Connectivity Of Devices Research Articles

Edge computing task scheduling method based on user’s social relations: a construction and solution for Smart City Library

In the realm of the development of a Smart City Library, the integration of robust edge computing is vital. The research suggests a novel task-scheduling model for edge computing, leveraging user’s social relationships. Analyzing these connections involves constructing a user’s social relationship graph by implementing mathematical convolution and the Jaccard similarity ratio. This precise quantification of social ties ensures secure and reliable task scheduling. An equipment connection graph of a user equipment service is also crafted based on Euclidean distance, aligning task scheduling with device-to-device (D2D) communication conditions. Combining a user’s social relationship graph and a user’s device-service device connection graph creates a task-device bipartite graph. On the other hand, the calculation of a task execution cost and edge weight determination finalize a scheduling model. Implementing the proposed method for constructing a model for edge computing task scheduling based on utilizing the Kuhn–Munkres (KM) algorithm demonstrates positive impacts, which are few delays and less energy consumption, on edge computing task scheduling. For instance, when the social threshold score changes from 02. To 0.6, the total task execution delay time increases from 23 to 32, which is the best when compared with other algorithms. The approach strengthens security and reliability while decreasing task execution delays and energy consumption. This research advances edge computing for Smart City Libraries, promising transformative implications.

Application of safety and stability optimization algorithms for charging connection devices in high-power charging systems

In response to the safety and stability issues of current electric vehicle charging connection devices, this study proposes a charging system planning for electric vehicles with different capacity charging piles based on the user behavior characteristics of electric vehicles and Monte Carlo methods. It is found that the predicted results under the set management strategy are most consistent with the trend of actual load changes. Moreover, in the prediction of weekly load, the research strategy has better performance than traditional unmanaged strategies. Under the research scheme, the average charging speed of charging piles with capacity of A and B in the peak period was 41.4 min/ and 18.8 min/, respectively, which increased by 29.3% and 11.7% respectively compared with 58.6 min/ and 21.3 min/ in the normal period. The total economic cost of the research plan was 4.871 million yuan, which was 67.0 million yuan and 3.833 million yuan lower than the control methods 1 and 2, respectively. The total number of charging stations of types a and b that need to be purchased for the research method decreased by 18.47% and 63.24% compared to the comparative method 3. The results indicate that the research method significantly improves the utilization rate of charging stations in the electric vehicle charging system. This study has important application value in the intelligent management of electric vehicle charging systems.

Robustness of Dry‐Assembled Precast Wall and Coupled Wall‐Frame High‐Rise Structures With Voided Prestressed Slabs

ABSTRACTPrecast concrete structures are recognized among the structural typologies that may mostly suffer from progressive collapse as a consequence of local unpredicted damage, also due to past accidents occurred in buildings employing old precast technologies neglecting the basic robustness criteria and characterized by poorly detailed and/or executed joints and connections. Although vertical ties may relatively easily be provided in both frame and wall panel structures by employing ductile connection devices, dry‐assembled precast systems avoiding concrete pouring in the whole superstructure typically struggle to provide horizontal ties as strong as required by the current standards concerning progressive collapse, in which they are not fully framed yet. Nevertheless, these systems may exploit alternative sources of robustness to stop collapse progression arising from their structural arrangement, the shape of the slab elements employed, and the slab mechanical connections. This article presents the results of a numerical investigation focused on the structural behavior of a dry‐assembled precast floor system consisting of prestressed box‐section slab elements employed in either wall panel or coupled wall‐frame structural systems following the loss of one or multiple primary load‐bearing vertical elements. Nonlinear static simulations of different loss scenarios occurring in prototypes of 100‐m‐tall high‐rise buildings were carried out based upon spread plasticity modeling of the structural elements and experimentally calibrated nonlinear behavior of mechanical connections.

Design Method of a Novel Interface Connection Device for Multiscale Test Model Considering Multiparametric Similarity of Internal Forces

ABSTRACTThe multiscale model of building structures, as a balanced solution between accuracy and cost, has been widely used in the analysis of structural seismic performance. A reasonable interface connection method can accurately ensure load transfer and motion coordination between models of different scales. In this paper, a novel interface connection device and the corresponding design method for a multiscale test model of building structures were proposed, in which the upper structure with smaller sized components was replaced by a simplified story‐scale model, and the lower structure was adopted as a component‐scale model. The overall and local equations of motion for this multiscale model were established. For the interface connection between different scale models, a design method considering multiparametric similarity of shear force, axial force, and bending moment was proposed. In this method, the internal nodes at the interface of the component scale model were decomposed, and the coupling relationship of internal force between two adjacent nodes was established. The axial force of each node was decoupled into the interstory shear force and bending moment provided together. Additionally, the overturning moment is provided by adding the overlapping domain. According to the equilibrium relationships of the nodes at the interface, the corresponding transfer matrix was provided, and the design method of the interface connection device was proposed. The accuracy and feasibility of the method were validated by static and shaking table tests on a frame structure.

Microsoft applications of 6G in V2X

Abstract. With the rapid advancement of intelligent transportation systems and autonomous driving technologies, 6G networks, as a key technology for future communications, are set to bring trans-formative changes to V2X (Vehicle-to-Everything) communication. This paper explores the application prospects of 6G in V2X, with a focus on the critical technological support 6G provides to vehicular networks. The high transmission rates and ultra-low latency of 6G will significantly enhance real-time interactions between vehicles and between vehicles and infrastructure, improving traffic safety and efficiency. Additionally, 6G's broad connectivity and flexible network architecture will support a greater number of simultaneous device connections, facilitating the widespread adoption of intelligent transportation systems. However, 6G faces challenges in V2X applications, including security issues, privacy protection, and internet power supply concerns. The paper analyzes these challenges, proposes corresponding solutions, and envisions the future development of 6G technology in the V2X domain.

SensorHUB – a Novel, Open-source Software Stack for Enhanced Accessibility and Secure Interoperability in IoT Project Management

Abstract. The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.

Multi-Criteria Decision Analysis of Wireless Technologies in WPANs for IoT-Enabled Smart Buildings in Tourism

The increasing demand for energy-efficient and interconnected smart buildings, particularly in the tourism sector, has driven the adoption of advanced wireless technologies. IoT technologies are crucial in this evolution, improving modern buildings’ functionality and operational efficiency. This study investigates the utilization of various wireless technologies within Wireless Personal Area Networks (WPANs), including Bluetooth BLE 4.2, Bluetooth BLE 5.0, ZigBee, and Z-Wave, in smart buildings. A multiple-criteria decision-making (MCDM) approach, specifically the PIPRECIA-S model, was applied to evaluate these technologies based on criteria such as device connectivity, mobility, low energy consumption, scalability, flexibility, and interoperability. Simulations using the PIPRECIA-S model were conducted to assess technology performance across various real-world scenarios. The results indicate that ZigBee (0.2942) and Bluetooth BLE 5.0 (0.2602) provide superior performance in terms of energy efficiency and scalability, followed by Z-Wave (0.2550) and Bluetooth BLE 4.2 (0.1906). These findings provide decision-makers with data-driven recommendations for selecting the most suitable wireless technologies for smart buildings.

Research on the mechanism and measures of riding comfort deterioration in a low floor tram

With the global widespread preference for low-floor trams, the expectation for tram riding comfort has been increasingly heightened. This paper identifies an abnormal riding comfort problem (ARCP, characterized by an excessive and W-shaped distribution of riding comfort) during dynamic field tests on a low-floor tram. To address ARCP, a multi-body dynamics model of the tram was constructed and validated its accuracy through dynamic field tests. By integrating track irregularities and wheel-rail contact analysis, the riding comfort index was assessed and reproduced the ARCP phenomenon. Linearization and time-domain integration studies were conducted on the mechanisms and measures to resolve the ARCP. The research findings reveal that the primary cause of the deterioration in the mean comfort index is the large amplitude of the track irregularity and the low equivalent conicity of the wheel-rail contact relationship. The main reason for the W-shaped distribution of riding comfort is the weaker inter-vehicle constraints and the lack of lateral energy-absorbing devices. Finally, optimization measures were proposed, including reducing the lateral stiffness of the second suspension, altering the carbody structural parameters, and modifying the inter-vehicle connection devices. This research enriches the study of tram dynamic performance and offers insights that may contribute to the resolution of ARCP.

Beyond the Chalkboard: Digital Innovations in Islamic Learning through Interactive PowerPoint

In the Gen Z era, innovative and creative methods are essential for effective Islamic education. During that period, students are granted internet connectivity and portable digital devices, which facilitate the acquisition of knowledge about Islam. This study developed a novel interactive learning medium using PowerPoint Office 2010, focusing on Aqidah Akhlak’s curriculum implementation. Employing the ADDIE model as the research and development (R&D) strategy, the study involved validation by experts and practical assessments by teachers and students. The selected research instruments comprised expert validation sheets and media practicality assessments. The impact was computed with descriptive statistics. The results indicated high validity, with scores of 85% from material experts, 82.4% from media experts, and 87% from design experts. The practicality tests revealed 98% and 89.17% approval ratings from teachers and students, respectively. The effectiveness of the medium was demonstrated by a significant improvement in student performance, with pre-test and post-test scores of 69.70 and 85.29, respectively. These findings affirmed the impact of the developed interactive media on enhancing student learning outcomes in Islamic education.

High-Altitude-UAV-Relayed Satellite D2D Communications for 6G IoT Network

High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies.

Monitoring and enhancing the co-operation of IoT network rhrough scheduling function based punishment reward strategy

The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour.

Automatic device arrangement and wiring for fire alarm systems in metro stations: A case study

Fire alarm systems in metro stations play an important role in safeguarding lives and properties. The design process for these systems is complex and time-consuming due to the diversity of device types, wide distribution of device locations, and numerous compulsory design codes. This paper proposes an automatic design framework for fire alarm systems aimed at enhancing design efficiency. The framework includes five automated processes: information preprocessing, device arrangement, system loop generation, device wiring, and result generation. By converting the computer-aided design (CAD) results into building information models (BIM), building information and related professional device information are obtained, addressing the need for fire alarm system (FAS) design to integrate with multiple disciplines. The process can also automatically arrange FAS devices according to design specifications. Optimization algorithms, such as Ant Colony Algorithm and Prim Algorithm, are used to determine the device connection order and solve the problems of obstacle avoidance and orthogonal connections between two points. Compared to traditional design methods, the proposed automatic design framework for fire alarm systems in metro stations can not only satisfy the mandatory design requirements but also avoid the cumbersome and repetitive design process, providing new solutions for designers. Additionally, it can reduce wire length and save on construction costs. Using a real metro station as a case study, it is calculated that the automatic design can save between 8 % and 48 % of the wire length.

Evaluation of the Veterans Health Administration's Digital Divide Consult for Tablet Distribution and Telehealth Adoption: Cohort Study.

Video telehealth offers a mechanism to help Veterans Health Administration (VHA) patients overcome health care access barriers; however, many veterans lack a suitable device and sufficient internet connectivity. To address disparities in technology access, VHA established a Connected Device Program that offers veterans loaned video-capable tablets and internet service. In 2020, VHA introduced a national Digital Divide Consult to facilitate and standardize referrals for this resource. We sought to evaluate the reach and impact of VHA's Connected Device Program, leveraging Digital Divide Consult data to determine whether resources are supporting veterans with health care needs and access barriers. We examined the reach of VHA's Connected Device Program using national secondary data from VHA's electronic health records among 119,926 tablet recipients who received a tablet (April 1, 2020, to February 28, 2023) and 683,219 veterans from the general VHA population. We assessed changes in tablet recipients' demographic and clinical characteristics before and after implementation of the Digital Divide Consult compared with the general VHA population. We examined the impact of tablets and the consult on adoption of telehealth (ie, video visit use and number of visits) adjusting for differences between tablet recipients and the general VHA population. Finally, we evaluated consult implementation by assessing the use of video-based services by tablet referral reason. Common reasons for tablet referral included mental health diagnoses (50,367/79,230, 63.9%), distance from a VHA facility >30 miles (17,228/79,230, 21.7%), and social isolation (16,161/79,230, 20.4%). Moreover, 63.0% (49,925/79,230) of individuals who received a tablet after implementation of the Digital Divide Consult had a video visit in the first 6 months of tablet receipt. Some consult reasons were associated with a higher-than-average percentage of video telehealth use, including enrollment in evidence-based mental health programs (74.8% [830/1100] with video use), living >30 miles from a VHA facility (68.3% [10,557/17,228] with video use), and having a mental health diagnosis (68.1% [34,301/50,367] with video use). Tablet recipients had nearly 3 times the likelihood of having a video visit within a month once provided a tablet compared to the general VHA population, with an adjusted risk ratio of 2.95 (95% CI 2.91-2.99) before consult implementation and 2.73 (95% CI 2.70-2.76) after consult implementation. Analyses of telehealth adoption suggested that veterans receiving tablets for mental health care and evidence-based programs have higher rates of video visits, while those who are homebound or receiving tablets for hospice have higher rates of nonuse. This evaluation of VHA's Connected Device Program suggests that tablets are facilitating video-based care among veterans with complex needs. Standardization of referrals through the Digital Divide Consult has created opportunities to identify groups of tablet recipients with lower telehealth adoption rates who might benefit from a targeted intervention.

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Methods: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

Analyzing AWS Edge Computing Solutions to Enhance IoT Deployments

This paper explores integrating Internet of Things (IoT) deployments with edge computing, focusing on Amazon Web Services (AWS) as a key facilitator. It provides an analysis of AWS IoT services and their integration with edge computing technologies, addressing challenges, and practical applications across industries, and outlining future research directions. IoT and edge computing revolutionize data processing by enabling real-time analytics, reduced latency, and enhanced operational efficiency. IoT involves interconnected devices autonomously gathering and exchanging data, while edge computing processes data near its source, decentralizing data processing and minimizing data transmission to centralized servers. AWS facilitates scalable and secure infrastructures for IoT and edge computing. AWS IoT Core manages IoT device connectivity and data ingestion, AWS Greengrass extends AWS capabilities to edge devices, and AWS Lambda enables serverless computing, empowering efficient deployment and scaling of IoT applications. Centralized cloud architectures often struggle with vast IoT data. Edge computing decentralizes data processing, reducing latency, enhancing real-time capabilities, and minimizing bandwidth. AWS ensures secure device connectivity through AWS IoT Core, supporting various protocols for seamless integration with IoT devices. AWS Greengrass allows local data processing and machine learning at the edge, vital for environments with limited connectivity or stringent latency requirements. AWS Lambda supports serverless computing, enabling scalable, event-driven architectures without server management, crucial for fluctuating IoT workloads. In conclusion, AWS advances IoT capabilities at the edge, with practical implementations across industries. As IoT evolves, AWS remains pivotal, innovating to meet dynamic IoT deployment demands.

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

The connection of devices in distributed environments produces and shares a vast amount of data useful for different organisational decision-making. In healthcare service organisations, for example, multiple e-health systems from different departments or facilities connect and share health data and information. During sharing, proper management is important to ensure the information is secure against intruders. Machine learning as a non-conventional security technique can be used along conventional techniques like firewalls, antivirus and intrusion detection systems to predict future network threats and other anomalies using historical backgrounds and other features. However, some machine learning algorithms have complex computation thus requiring resourceful systems in terms of network bandwidth, CPU power, memory, and storage capacity. In resource-constrained environments, therefore, special consideration is needed to ensure that the analysis of the big data is successful and that the benefits associated with them are effectively obtained. In this paper, a Machine Learning algorithm was selected among four algorithms whose performance was compared through various performance metrics. Classification accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Absolute Error (RAE) among other performance metrics were used to compare the ANN, Random forest, Decision trees, and Naïve byes classification algorithms using an extract from CICDDOS2019 dataset. Using the Weka version 3.8.6, the algorithms were compared to choose the best one to classify the data. By using three computers with different resources, the experiments were carried out to determine the performance of those machine learning algorithms. The result revealed that the random forest produced a good average classification performance in resource-limited systems since it surpassed other algorithms in classifying the data at an average of 99 per cent with a low average mean absolute error of 0.0001. Furthermore, as an ensemble that classifies with multiple decision trees algorithm, it likewise uses reasonable time to build and test the model therefore recommended for resource-limited systems.

Advancing Healthcare Research Through Live-Data Integration: Implementing IEEE 11073 and FHIR in Clinical Environment.

The efficient direct integration of real-time medical device data is a promising approach to improve patient care enabling a direct and eminent intervention. This study presents a comprehensive approach for integrating real-time medical device data into clinical environments using the HL7® FHIR® standards and IEEE 11073 Service-Oriented Device Connectivity (SDC). The study proposes a conceptual framework and an opensource proof-of-concept implementation for real-time data integration within the Medical Data Integration Center (MeDIC) at UKSH. Key components include a selective recording mechanism to mitigate storage issues and ensure accurate data capture. Our robust network architecture utilizes Kafka brokers for seamless data transfer in isolated networks. The study demonstrates the selective capturing of real-time data within a clinical setting to enable medical device data for a down-stream processing and analysis.

A novel sum-rate maximization scheme for NOMA-VLC systems via the black widow enhanced Kepler optimization algorithm

Non-orthogonal multiple access (NOMA) is a forthcoming method to address the challenges posed by increasing device connectivity and diverse communication requirements of visible light communication (VLC) systems in the sixth generation (6G) networks. This paper integrates NOMA and VLC systems (NOMA-VLC) and concentrates on studying the problem of maximizing the sum rate in downlink systems using NOMA, considering the existence of channel state information (CSI) errors. Taking power distribution and the user's quality of service (QoS) requirement as constraints, the purpose is to maximize the total sum rate. However, because this problem is usually a nonconvex NP-hard problem, it is challenging to obtain the best solution. In this paper, a combination of the improved Kepler optimization algorithm and the black widow optimization algorithm (BWO-KOA) is provided as a solution to solve this optimization problem and get an effective solution. By comparing the properties of BWO-KOA with other existing algorithms, the simulation results demonstrate that BWO-KOA outperforms these methods in the sum rate characteristics of the NOMA-VLC system.

An individualized adaptive distributed approach for fast energy-carbon coordination in transactive multi-community integrated energy systems considering power transformer loading capacity

The optimization of multi-community integrated energy systems (MCIES), as a proficient means of integrating distributed energy resources and diverse loads, poses a compelling challenge. This challenge revolves around strategically harnessing the energy-carbon synergy advantages within these systems, while factoring in the heterogeneity among different communities and the constraints imposed by electrical connectivity devices. Facing the challenge, this paper proposes an individualized adaptive distributed approach for energy-carbon coordination in MCIES with power transformers (PT-MCIES). Firstly, a feasible region is constructed based on current and temperature limits to assess the loading capacity of power transformers. Based on this, a transactive energy (TE) based co-optimization model for PT-MCIES considering energy sharing and carbon trading is developed, where rolling horizon optimization is adopted to cope with the randomness from renewable energy and loads. For the TE problem, the alternating direction method of multipliers (ADMM) algorithm is employed to achieve distributed optimization. Uncertain renewable energy and loads, along with variable rolling horizons, make it difficult for the traditional ADMM to ensure convergence speed in the co-optimization of PT-MCIES. Accordingly, an individualized adaptive ADMM (IAADMM) embedded a spectral penalty parameter selection rule is adopted. Simulation results show that the proposed scheduling model can effectively guarantee the safe operation of power transformers with the suggestion of a feasible region, and achieve higher economic and environmental benefits than the reference one that ignores energy-carbon coordination. Furthermore, the proposed IAADMM exhibits superior performance in terms of convergence speed and robustness to the initial penalty parameter when compared to ADMM and its various variants.