Search Results (86)

With the increasing scale of low Earth orbit (LEO) satellite networks, leveraging non−terrestrial networks (NTNs) to complement terrestrial networks (TNs) has become a critical issue. In this paper, we investigate the issue of handover satellite selection between multiple terrestrial terminal groups (TTGs). To support effective handover decision-making, we propose a long short-term memory (LSTM)-network-based traffic prediction mechanism based on historical traffic data. Building on these predictions, we formulate the handover strategy as a Markov Decision Process (MDP) and propose an attention-enhanced rainbow-DQN-based joint traffic prediction and handover design framework (ARTHF) by jointly considering the satellite switching frequency, communication quality, and satellite load. Simulation results demonstrate that our approach significantly outperforms existing methods in terms of the handover efficiency, service quality, and load balancing across satellites. Full article

This article first constructs a multi-layer deep learning neural network to help understand the structural characteristics of communication data, thereby learning complex functions and obtaining the predicted network values. At the same time, signal transmission is achieved through the interconnection of neurons, the representation performance of which is enhanced through activation functions; this completes the modeling of IoT communication models. Then, we use the analytic hierarchy process to construct a deep learning autoencoder and extract the feature elements of network communication reliability parameters. Finally, we use the obtained total reliability indicators as features for automatic coding and evaluate the mapping relationship between indicators. The results show that the success rates of handovers in deep leaning-based IoT communication based are all greater than 99.6%. The predicted transmission rate can reach a maximum of 99.5%, achieving error free communication output and improving fidelity. Full article

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. Full article

Cooperative multi-UAV clusters have been widely applied in complex mission scenarios due to their flexible task allocation and efficient real-time coordination capabilities. The Air Command Aircraft (ACA), as the core node within the UAV cluster, is responsible for coordinating and managing various tasks within the cluster. When the ACA undergoes fault recovery, a handover operation is required, during which the ACA must re-authenticate its identity with the UAV cluster and re-establish secure communication. However, traditional, centralized identity authentication and ACA handover mechanisms face security risks such as single points of failure and man-in-the-middle attacks. In highly dynamic network environments, single-chain blockchain architectures also suffer from throughput bottlenecks, leading to reduced handover efficiency and increased authentication latency. To address these challenges, this paper proposes a mathematically structured dual-chain framework that utilizes a distributed ledger to decouple the management of identity and authentication information. We formalize the ACA handover process using cryptographic primitives and accumulator functions and validate its security through BAN logic. Furthermore, we conduct quantitative analyses of key performance metrics, including time complexity and communication overhead. The experimental results demonstrate that the proposed approach ensures secure handover while significantly reducing computational burden. The framework also exhibits strong scalability, making it well-suited for large-scale UAV cluster networks. Full article

(1) Background: This paper examines the relationships between generational handover protocols, knowledge transfer behavior, and key organizational outcomes. (2) Methods: A quantitative design was applied, using non-parametric tests and partial least squares structural equation modeling (PLS-SEM) on survey data from 168 employees in companies located in the Basque Country. (3) Results: The presence of formal knowledge transfer protocols and generational handover processes was significantly associated with greater employee knowledge-tranfer behaviors. These behaviors, in turn, had significant positive effects on organizational innovation and job performance. Although moderate correlations were observed with strategy, performance, and competitive advantage, the structural model did not confirm direct relationships. (4) Conclusions: Despite their strategic importance, many organizations still lack formalized mechanisms for knowledge preservation. This study offers a framework for understanding the impact of structured knowledge transfer practices on organizational performance and suggests avenues for future research in knowledge continuity and succession planning. Full article

The current trends in the research and development of self-driving technology aim for Level 3+ autonomy, where the vehicle controls both lateral and longitudinal motions of the dynamic driving task, while the driver is permitted to divert their attention, as long as they are able to react properly to a handover request initiated by the vehicle. At this level of autonomy, situation awareness of the human driver has become one of the most important metrics of safety. This paper presents the results of a user study to evaluate handover performance at Level 3 autonomy. The study investigates whether the level of situation awareness during critical handover situations has a direct impact on task performance, with higher situation awareness expected to lead to better outcomes during emergency interventions. The study is performed in a simulated environment, using the CARLA driving simulator and the master console of the da Vinci Surgical System. The test subjects were asked to answer the questions of a questionnaire during the experiment; the answers for those questions and the measured control signals were analyzed to gain further knowledge on the safety of the handover process. Full article

In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a common pivot station. Initially, we partition the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. The augmentation parameters for each SN are then computed using the precise orbit corrections and ionosphere-weighted constraints. However, directly applying the estimated augmentation parameters to users across subnetworks poses challenges due to inter-subnetwork discontinuities. These discontinuities arise from variations in the network configurations and the time correlation of the Kalman filters, despite the use of the same pivot station. To address this, common augmentation parameters, such as the satellite clocks and phase biases from each SN, are integrated into a unified set of parameters and broadcast to users. The aligned common augmentation parameters are then fed back into each SN, and the Kalman filter is re-updated to mitigate the inter-subnetwork discontinuities. The proposed architecture offers a reduced computational burden compared to the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Unlike previous research on decentralized PPP-RTK, the use of a common pivot station ensures a consistent basis for the common augmentation parameters. This approach enables seamless user positioning during transitions between SNs, eliminating the need to reset the user navigation filter during handover operations and simplifying the integration process. To evaluate the effectiveness of our proposed architecture, we gather dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORSs) in Korea. These data are then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compare the static positioning error and processing time of our proposed architecture with those of the centralized architecture. Additionally, the mitigation performance of the inter-network discontinuities is shown. Full article

The current study aims to determine the meaning and the role of legacy in the development of family businesses from the perspective of multigenerational family businesses. Employing Thematic Analysis (TA) and Gioia methodology, the transcript of in-depth interviews with representatives of five family businesses, from different industries (military products and wine, banking and jewelry) were analyzed and checked against the literature. The findings show that legacy is a complex process that evolves not only from its core elements, but as a part of business involvement in society. According to these elements, there are four patterns of legacy, namely legacy of knowledge, legacy of values, legacy of relationships, and legacy of contribution to society. These four patterns of legacy determine a specific type of doing business: “Sustainability Stewards”, “Knowhow Handover”, “Values Inheritance”, and “Intergenerational Blueprint”. Each type corresponds to a unique approach to managing and preserving the legacy within the family business. A set of best practices that family businesses seeking to consolidate their legacy is proposed as a practical value of this study. Full article

The retention of organizational knowledge is increasingly challenging for companies giventhe aging workforce, high turnover rates, and declining birth rates. This study explores the knowledge transfer processes during generational transitions and examines how digital transformation facilitates business model innovation. Specifically, it examines theknowledge transfer procedures implemented in companies in the Autonomous Community of the Basque Country, a competitive industrial region in Europe. Using a quantitative approach, 168 individuals in key leadership positions were surveyed on the mechanisms used for knowledge retention and their effectiveness. The results reveal that while companies prioritize knowledge transfer, most lack effective protocols. Organizations employing both digital and analog strategies are perceived as more efficient in retaining knowledge. Only half of the companies integrate knowledge transfer processes into their management strategies, with no observed differences in employee knowledge-sharing behaviors based on company size. This study concludes that a lack of structured procedures may harm long-term competitiveness, recommending that companies invest more in developing formal generational handover protocols. This research underscores the vital importance of knowledge retention for organizational sustainability and highlights the need for further exploration to address this issue. Full article

Handover is crucial for ensuring seamless connectivity in heterogeneous networks (HetNet) by enabling user equipment (UE) to switch its connection link between cells based on signal conditions. However, conventional analytical approaches ignored the distinctions between macro-cell to small-cell (M2S) and small-cell to macro-cell (S2M) scenarios during a handover decision-making process, which resulted in handover failures (HoF) or ping-pong handovers. Therefore, this paper proposes a novel framework, Do-SPN-PPP, that combines stochastic Petri net (SPN) and the Poisson point process (PPP) to quantitatively analyze M2S and S2M handover performance differences. The proposed framework also reveals and predicts how handover parameters affect UE residence time in a cell within the HetNet, and it exhibits a higher predictive accuracy compared with the traditional conventional analytical approach. In addition, the Monte Carlo simulation verified the Do-SPN-PPP framework, and the proposed framework exhibits a 96% reduction in computation time while maintaining a 95% confidence interval and 0.5% error tolerance compared with the simulation. Full article

Standardized transfer is an evidence-based framework designed to improve communication between healthcare professionals, reducing risks and ensuring safe, high-quality care. Despite its benefits, implementing this framework in clinical practice poses challenges. Nurses often do not use a systematic guide as a theoretical framework for handovers in daily practice. Objective: To explore nurses’ perceptions regarding the use of standardized transfers. Methodology: This exploratory qualitative cross-sectional study aimed to gain insight into nurses’ experiences and perspectives on pediatric patient transfers. Using purposive sampling, nurses from the pediatric intensive care unit and hospital wards at the hospital institution hosting the study were interviewed. Data were collected through 21 in-depth individual interviews conducted between April and May 2023. The semi-structured interviews, lasting 16 to 28 min, focused on nurses’ views on communication between units during patient transfers. The qualitative approach allowed for a comprehensive understanding of nurses’ perceptions, particularly the barriers they face in practice. The study included 21 nurses: 9 from the pediatric intensive care unit and 12 from pediatric wards. To ensure diverse representation, nurses with varying levels of work experience were included, and at least one nurse from each hospital ward participated. Results: The data were classified into the following main categories: the current state of pediatric patient transfers, attitudes of healthcare professionals, barriers and challenges to implementation, nursing documentation, motivational aspects, and the child-family relationship. The findings revealed significant issues in the communication process during patient transfers, with no systematic guidelines in place. While nurses demonstrated a positive attitude toward the standardization of transfers, they identified numerous practical challenges, particularly those related to the hospital’s nursing documentation system. Conclusions: Nurses view standardized transfers favorably, but they face substantial barriers that limit their practical implementation. Full article

Due to the rapid development of smart vehicles, such as self-driving cars, the demand for mobile data traffic by vehicle users has increased so much that base stations cannot handle it, causing delays in content provision. The burden on the base station can be alleviated through roadside units (RSUs) to distribute the demand. However, outage zones, which fall outside the communication range of RSUs, still exist due to their high deployment cost. Existing schemes for covering outage zones have only considered single-hop precaching vehicles to provide precached content, which is insufficient to reduce outage zones effectively. Therefore, we propose a scheme to reduce outage zones by maximizing the amount of precached content using multi-hop precaching vehicles. The proposed scheme optimally selects precaching vehicles through a numerical model that calculates the amount of precached content. It enhances the process of multi-hop precaching by comparing the connection time of vehicles with the dark area time in the outage zone. To prevent excessive overheads due to frequent precaching vehicle handovers, the proposed scheme limits the selection to vehicles with a longer communication time, based on a precaching restriction indicator in the multi-hop precaching vehicle selection process. The simulation results show that our scheme outperforms representative schemes based on single-hop precaching. Full article

In 5G networks and beyond, managing handovers (HOs) becomes complex because of frequent user transitions through small coverage areas. The abundance of small cells (SCs) also complicates HO decisions, potentially leading to inefficient resource utilization. To optimize this process, we propose an intelligent algorithm based on a method that utilizes a fuzzy logic controller (FLC), leveraging prior expertise to dynamically adjust the time-to-trigger (TTT), and handover margin (HOM) in a 5G ultra-dense SC heterogeneous network (HetNet). FLC refines TTT based on the user’s velocity to improve the response to movement. Simultaneously, it adapts HOM by considering inputs such as the reference signal received power (RSRP), user equipment (UE) speed, and cell load. The proposed approach enhances HO decisions, thereby improving the overall system performance. Evaluation using metrics such as handover rate (HOR), handover failure (HOF), radio link failure (RLF), and handover ping-pong (HOPP) demonstrate the superiority of the proposed algorithm over existing approaches. Full article

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a, and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 $\mathsf{\mu }$s less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover. Full article

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts. Full article