Service Requirements Research Articles

Impact of the COVID-19 lockdown on the O'Sullivan test and gestational diabetes mellitus diagnosis in pregnant Spanish women

ObjectiveTo analyze the impact of COVID-19 lockdown on serum glucose levels of pregnant women. DesignA retrospective analysis of O'Sullivan test in pregnant women who underwent COVID-19 lockdown compared to controls. SitePoniente Primary Health Care center in Córdoba (Spain). Participants235 pregnant women from 23+0 to 25+0 weeks of gestation without diabetes mellitus. InterventionsGestational diabetes mellitus screening with O'Sullivan test and 3-h oral glucose tolerance test. Main measurementsPregnant women who underwent gestational diabetes mellitus screening with O'Sullivan test before (control group) and during COVID-19 Lockdown (Lockdown group) in Córdoba (Spain) were investigated. Lockdown group was divided in early and late lockdown. An additional, control group from data of the same months of the Lockdown in the previous year were recorded to discarded seasonally (adjusted seasonally control) this group was also divided in early and late seasonally adjusted.A logistic regression model for O'Sullivan test has been performed to analyze potential cofounders. Kolgomorov–Smirnov and Kruskal–Wallis test comparing pregnant women who underwent COVID-19 lockdown with the two types of controls. ResultsStatistically significant differences were found in serum glucose after O'Sullivan test between lockdown group and control group (123.51±26.02mg/dL and 112.86±31.28mg/dL; p=0.017). When early lockdown group and control group were compared no differences were found (119.64±26.18mg/dL vs. 112.86±31.28mg/dL; p>0.05) whereas differences were observed in late lockdown group and control group (127.22±25.59mg/dL vs. 112.86±31.28mg/dL; p=0.009). Statistical trends were also found between lockdown group and seasonally adjusted group and between lockdown and late seasonally adjusted group (p=0.089). A higher proportion of positive O'Suvillan pregnant women who were subsequently diagnosed with GDM were found in lockdown group compared to the seasonally adjusted control group (60% vs. 26.06% respectively; p<0.05). ConclusionsThe COVID-19 lockdown was associated with an increase in serum glucose levels after the O'Sullivan test as well as a higher GDM diagnosis risk in pregnant women. The findings of our study emphasize the essential requirement for comprehensive maternal services and the accessibility to community's health assets during future lockdown scenarios to pregnant women.

Mechanical Behaviour Assessment and Optimization of Construction Sequence for an HSS Vierendeel Truss

Long-span Vierendeel trusses in construction are challenged by smooth force transfer and critical stress control concerns. In this article, construction sequences characterized by loading/unloading due to various assemblies of the branches and/or temporary lattice column supports are focused on to reproduce their related mechanical behaviours in finite element modelling. Typical bending moment and shear force diagrams for hollow structure section (HSS) chords are analysed first, and then combined with a corrected assessment index in the development of an optimized construction scheme through effective discussion. It is observed that the branch assembly sequence has significant but reversed influences on either the bending moments or the shear forces of the interior support and the midspan of the HSS chords. The influence of the branch assembly sequence on the bending moment and shear force are compromised with the introduction of a temporary support. The corrected assessment index superimposed by the internal force and the deflection is demonstrated properly to represent the strength and the serviceability requirements, especially for characterizing the removal of a temporary support after the completed construction of a twelve-panel Vierendeel truss for the roof of an assembly hall.

Traffic Awareness-Based Target Wake Time Scheduling in 802.11ax WLANs

In recent years, the rapid growth of mobile communication has led to the continuous expansion of network scale, and the energy consumption of wireless communication has also increased rapidly. Under the high-density networking conditions, frequent communication activities greatly increase the energy consumption. In order to reduce channel competition, optimize resource scheduling and reduce equipment power consumption, the target wake-up time (TWT) mechanism in IEEE 802.11ax system plays an important role through timely scheduling and transmission. To improve channel efficiency for multi-user (MU) transmission and tackle with complicated traffic conditions, this paper presents a traffic awareness-based TWT scheduling scheme (TAT). The traffic is predicted and classified using a spatio-temporal series model for traffic-based TWT parameter generation. Subsequently, the time–frequency scheduling table is developed to adaptively assign Resource Units (RUs) and time slices to active STAs. Simulation results show that TAT can guarantee the Quality of Service (QoS) under dynamic changes in power service and delay requirements while reducing power consumption, effectively meeting the energy-saving demands in the intelligent access scenarios for power terminals.

Barriers, Facilitators, and Requirements for a Telerehabilitation Aftercare Program for Patients After Occupational Injuries: Semistructured Interviews With Key Stakeholders.

Patients with occupational injuries often receive multidisciplinary rehabilitation for a rapid return to work. Rehabilitation aftercare programs give patients the opportunity to help patients apply the progress they have made during the rehabilitation to their everyday activities. Telerehabilitation aftercare programs can help reduce barriers, such as lack of time due to other commitments, because they can be used regardless of time or location. Careful identification of barriers, facilitators, and design requirements with key stakeholders is a critical step in developing a telerehabilitation aftercare program. This study aims to identify barriers, facilitators, and design requirements for a future telerehabilitation aftercare program for patients with occupational injuries from the perspective of the key stakeholders. We used a literature review and expert recommendations to identify key stakeholders. We conducted semistructured interviews in person and via real-time video calls with 27 key stakeholders to collect data. Interviews were transcribed verbatim, and thematic analysis was applied. We selected key stakeholder statements about facilitators and barriers and categorized them as individual, technical, environmental, and organizational facilitators and barriers. We identified expressions that captured aspects that the telerehabilitation aftercare program should fulfill and clustered them into attributes and overarching values. We translated the attributes into one or more requirements and grouped them into content, functional, service, user experience, and work context requirements. The key stakeholders identified can be grouped into the following categories: patients, health care professionals, administrative personnel, and members of the telerehabilitation program design and development team. The most frequently reported facilitators of a future telerehabilitation aftercare program were time savings for patients, high motivation of the patients to participate in telerehabilitation aftercare program, high usability of the program, and regular in-person therapy meetings during the telerehabilitation aftercare program. The most frequently reported barriers were low digital affinity and skills of the patients and personnel, patients' lack of trust and acceptance of the telerehabilitation aftercare program, slow internet speed, program functionality problems (eg, application crashes or freezes), and inability of telerehabilitation to deliver certain elements of in-person rehabilitation aftercare such as monitoring exercise performance. In our study, the most common design requirements were reducing barriers and implementing facilitators. The 2 most frequently discussed overarching values were tailoring of telerehabilitation, such as a tailored exercise plan and tailored injury-related information, and social interaction, such as real-time psychotherapy and digital and in-person rehabilitation aftercare in a blended care approach. Key stakeholders reported on facilitators, barriers, and design requirements that should be considered throughout the development process. Tailoring telerehabilitation content was the key value for stakeholders to ensure the program could meet the needs of patients with different types of occupational injuries.

An Innovative Method for Improving the Performance of UAV‐SM‐NOMA for 6G Networks Over Generalized Fading Channels

ABSTRACTThe emergence of 6G wireless communications has brought numerous challenges and opportunities for meeting the growing demands of enhanced system capacity, higher data rates, lower latency, improved security, and stringent quality of service (QoS) requirements. One promising solution to tackle these challenges involves utilizing unmanned aerial vehicles (UAVs) in conjunction with nonorthogonal multiple access (NOMA) and spatial modulation (SM). However, optimizing the performance of UAV‐based SM‐NOMA systems for both perfect and imperfect channel state information (CSI) over generalized fading channels (α–μ, k–μ and η–μ) while simultaneously considering multiple objectives is a complex task. To address these challenges, this paper proposes integrating the monarch butterfly optimization algorithm (MBOA) into UAV‐SM‐NOMA systems for 6G networks. Inspired by the foraging behavior of monarch butterflies, the MBOA aims to enhance UAV‐based SM‐NOMA systems' performance by optimizing resource allocation, increasing system sum rate, reducing outage probability (OP) and bit error rate (BER), improving spectrum efficiency (SE), and ensuring desirable signal‐to‐interference‐plus‐noise ratio (SINR) levels. Key findings include that the MBOA effectively improves performance metrics across generalized fading channels compared to existing UAV‐assisted NOMA and orthogonal multiple access (OMA) models. Simulation results confirm that the proposed UAV‐assisted SM‐NOMA model with MBOA significantly outperforms conventional approaches, demonstrating superior capacity, reliability, and efficiency in 6G network scenarios.

Submucosal Thyroarytenoid Myectomy with Arytenoidectomy in Treating Bilateral Vocal Fold Immobility.

Surgery for bilateral vocal fold immobility (BVFI) aims to establish a patent airway while maintaining voice and swallow function, a unique dilemma. Current techniques display unfavorable healing vectors, and exposed endolaryngeal tissue results in substantial airway granulation requiring postoperative care. We present a novel technique to improve glottic airway patency by preserving laryngeal mucosal lining and harnessing lateral retraction of glottic tissue to reduce revisions for granulation tissue and laryngeal scar. Case-series analysis was performed on eight BVFI patients who underwent submucosal thyroarytenoid myectomy with arytenoidectomy for treatment from 2011 to 2021. Patient medical comorbidities and BVFI etiology were reviewed. Decannulation rates, revision procedures, requirement of emergency services, and pre- and postoperative status were assessed by laryngology and speech-language pathology using the following metrics: VHI, DI, Modified Medical Research Council (MMRC), GRBAS, jitter percentage, shimmer percentage, pitch range, maximum phonation time, and dysphonia severity index, and diet type. Pre- and postoperative distal-chip flexible laryngoscopy with stroboscopy was performed. Outcome measures were assessed using paired Student's t-test of pre- and postoperative categorical variables. Successful decannulation was achieved in all (four of eight) patients with previous tracheostomy. All patients reported improved respiratory symptoms without tracheotomy. There was no difference in long-term voice outcomes. All patients tolerated a mechanical soft or regular diet, without new or worsened dysphagia. Submucosal thyroarytenoid myomectomy with arytenoidectomy represents an effective BVFI treatment, through utilizing natural vectors of scarring, preservation of endolaryngeal mucosa, and preservation of the superficial lamina propria and the vocal ligament. 4 Laryngoscope, 2024.

ACORN+: Adaptive Compression-Reconstruction for Device-Cloud Collaboration Video Services

With the improvement of edge-based autonomous systems such as mobile Industrial IoT(IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the requirements of real-time video services. However, existing encoding standards for video services in Web 2.0 are specifically designed for something other than IoT video streaming. We have improved our Adaptive Compression-Reconstruction framework ACORN to obtain ACORN+, based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. Given that multiple kinds of intelligent tasks are expected to be finished on the device side, we also designed a device-cloud collaboration scheme where deep learning-based algorithms can be executed on both the device and server sides. Experiments reveal that video analytics can be conducted on compressed frames. Taking action recognition as a device-cloud collaboration use case, we find ACORN+ obtains more than 3x speedup on compressed frames. The reconstruction algorithm in ACORN+ is with 1- 4 dB improvements. Moreover, the encoding time cost and the encoded video volume are reduced by more than 4x under the ACORN+ framework. 1

Position paper of DGKL and DIVI on requirements for laboratory services in intensive care and emergency medicine

The timely determination and evaluation of laboratory parameters in patients with acute life- or organ-threatening diseases and disease states in the emergency room or intensive care units can be essential for diagnosis, initiation of therapy, and outcome. The aim of the position paper is to define the time requirements for the provision of laboratory results in emergency and intensive care medicine. Requirements for point-of-care testing (POCT) and the (central) laboratory can be derived from the urgency. Expert groups from the DGKL (Deutsche Gesellschaft für Klinische Chemie und Laboratoriumsmedizin) and DIVI (Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin) developed a classification about the urgency for the determination of laboratory parameters as well as recommendations on the necessary organizational framework and quality assurance measures using national and international guidelines, review articles, and original papers. Three levels of urgency are defined, based on the turnaround time of the most common laboratory parameters: emergency 1, with a turnaround time of no more than 15 min; emergency 2, with a turnaround time of a maximum of 60 min; urgent case, with a turnaround time within 4 h. In addition, a recommendation is made when to provide the results for the main ward rounds in the intensive care unit and the emergency department. The recommendations allow the organizational and technical regulations for each hospital to be aligned with the urgency of the provision of the test results to the medical team based on the medical requirements.

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

Language background and language self-assessment of Spanish-Swedish public service interpreters in Sweden

Advanced language proficiency is a requirement for public service interpreting, and yet, research has not described this construct holistically in interpreting studies. Previous studies have suggested that public service interpreters primarily have a migration background, and asymmetrical language proficiency. The present study reports findings of these aspects from a questionnaire that was conducted to investigate the linguistic background of Spanish-Swedish public service interpreters in Sweden. A total of 118 Spanish-Swedish interpreters answered the questionnaire. Results show that the interpreters have asymmetrical language proficiency but do not primarily have a migration background. Moreover, it is indicated that asymmetrical language proficiency and migration background have an impact on the likelihood of public service interpreters being state authorized. However, there is a need for further research on these aspects to better understand details regarding both public service interpreters in Sweden and public service interpreters internationally.

Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.

Powertrain matching and optimization for pure electric commercial vehicles based on service requirements

Powertrain matching and optimization for pure electric commercial vehicles based on service requirements

World Meteorological Organization (WMO)-Accredited Infrastructure to Support Operational Climate Prediction

Abstract The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate, and water information across its members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on subseasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated Global Producing Centers and collection of forecasts by Lead Centers to facilitate the dissemination of information and products to WMO members and relevant nongovernmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been suboptimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal time scale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.

Determination of the moment–rotation relation of continuous timber–concrete composite floors based on the component method

Timber–concrete composite (TCC) combines the advantages of timber and reinforced concrete construction. In multi-storey buildings, the design of TCC floors is often governed by the deformation serviceability requirements. One way to improve deformation serviceability is through the design of continuous floor systems. However, the current practice of TCC slab systems is often limited to load-carrying as single-span beams. This paper presents an analytical model based on the component method for determining the moment–rotation relation of continuous TCC slabs in the range with negative bending moments. The investigations involve the development of load–displacement curves for the individual force-transmitting components, including timber and reinforced concrete, followed by their assembly into a component model. The model has been verified using finite element calculations and the results demonstrate that the moment–rotation relation of continuous TCC systems can be accurately captured. As the variability of the material properties strongly influences the stiffness of the joint and therefore the deformation and stresses in the TCC slab, a probabilistic analysis of the material parameters was performed using the Monte Carlo method. The probabilistic investigations show that the variability of the input parameters has a significant impact on the joint stiffness, with notable scattering observed in the moment–rotation relation, especially in the range of the cracking phase of the concrete. The results from the probabilistic investigations enable initial statements about the joint stiffness of continuous timber–concrete composite slabs in ranges with negative bending moments.

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

Optical Networking Gym: an open-source toolkit for resource assignment problems in optical networks

The dynamic provisioning of optical network services requires algorithms to find a suitable solution given the specific service requirements and the current network state. These algorithms are usually evaluated using a software simulator developed ad hoc, which may require different levels of detail depending on the problem addressed and how realistic the evaluation needs to be. Moreover, to demonstrate they are a significant contribution to the field, these new algorithms must be benchmarked against the best-performing previously proposed solutions. Due to the large set of parameters and their wide range of possible values, benchmarking algorithms from the literature is not straightforward and can quickly become challenging and time-consuming. This work introduces the Optical Networking Gym, an open-source toolkit that simplifies implementing optical resource assignment simulations and benchmarking new solutions against previously published algorithms. The toolkit provides environments modeling relevant optical networking scenarios, common algorithms for solving problems related to these scenarios, and a set of scripts to prepare and execute simulations for various use cases. Currently, four environments are available, with the possibility of increasing this number through contributions from the co-authors and the community. This paper describes the architecture, interface, environments, and scripts included with the toolkit. We adopt the quality of transmission (QoT)-aware dynamic resource allocation of optical services as the network scenario under examination. Three use cases highlight the toolkit’s modularity, flexibility, and performance. The toolkit allows researchers to streamline the process of developing simulation scenarios and algorithms, enhancing their ability to benchmark their algorithms.

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

Study on the Oxidation Behavior of TiB2-CeO2-Modified (Nb,Mo,Cr,W)Si2 Coating on the Surface of Niobium Alloy.

A novel TiB2-CeO2-modified (Nb,Mo,Cr,W)Si2 coating was prepared on a Nb-5W-2Mo-1Zr alloy substrate using two-step slurry sintering and halide-activated pack cementation to address the limitations of a single NbSi2 coating in meeting the service requirements of niobium alloys at elevated temperatures. At 1700 °C, the static oxidation life of the coating exceeded 20 h, thus indicating excellent high-temperature oxidation resistance. This was due to the formation of a TiO2-SiO2-Cr2O3 composite oxide film on the coating surface, which, due to low oxygen permeability, effectively prevented the inward infiltration of oxygen. Additionally, the dense structure of the composite coating further enhanced this protective effect. The composite coating was able to withstand over 1600 thermal shock cycles from room temperature to 1700 °C, and its excellent thermal shock performance could be attributed to the formation of MoSi2, CrSi2, and WSi2 from elements such as Mo, Cr, and W, which were added during modification. In addition to adjusting the difference in thermal expansion coefficients between the layers of composite coatings to reduce the thermal stress generated by thermal shock cycles, the formation of silicide compounds also improved the overall fracture toughness of the coating and thereby improved its thermal shock resistance.

Effect of Drilling Parameters and Tool Diameter on Delamination and Thrust Force in the Drilling of High-Performance Glass/Epoxy Composites for Aerospace Structures with a New Design Drill.

Real service requirements of the assembly performance and joining properties of design components are critical for composite usage in the aerospace industry. This experimental study offers a novel and comprehensive analysis of dry drilling optimization for glass-reinforced, high-performance epoxy matrix composites used in aerospace structures, focusing on thrust force and delamination. The study presents a first-time investigation into the combined effects of spindle speed (1000, 2250, 4000 and 5750 rpm), feed rate (0.2, 0.4, 0.6 and 0.8 mm/rev) and tool diameter (3 and 5 mm) using a custom-designed drill tool specifically developed for this application, filling a gap in the current literature. By employing the Taguchi design of experiments, the study identified that medium spindle speeds (2250-4000 rpm), lower feed rates (0.2 mm/rev) and smaller tool diameters (3 mm) provided optimal conditions for minimizing thrust force and delamination. These results present actionable insights into improving the structural integrity and performance of drilled aerospace-grade composite components, offering innovative advancements in both the aerospace and defense industries.

5G Network Slicing as a Service Enabler for the Automotive Sector

ABSTRACTNetwork slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range of heterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of network slicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the state of the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such as automotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previous cellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enabler to face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers, and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services.