things systems are increasingly being installed in buildings to transform them into smart ones and to assist in the transition to a greener future. A common feature of smart buildings, whether commercial or residential, is environmental sensing that provides information about temperature, dust, and the general air quality of indoor spaces, assisting in achieving energy efficiency. Environmental sensors though, especially when combined, can also be used to detect occupancy in a space and to increase security and safety. The most popular methods for the combination of environmental sensor measurements are concatenation and neural networks that can conduct fusion in different levels. This work presents an evaluation of the performance of multiple late fusion methods in detecting occupancy from environmental sensors installed in a building during its construction and provides a comparison of the late fusion approaches with early fusion followed by ensemble classifiers. A novel weighted fusion method, suitable for imbalanced samples, is also tested. The data collected from the environmental sensors are provided as a public dataset.

Does the world belong to the smart ones? Dishonest behavior in business schools

Purpose: the main objective of this study is to analyze the meanings and perceptions of graduate program students (master's and doctoral students) in Business Administration, as well as professors of graduate programs (PPGs) from different institutions, in relation to possible practices of dishonest behavior and plagiarism in the academic context. It highlights the causes, consequences and meanings of possible practices of dishonest behavior and plagiarism in the academic context, using the theoretical framework of possible reasons for plagiarism and dishonest behavior in this context. Design/methodology/approach: the perceptions of 28 professors and students were analyzed, extracted from semi-structured interviews and questionnaires, and later analyzed using Flores' categorical analysis, where the qualitative data were reduced, categorized, and coded, capturing the relevant meanings. Findings: the study highlights the phenomenon of academic dishonesty through reports from professors and students, listing four metacategories: meaning and perception of the phenomenon, causes, consequences and forms of combat, which in turn generated seven other categories. Practical implications: possible causes and implications of plagiarism are discussed, as well as ways to combat or mitigate the impact of these acts in Business Administration teaching programs, fostering the debate about these deleterious practices in the academic context. Originality/value: the paper highlights evidence of dishonest behavior in the academy, which is sometimes overlooked by several stricto sensu management postgraduate programs.

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational process, considering transparency in its privacy, data protection, and disaster recovery, it needs state-of-the-art systems and methods to tackle the evolving environment. This research aims to improve the security of IoT devices by investigating the likelihood of network attacks utilizing ordinary device network data and attack network data acquired from similar statistics. To achieve this, IoT devices dedicated to smart healthcare systems were utilized, and botnet attacks were conducted on them for data generation. The collected data were then analyzed using statistical measures, such as the Pearson coefficient and entropy, to extract relevant features. Machine learning algorithms were implemented to categorize normal and attack traffic with data preprocessing techniques to increase accuracy. One of the most popular datasets, known as BoT-IoT, was cross-evaluated with the generated dataset for authentication of the generated dataset. The research provides insight into the architecture of IoT devices, the behavior of normal and attack networks on these devices, and the prospects of machine learning approaches to improve IoT device security. Overall, the study adds to the growing body of knowledge on IoT device security and emphasizes the significance of adopting sophisticated strategies for detecting and mitigating network attacks.

Impact of digital innovations to manage the challenges of COVID-19 lockdown: A study based on the lessons from Indian organizations

The purpose of this study is to identify and analyse the impact of digital innovations to manage business operations during COVID-19 lockdown. Due to COVID-19, a nation-wide lockdown affected the Indian economy severely as both service and manufacturing organizations were closed for a long duration. Thus, to manage with these challenges and address the expectations of their employees, organizations identified various artificial intelligence (AI) and cloud-based digital innovations to continue their business operations during lockdown. The study used a systematic review of literature and document analysis to unravel such innovations that have been initiated and implemented in Indian organizations during COVID-19 lockdown. The study finds that digital innovations and strategies are useful for coping with unseen challenges and also can transform the organizations into smarter ones. The study proposes a conceptual framework on the basis of findings, which will be used for futuristic studies.

Behind the Saying “Landsbergis Destroyed the Collective Farms”: Transformation of Agriculture from the Perspective of Cultural Trauma Narrative

This article explores what is told about the supposed “collapse” of the collective farms in Lithuania, how is the negative connotation explained, and how is it attuned to the negative understanding of collectivization and collective farms in general. Relying on the theoretical literature dedicated to narrative and trauma, a scheme of cultural trauma narrative is formulated as a conceptual tool. Through narrative analysis of qualitative interviews collected in Panevėžys, Jonava, and their vicinities in 2021, it is demonstrated that the collective farm of early Soviet times is seen negatively, but the collective farm of late Soviet times is defined as a consistent unit. During transformation, this unit is suddenly disintegrated. Ordinary people are defined as the main victims. The named perpetrators can be divided into two groups. The first one is local people: the former chairs of the collective farms and other local leaders are mostly mentioned, as well as the “smarter ones” (“gudresnieji”). The second group is comprised of National government and Vytautas Landsbergis (personally); they are assigned a fatal role. A group of “critics” has been analytically distinguished. These informants have been harshly critical of collective farms regardless of times and distanced themselves from parlance regarding the “collapse” of collective farms. However, some of the “critics” repeated some elements of the cultural trauma narrative.

Interoperability Testing of a Smart Home Automation System under Explicit Demand Response Schemes

Interoperability becomes a key issue for smart grid systems, as the interaction between diverse components needs to lead to a normal system operation. In this paper, we test interoperability issues with respect to home automation. In particular, interaction of a home energy management system (HEMS) is examined with an external actor for home/building remote control. We show the importance and the feasibility of remotely controlling domestic loads from outside the house premises, which can be crucial for energy saving operations, such as demand response. The Smart Grid Architecture Model (SGAM) is used, where the different actors are depicted. The interoperability testing methodology for smart grids, developed by our unit, is followed in order to design the necessary tests and execute them. For the experimental part, we develop an HEMS in our lab along with a Home Automation End Device (HAED), used to transform two normal plugs, and consequently, normal loads into smart ones, thus creating a system for home automation and control. The described configuration is only one possible configuration out of the available ones existing in the market for home automation. LabVIEW programming is used in order to realize the actual explicit demand response program through remote load control and scheduling. The results show that explicit demand response can be achieved by an external actor with success and interoperability is preserved.

Intelligent home energy management using Internet of Things platform based on NILM technique

Due to the continuous increase in the global energy demand, it is essential to find solutions to improve energy efficiency. Non-intrusive load monitoring (NILM) is one of the most prominent energy management techniques, and it allows consumers and companies to manage their energy consumption efficiently. The energy footprint of residential buildings is monitored, and the total electrical consumption is disaggregated into appliance related signals through NILM. The NILM applications are working based on the “energy awareness” concept where the consumer receives some itemized information from the application to reduce its consumption and thus improves energy efficiency. This paper presents an Internet of Things (IoT) based criterion by using NILM to transform modern buildings and homes into energy-efficient and smart ones. The Factorial Hidden Markov Model (FHMM) is used as a NILM technique to disaggregate all appliances’ total power consumption into individual appliance load consumption by giving the power as an input. The FHMM is applied on reference energy disaggregation dataset (REDD) datasets, and a comparison is made between the measured and predicted values of appliances’ power consumption. Instant visualization of live data is then created using the ThingSpeak platform, which sends alerts through Twitter social media application to the consumer to control his consumption. The consumption behavior is assessed according to the time of use to make an energy saving based on the cost of electricity.

Wireless Control of Electrical Devices with Realtime Feedback

Abstract: This research idea originates from the core concepts of IoT connecting devices and focuses on minimizing human interference regarding the control of devices. This invention shapes its user into smart ones by making them a vital part of the technology. The end-user can supervise the devices connected to the developed hardware that reflects the operating state of these connected devices by use of the microcontroller ESP12F. The device state is presented to the end-user through the mobile application using the backend as the Blynk platform. This project is run on the cloud server and hence provides real-time status of the connected device.

Special Issue on State-of-the-Art Actuators: Design, Analysis, Control, Materials, Systems, and Applications

Actuators are components that are essential to the moving, manipulating, or deforming of objects. Historically, conventional electromagnetic motors as well as pneumatic and hydraulic actuators have been developed to sophistication. In the past several decades, however, many other kinds of actuators based on novel principles have also been proposed. These have employed physical or/and chemical effects, such as piezoelectric, electrostatic, or giant magnetostrctive effects, as well as thermal expansion, phase transformation, or ion mobility in polymers. Not only the novel actuators but also conventional ones have been continually evolving in astounding ways. These actuators have been embedded not only in conventional machines but also in smart ones, such as artificial muscles for robots and power assist systems, electric vehicles, and machine tools. By combining the actuators with the Internet of Things (IoT), they are also used to test equipment. In addition, there is no doubt that the technologies involved in the development of novel actuators and the improvement of their efficiency are key to the achievement of Sustainable Development Goals (SDGs), as actuators currently consume a huge aggregate amount of energy. In sum, actuators have the potential to be central to the development of innovative machines. This special issue features one review, six research papers, and three technical papers on the most recent advances in various types of actuators. These papers cover topics that include magnetic levitation technologies for precision motion control, electromagnetic motors, fluid power actuators, electrostatic actuators embedded in micro-electromechanical systems, and an ultrasonic motor, plus their applications. All the papers were refereed through careful peer reviews. I believe that this special issue will help the readers to enhance their understanding and knowledge of actuators and their applications. I would like to express my sincere appreciation to the excellent contributions of all the authors, and I appreciate the incisive efforts of reviewers in producing this special issue.

Best of Both Worlds? The Potentials and Challenges of Implementing Sustainable and Smart Urban Mobility

In recent years, many academic and technical discussions about cities have been shaped by two topics: sustainability and smartness. While these two areas are evolving, there are definitely common grounds to be found in discourses on sustainability and smartness. First of all, this is the realization that any comprehensive transformation of long-term, complex processes requires governance and integration of topics and institutions, and second, that there can be no uniform approach to successfully becoming more sustainable or smarter. However, different directions of development can be identified that may—or may not—go together. Urban mobility has to deal with different definitions of and approaches to sustainability and smartness too. A specific format developed during the transdisciplinary project “Neue Mobilität Berlin” (New Mobility Berlin, http://neue-mobilitaet.berlin/) addresses these questions. Research results suggest, for one, that there are very practical technical issues that complicate a transition from existing mobility systems toward more sustainable and smart ones. For another, the results suggest that a comprehensive sustainable and smart urban mobility system will need more integration and coordination. This contribution takes off from project findings and discusses implications for the implementations of and discourses on smart and sustainable urban mobility.

A multi-dimensional city data embedding model for improving predictive analytics and urban operations

PurposeA smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region.Design/methodology/approachThe authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction.FindingsThis study’s results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient.Originality/valueThe authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.

Parametric Urban-Scale Analysis of Space Cooling Energy Needs and Potential Photovoltaic Integration in Residential Districts in South-West Europe

The energy needs for space cooling are becoming a significant share of the energy balance of different Member States of the European Union, in particular the Mediterranean countries. This trend has been observed and monitored by the European Union, which has started a number of initiatives to promote the reduction in the energy demand for space cooling and have it satisfied by renewable energy sources, such as photovoltaic electrical energy. Nevertheless, even if the potential of those solutions has been widely investigated at the single-building level, this scale of analysis seems not fully adequate to support the definition of the energy policies addressed towards the renovation of the current cities into smart ones, with a large share of their energy demand satisfied with renewable energy. In this framework, this research aims to investigate the topic of building energy performance for space cooling services by adopting an urban-scale approach. In detail, a parametric simulation plan was run with CitySim in order to assess the impact of different quantities, i.e., climate conditions, districts’ and buildings’ geometry features, and the thermal quality of the building envelope, on the overall cooling energy need for districts and the specific building energy performance. Furthermore, the advantages of the integration of photovoltaic systems to supply power to the cooling system were analyzed, identifying the district configurations with the highest potential. For instance, in Athens, the share of space cooling demand satisfied by PV in high-rise nZEB configurations ranges between 64% (Building Density = 0.25) and 87% (Building Density = 1), while in the low-rise nZEB configurations it ranges between 81% (Building Density = 0.25) and 75% (Building Density = 1).

Stability Assessment of Secondary Frequency Control System With Dynamic False Data Injection Attacks

The progression of modern computing technologies assists the development of cyber-physical systems, which are transforming the legacy electrical power systems into smarter ones. The informationalization of the grid poses potential vulnerabilities concerning cyberattacks. With dynamic variations over time, cyberattacks can cause significant impacts on the secondary frequency control with various attack scenarios. In this article, by divulging the characteristics of dynamic attacks, the stability and dynamic responses of secondary frequency control systems are analyzed. The complete attack models considering dynamic load altering attack and dynamic false data injection attack are both derived first. Then the system stability is evaluated with different attack models through mathematical analysis. Eventually, the simulation studies against two benchmark power system models validate the evaluation results.

IoT Based Disease Prediction Using Mapreduce and LSQN3 Techniques

In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN3 techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM) approach. Initially, the heart disease prediction dataset is acquired from publicly available sources in the proposed approach. Following that, the dataset is mitigated by reducing redundant data using Map-Reduce and making it useful for the upcoming examination. During the mapping step, the Linear Log induced K-Means Algorithm (LL-KMA) clustering algorithm is used. The LF-CSO technique is used in the reduction phase to select the optimal Cluster Centroids (CC). The features are extracted from the reduced data. After that, utilizing the Pearson Correlation Coefficient based Generalized Discriminant Analysis (PCC-GDA), the extracted features’ dimensionality is mitigated. Subsequently, the features being reduced are neumaralised for classification purposes. Lastly, to classify the disease, the Log Sigmoid activation based Quasi-Newton Neural network (LSQN3) classifier is employed. The proposed method is contrasted with the existing methodologies to assess the performance. The experiential outcomes displayed that the proposed work is highly efficient than the other methodologies.

Smart City: A Mobility Technology Adoption Framework Incorporating Surface-Level Technical Analysis

Rising urban population, aging infrastructure, and increasing capital maintenance costs call for more efficient use of limited available resources. To address these concerns, the use of technology for urban infrastructure management and operational efficiency comes naturally with emerging technological advancements. Although there have been analyses on how to conceptually design a smart city from the ground up, they are often less applicable in transforming existing cities into smart ones. Retrofitting existing infrastructures requires integration and synergies with existing systems. Given the broad scope of smart cities, this paper equips planners with surface-level considerations in adopting smart mobility solutions. This provides an avenue to assess project feasibility, risk management, and investment requirement. The process is presented via a replicable framework with a use case with simplistic approaches that do not require complex constraints or modeling. The framework streamlines how to deduce a feasible set of user-centric smart solutions, which are then ranked according to their impacts for implementation priority. Middle East Technical University campus located in Ankara (Turkey) is considered for the use case. The main outcomes for the use case are deducing high-impact smart solutions based on the proposed framework. Preliminary system design analyses are showcased for three high-ranked solutions: electric vehicle charging station installation and investment optimization, autonomous electric shuttle system design, and bus network electrification strategies.