Search

The future communication technologies like 6G are capable to provide higher mobility and better quality of service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next generation networks. In this paper, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost, load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next generation large scale IoT networks.

As the era of exascale supercomputing is coming, it is vital for next-generation supercomputers to find appropriate applications with high social and economic benefit. In recent years, it has been widely accepted that extremely-large graph computation is a promising killer application for supercomputing. Although Tianhe series supercomputers are leading in the world-wide competition of supercomputing (ranked No. 1 in the Top500 list for six times), previously they had been inefficient in graph computation according to the Graph500 list. This is mainly because the previous graph processing system cannot leverage the advanced hardware features of Tianhe supercomputers. To address the problem, in this paper we present our integrated optimizations for improving the graph computation performance on our next-generation Tianhe supercomputing system, mainly including sorting with buffering for heavy vertices, vectorized searching with SVE (Scalable Vector Extension) on matrix2000+ CPUs, and group communication on the proprietary interconnection network. Performance evaluation on a subset of the Tianhe supercomputer (with 512 nodes and 196,608 cores) shows that our customized graph processing system effectively improves the graph search performance and achieves the BFS performance of 2131.98 GTEPS.

The promise of future generations of mobile communication, both 5.5 and 6G, is that of continued increases in capacity and coverage of the wireless communication network together with novel applications demanding higher levels of performance between the mobile devices and their applications on servers in the cloud. In addition, the growing shift towards virtualization of network function itself offers perhaps the most stringent requirements on the network. To continue to offer ever higher capacities and speeds, future networks will be required to further densify addressing capacity and coverage demands through the use of small cells or new radio spectral bands, such as 7-20GHZ or sub-THZ, with inherently shorter range. Therein lies the dilemma. A denser and ever more complex network of access points, with higher speeds and performance, but continued pressure on the deployment and costs. To address this challenge solutions which address all aspects of the End to End network must be considered together the mobile access points, transport networks, deployment options, and edge cloud all contribute equally to the success of the new consumer and industrial applications promised for next generation networks as well as the operation of the networks themselves. In this paper we will outline the expected requirements of next generation networks as well as new technologies and methods which address the simultaneous challenges of higher capacity, lower costs, and reliable deterministic performance.

A clinical teaching and evaluation tool was created to improve clinical judgment and diagnostic reasoning for Practical Nursing (PN) and Associate Degree in Nursing (ADN) students in preparation for the Next-Generation National Council Licensure Examination (NCLEX). Implementation of the PURPOSE tool offers a structured approach for nursing students to obtain and retain necessary clinical information in a variety of populations and settings. In addition, the PURPOSE tool provides all levels of faculty a consistent method to contribute to clinical evaluation. Integration of the PURPOSE tool at a large community college in the Southeast resulted in a 100% NCLEX pass rate for new stand-alone program PN graduates.

Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the next generation network. The space satellites and air nodes are the potential candidates to assist and offload the terrain transmissions. However, due to the high mobility of space and air nodes as well as the high dynamic of network traffic, the conventional traffic offloading strategy is not applicable for the high dynamic SAGIN. In this paper, we propose a reinforcement learning based traffic offloading for SAGIN by considering the high mobility of nodes as well as frequent changing network traffic and link state. In the proposal, a double Q-learning algorithm with improved delay-sensitive replay memory algorithm (DSRPM) is proposed to train the node to decide offloading strategy based on the local and neighboring historical information. Furthermore, a joint information collection with hello package and offline training mechanism is proposed to assist the proposed offloading algorithm. The simulation shows that the proposal outperforms conventional offloading algorithms in terms of signaling overhead, dynamic adaptivity, packet drop rate and transmission delay.

The deployment of 5G and IoT networks has already started. Considering their growing complexity, such networks require complete autonomy, that is, without human intervention. For this purpose, an orchestration application has been introduced in the field of network management. This application automates, coordinates, and manages network resources, supporting countless demands for services and applications. In this context, complex rule‐driven and artificial‐intelligence based methods are investigated to help orchestrators make accurate decisions in becoming a zero‐touch network. Thus, a decision‐making process at the orchestrator level is important. However, rules defined at the level of the orchestrator can generate delays and yield loops, contradictions, redundancies, or even deadlocks in a network. In this study, a novel mathematical function, located at the orchestrator of the network resources, is presented to support decision‐making in selecting the most relevant rule to apply under a contention scenario. A network requires permanent monitoring; thus, if some rules appear to be ineffective or dangerous for network autonomy in certain cases, they will be eliminated. Under this function, different applicable rules are compared, and the most efficient rule for optimally allocating the required resources is selected. Based on a numerical analysis, we show how the usage of this decision process creates a more resilient and autonomous network.

In 5G and beyond telecommunication infrastructures a crucial challenge in achieving the strict Key Performance Indicators (KPIs) regarding capacity, latency, and guaranteed quality of service, is the efficient handling of the fronthaul bottleneck. This part of the next generation networks is expected to comprise the New Radio (NR) access and the Next Generation Passive Optical Network (NGPON) domains. Latest developments load the fronthaul with computing tasks as well (e.g., for AI-based processes) in the context of Mobile Edge Computing (MEC). Towards efficient management of all resource types, this paper proposes a joint allocation scheme with three optimization phases for radio, optical, and MEC resources. This scheme, which has been developed in the context of the blueSPACE 5G Infrastructure Public Private Partnership (5G PPP) project, exploits cutting-edge technologies, such as radio beamforming, spatial-spectral granularity in optical networks, and Network Function Virtualization (NFV), to provide dynamic, adaptive, and energy efficient allocation of resources. The devised model is mathematically described and the overall solution is evaluated in a realistic simulation scenario, demonstrating its effectiveness.

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.

Nowadays, video conferencing is one of the state-of-the-art and highly demanded technologies for conducting online communications. Providing real-time and rapid connection establishment is one of the chief principles of video conferences which is competently achieved by Multi-Protocol Label Switching (MPLS). Also, the emerging concept of Next Generation Networks (NGN) has accelerated the advancement of IP-based multimedia networks including Voice over IP (VoIP), Video on Demand (VoD) and IPTVs. MPLS networks have formed the foundation of fulfilling multimedia requirements in NGN. Therefore, Label Switching Path (LSP) routing is one of the highlighted challenges of Traffic Engineering (TE) in MPLS networks. MPLS routing algorithms attempt to increase the acceptance rate of requests and consequently meet the Quality of Service (QoS) constraints satisfaction. In this paper, we propose a novel routing algorithm that applies fuzzy rules to meet the bandwidth and end-to-end delay constraints in the routing. The proposed fuzzy mechanism constitutes a predicting system based on weighted fuzzy rules that filters the requests with higher resource demands. We name the proposed method as Fuzzy Bandwidth and Delay guaranteed Routing Algorithm (FBDRA). Schematically, FBDRA aims to defer the requests with maximum bandwidth and minimum end-to-end delays. All simulation experiments are carried out in MATLAB R2019a on different scenarios. We have gauged several metrics such as the number of accepted requests, average path length, energy consumption, load balancing, and average end-to-end delay to evaluate our proposed algorithm performance. The simulation results evidence the superiority of FBDRA in video conference applications.

The never-ending human curiosity about exploring the universe is pushing the limits of robotic autonomy from remote-controlled to fully autonomous systems characterized by advanced learning, cognition, and reasoning for operating in completely unknown and unstructured environments.

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications of unsupervised learning that can enable real-time performance self-diagnosis on edge nodes such as the radio access network intelligent controller (RIC). The first application detects anomalous performance and finds its root cause of faults, configuration, or network procedure failures. The second application uses clustering to learn the relationship between two performance measures. Our proposed applications run in near-constant time complexity, making them, combined with subject-matter expertise validation, suitable real-time RIC applications for network diagnosis., Comment: 5 pages, 5 figures. Submitted to IEEE Communications Letters

With space and aerial platforms deployed at different altitudes, integrated ground-air-space (IGAS) networks will\ud have multiple vertical layers, hence forming a three-dimensional\ud (3D) structure. These 3D IGAS networks integrating both aerial\ud and space platforms into terrestrial communications constitute a\ud promising architecture for building fully connected global next\ud generation networks (NGNs). This article presents a systematic\ud treatment of 3D networks from the perspective of multi-objective\ud optimization. Given the inherent features of these 3D links,\ud the resultant 3D networks are more complex than conventional\ud terrestrial networks. To design 3D networks accommodating the\ud diverse performance requirements of NGNs, this article provides\ud a multi-objective optimization framework for 3D networks in\ud terms of their diverse performance metrics. We conclude by\ud identifying a range of future research challenges in designing\ud 3D networks and by highlighting a suite of potential solutions.

This article describes the envisioned interactions between information and communication technology (ICT) and the aerospace industries to serve autonomous devices for next-generation aerial parcel delivery networks. The autonomous features of fleet elements of the delivery network are enabled by the increased throughput, improved coverage, and near-user computation capabilities of vertical heterogeneous networks (VHetNets). A high-altitude platform station (HAPS), located around 20 km above the ground level in a quasi-stationary manner, serves as the main enabler of the vision we present. In addition to the sensing potential of the HAPS nodes, the use of communication, computing, and caching capabilities demonstrates the attainability of the ambitious goal of serving a fully autonomous aerial fleet capable of addressing instantaneous user demands and enabling supply chain management interactions with delivery services in low-latency settings.

5G relies on its pervasive and convergent cloud-based architecture to accomplish its futuristic challenge of being the next-generation communication platform. However, the new perspectives opened by 5G networks do not go unnoticed. Regardless of their motivation or objectives, cyberattackers find in the new 5G ecosystem, including its tenancy-driven control plane, an attractive greenfield to create new types of denial of services attacks. In this article, we leverage on the virtualised environment of 5G to propose REPEL – an intelligent resource scaling strategy to mitigate DDoS signalling attacks preserving legitimate traffic. Our prevention-based approach uses games theory to build up a defence front line, able to keep services availability and discourage the attacker. To demonstrate the effectiveness and feasibility of our approach, we feed a queuing model with parameters obtained from a testbed, where simulated subscribers connect to a virtualised evolved packet core prototype. The final results show a dramatic signalling losses reduction, which can ensure the appropriate control plane availability under a DDoS attack.

The outbreak of coronavirus 2019 (Covid-19) has imposed a worldwide lockdown, changing the way people live and work, and pressuring the healthcare systems in many countries. Contact tracing based on smartphone apps (app-based contact tracing) has emerged as a possible solution to trace contagions and enforce more sustainable and selective quarantines. However, these apps require a very high adoption rate to reach the critical mass for effective contact tracing. As an alternative, network-based contact tracing, which exploits geo-localization in next generation networks (e.g., 5G) can be used by mobile operators (MOs) to passively trace users' mobility and contacts, provided that a targeted localization accuracy of down to one meter can be achieved. To effectively trace contagions, the identities of positive individuals, which are known by governmental authorities (GAs), are also required. Hence, in network-based contact tracing, MOs and GAs must exchange users' privacy-sensitive data, as geo-locations and infection status, to compute the likelihood that an individual has been infected. To address the privacy issues raised by network contact tracing, after presenting an overview of app-based vs. network-based contact tracing systems, we propose a protocol to make network-based systems compliant with stringent privacy requirements. From extensive simulations, we observe that the cost to guarantee privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable. Finally, we elaborate on the advantages of the proposed system (e.g., more efficient monitoring of virus spread), as well as on the open issues that should be solved before its adoption (e.g., extensive research on potential privacy leakage is required).

Internet of Things (IoT) a growing phenomenon, refers to the seamless integration of things into the information network. The security in IoT is tampered because of the various attacks which happen due to resource constrained nature of the devices in the network. Thus, although IoT is evolving as an attractive next generation networking paradigm, it can be adopted only when the security issues are resolved. This implies that, in a dynamic and collaborative IoT environment, the devices need to be trustworthy. This paper proposes a gateway based trust management system and an algorithm for computation of trust for the devices. The system focuses on making the computations on the devices lightweight and the network robust. The proposed system is tested against various IoT attacks and results demonstrate that it can clearly identify the malicious device if any, in the IoT network.

A precise and stable timing system is necessary for high intensity proton accelerators such as the J-PARC. The original timing system, which was developed during the construction period of the J-PARC, has been working without major issues since 2006. After a decade of operation, the optical modules, which are key components for signal transfer, were discontinued already. Thus, the next-generation timing system for the J-PARC has been developed and built. The new system is designed to be compatible with the original system in terms of the operating principle. The new system utilizes modern high speed serial communication for the transfer of the clock, reference trigger, and type code. We describe the system configuration of the next-generation timing system and current status., 22nd IEEE Real Time Conference

The Optical Wireless Communication (OWC) offers the high capacity of optical fiber communication with the flexibility of wireless communication. Since it works in the optical region of the ElectroMagnetic (EM) spectrum, it guarantees safety and security which are critical in radio and microwave frequency communication. The principal objective of this paper is to analyze the indoor OWC systems on these guaranteed features, and safety and security are jointly denoted by the term green. The high obstacle impermeability of optical signals and their directivity strengthen the security of indoor OWC data transmission. The confidentiality and authenticity of optical wireless data can also be preserved with the Quantum Key Distribution (QKD). This paper provides a technological overview and a review of literature about the OWC system that helps to identify the challenges in the path of a ubiquitous deployment of green wireless communication systems. Significant advancements in the sources and detectors are discussed together with the coding, modulation and multiplexing techniques for making highly robust OWC links. The ubiquitous deployment of green OWC necessitates the development of optical transmitters and receivers, performance enhancement techniques, incorporation of uplink and energy harvesting abilities, and safety and security enhancement techniques. Hence, a special emphasis is placed on these aspects and their challenges towards the green implementation. Furthermore, the paper explores some significant indoor applications based on the OWC that have great impacts on the Next Generation Networks (NGN) and the Internet of Things (IoT).

Routing services in next generation networks not only need to provide good transmission quality in heterogeneous network environments, but also need to meet the differentiated performance requirements of different applications. For example, real-time applications require low latency performance guarantees, while low-cost applications pay more attention to strict cost guarantees. Recently, deep learning has been widely applied in the field of network. With the aid of the powerful deep neural networks, the communication network can perform the routing operation intelligently to avoid the possible failure and congestion. However, existing deep learning-based network routing algorithms cannot satisfy the specific performance requirements of users, because this kind of algorithm is an unconstrained feature learning method essentially, while the routing requirements of different applications are really a constrained problem. In order to solve the above problems, we propose a deep learning-based constrained intelligent routing method, which combines the advantages of Lagrange multiplier method for solving constrained problems and the learning ability of deep learning methods, making the routing service can not only learn complex features to adapt to network environments, but also can meet differentiated requirement of users on the performance. To the best of our knowledge, this is the first work to solve the constrained routing problem by using deep learning system. Experimental results prove the effectiveness of the proposed method and show it is a method suitable for providing high-quality routing services for the next generation network.

Software-defined networking (SDN) has predominantly become the defacto network standard for next generation networking due to its flexibility features; through a single-phase full deployment is unlikely owing to obvious tedious substitution and economic reasons. This has paved the path for hybrid SDN (h-SDN) as a practical alternative with a centralized SDN controller that connects a slow of OpenFlow switches (OFSs) and Legacy switches (LSs). However, such h-SDN imposes fresh challenges since the existing SDN solutions for key functions like topology discovery are rendered futile since LSs do not respond to the controller in the same way OFSs do. State-of-the-art SDN topology protocols, hence, leave out LSs undetected resulting in suboptimal performance. This article presents a novel indirect controller to legacy forwarding (ICLF) scheme that sets out by the controller sending a solitary PACKET_OUT ( P $_{\text{out}}$ ) to an OpenFlow device, which then percolates through the slow of OFSs and LSs; by exploiting the way OFSs and LSs react to the forwarded P $_{\text{out}}$ , the controller can discover the network topology, except for terminal LSs configurations if any. Experiments performed on Mininet shows that the proposed scheme shows an improvement amounted between 34.6%–97.9% in the number of ports detected and reduction in number of messages of about 25%–58.9% under varying degree of legacy device densities, albeit at a slightly overhead of messages forwarded.

New graduate nurse (NGN) turnover during the first year of practice continues to concern nurse leaders and offers operational challenges. Because of the investment in nurse residency programs and the commitment needed to provide resources to operationalize and sustain such a program, it is critical to retain NGNs. In this article, we offer NGNs’ intent to leave at 4 specific times during their first 2 years of practice: upon 100% competency validation, at month 8, year 1, and year 2. We explore reasons why NGNs say they intend to leave and provide insights into what organizational leaders can do to reverse the intent to leave. Lastly, we provide actionable strategies to retain NGNs for nurse leaders to consider.

Numerous devices are expected to upsurge profoundly in the proximate future with an estimated figure exceeding the 50 billion benchmark of connected devices by 2020. There are some rudimentary demands of the subscribers like system capacity, improved data rates with reduction in the latency and secure transmission of data in an unsecured media and to meet these demands, the cellular network has to go under suitable progression. As a building prospective for fulfilling these demands, to overcome the threats and challenges in the transmission of the data and to provide security to the information from the possible attacks, 5G is emerging as an optimal solution. For the demands of the tremendous count of subscribers, Device-to-Device Wireless Communication Network is an operative technology. The escalating content sharing amongst the users has been resulting in an exponential intensification in the wireless data traffic, coercing networks of cellular users to experience a suitable cataclysm. For allocating the resources to the Cellular Users under an attack scenario, numerous advances have been made till now. In this paper, an adaptive resource block allocation algorithm using HMM with beamforming approach has been projected to achieve high values of secrecy rate and low secrecy outage probability in 5G Wireless Communication Networks with the consideration of different applications demanded by Cellular Users and the priority of the applications (video, voice and data) in accord with enhanced Quality of Service of the channel. As the study of inherent secrecy rate for secure transmission of data in Wireless Communication Networks, random techniques are currently in great interest over the communication field. In this paper, it is to explore a multi-user scenario demanding different applications from BS under threat scenario of multi- eavesdropper. In this paper, different types of Beamforming are studied and different techniques involved for secure communication. An architecture has been provided for the security aspect in accordance with beamforming approach. As for better channel capacity, beamforming approach with diversity is an optimal solution for the secure transmission of information for Next Generation Networks.

The outbreak of coronavirus COVID-19 not only brings great disaster to the people of the world, but also brings heavy burden to the medical and health network system. Massive network data traffic and resource optimization requests make traditional network architectures unable to calmly deal with the impact of COVID-19. Artificial intelligence (AI) can effectively raise the upper limit of the medical and health network, as evidenced by the ever-increasing restorative clinical data. In addition, the development of next-generation network (NGN) technologies based on machine learning (ML) has created unlimited possibilities for the emergence of emerging medical methods. In order to reflect the effective results of the current application of AI technologies in the fight against the COVID-19 epidemic and provide a reliable guarantee for subsequent diagnosis and treatment of COVID-19 epidemics, a series of AI technologies which can be used in the diagnosis and treatment of COVID-19 are systematically summarized and analyzed. Based on various AI technologies and methods, we try to propose an AI-based medical network architecture. The architecture uses AI technologies to quickly and effectively realize the monitoring, diagnosis and treatment of patients. Finally, we rationally analyzed the technical challenges and practical problems that may be faced in implementing the architecture. The purpose of this article is to inspire scholars and medical researchers to carry out the latest research in response to the COVID-19 epidemic and make breakthrough medical technology progress.

In recent years, automated software verification has progressed significantly. We can now effectively explore complex software structures through automated testing or to prove properties of complex programs, such as compilers using formal methods. But, for the most part, software testing and formal software verification techniques have advanced independently with relatively few insights on how their research thrusts compare or can be combined.

Develop a proposal for a management model that allows, through knowledge management, to preserve the quality of services during the technological evolution of networks in a telecommunications company. Today, BaghTel is considered the main telecommunications company in Iraq and some time ago it was acquired by the Iraqi government, which has generated a significant organizational transformation, added to the fact of the rapid technological evolution and the strong competition of the sector. These factors give rise to concern on the part of certain units regarding which management model can best adapt to these changes. In addition to these reasons, there is a need for companies like BaghTel to improve the management of this type of updates or migrations, which would later translate into improvements in the quality of services provided by the company. This study seeks to propose a management model that allows optimizing the administration of networks and services, during the organizational transformation processes that occur in a telecommunications company as a result of today's technological evolution. The importance of this work lies in the opportunity to optimize, through a management model, the way to control an important technological change, which will ensure and improve the service levels that any telecommunications company provides to its end users. The final process, measures and assessments, include a viewing and use of management indicators of the results obtained in BaghTel's Specialist Networks Management; this consists essentially of a periodic monitoring and evaluation of the key variables in the management of the TDM (Time Division Multiplexing) and NGN (next generation of networks) networks

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultradense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decisionprocess (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge caches

Mobile-edge computing (MEC) is expected to provide reliable and low-latency computation offloading for massive Internet of Things (IoT) with the next generation networks, such as the sixth-generation (6G) network. However, the successful implementation of 6G depends on network densification, which brings new offloading challenges for edge computing, one of which is how to make offloading decisions facing densified servers considering both channel interference and queuing, which is an NP-hard problem. This article proposes a distributed-two-stage offloading (DTSO) strategy to give tradeoff solutions. In the first stage, by introducing the queuing theory and considering channel interference, a combinatorial optimization problem is formulated to calculate the offloading probability of each station. In the second stage, the original problem is converted to a nonlinear optimization problem, which is solved by a designed sequential quadratic programming (SQP) algorithm. To make an adjustable tradeoff between the latency and energy requirement among heterogeneous applications, an elasticity parameter is specially designed in DTSO. Simulation results show that compared to the latest works, DTSO can effectively reduce latency and energy consumption and achieve a balance between them based on application preferences.

Internet usability is expanded form just human-to-human interactions toward different communication types, while the communication itself is shifting from the host-centric model to the content-centric paradigm. The 5G and beyond networks promise not only to support such changes but also to provide massive data exchange and connectivity with high reliability. The next-generation networking technologies are the key enabled for 5G that aim at building a new ecosystem. One promising piece of this ecosystem is the information-centric network (ICN), which is a future network architecture that tends to tackle the current host-centric model issues. It natively supports several features, including abstraction content naming and transparent in-network content caching that contribute to improve network performance, reduce traffic, and improve the latency. In this article, we first provide a potential road map by introducing different next-generation active technologies to enable the big picture of 5G, including mobile-edge computing (MEC), software-defined networking (SDN), and network function virtualization (NFV). Then, we discuss the need for ICN and its coexistence within this ecosystem. Later, we present an in-depth review of the recent content naming schemes and a comprehensive review of in-network content caching solutions. We classify these solutions into different classes based on the used technologies and their working principle. Finally, we highlight some research challenges and propose promising directions for the research community.

За аналізом якості обслуговування абонентів у реалізованих проектах мереж 5G в світі встановлено, що заявлені вимоги до мереж п’ятого покоління не досягаються в жодній із реалізованих мереж, а фактична якість обслуговування абонентів перебуває на не досить високому рівні, що свідчить про низьку ефективність наявних методів керування мережами стільникового оператора та розподіленої обробки даних у них. Тому було розроблено метод оптимізації розміщення масштабованих послуг на розподілених обчислювальних ресурсах мережі стільникового оператора. Також було проведено імітаційне моделювання для оцінювання ефективності розробленого у роботі методу, результати якого підтвердили його ефективність. The creation of information society in Ukraine is one of the most urgent tasks today. In the Strategy for the Development of the Information Society in Ukraine, the priorities for the formation of the country's modern information infrastructure include the creation of high-speed mobile broadband access networks to Internet resources throughout Ukraine. At the same time, with the development of cellular networks, new and more advanced network architectures for data transmission and management appear. However, there are still a number of unresolved issues and problem areas that need to be addressed accordingly. Therefore, the creation of high-speed networks of the fourth and fifth generations of broadband access to Internet resources and increase of the efficiency of their operation is an urgent and promising task. The purpose of the study is to develop methods to improve the efficiency of distributed data processing in computer systems of cellular operators. Achieving the goal is aimed at improving the quality of customer service of modern cellular networks and meeting the requirements for next generation networks. The paper analyzes the effectiveness of methods, models and technologies of distributed data processing in computer systems of cellular operators and identifies their shortcomings. After that, a method has been developed to optimize the placement of scalable services on the distributed computing resources of the cellular network, which consists in the sequential use of the boundary computing model, a generalized cellular network model and a heuristic solution based on genetic algorithms. The method obtained as a result of this work allows to reduce the level of degradation of the quality of service of end subscribers of the cellular operator's network, in particular, delays of up to 8 ms for a large number of subscribers and, accordingly, a large number of tasks

Witnessing the recent rapid 5G commercialization with multiple advantages in terms of high throughput, low latency, great serviceability, and extreme density, people look forward to seeing a new innovation in the next-generation mobile networks–6G. As a demand response, 6G additionally integrates artificial intelligence into all operational perspectives of the network while incorporating new infrastructure to support service coverability to yet underserved areas. Since the artificial intelligence in 6G has been discussed significantly in the literature, this article, on the other hand, provides major insights of the 6G aerial radio access network (ARAN) as an expansion of mobile coverage from the sky. First, we highlight the distinct attributes of ARAN by constructing a comparative taxonomy among mobile access infrastructures. Consequently, comprehensive ARAN architecture, reference model, and potential technologies are analyzed. Next, current research trends are investigated followed by future challenge discussions.

In smart electricity grids, high time granularity (HTG) power consumption data can be decomposed into individual appliance load signatures via Nonintrusive Appliance Load Monitoring techniques to expose appliance usage profiles. Various methods ranging from load shaping to noise addition and data aggregation have been proposed to mitigate this problem. However, with the growing scarcity of natural resources, utilities other than electricity (such as water and natural gas) have also begun to be subject to HTG metering, which creates privacy issues similar to that of electricity. Therefore, employing privacy protection countermeasures for only electricity usage is ineffective for appliances that utilize additional/other metered resources. As such, existing privacy countermeasures and metrics need to be reevaluated to address not only electricity, but also any other resource that is metered. Furthermore, a holistic privacy protection approach for all metered resources must be adopted as the information leak from any of the resources has a potential to render the privacy preserving countermeasures for all the other resources futile. This paper introduces the privacy preservation problem for multiple HTG metered resources and explores potential solutions for its mitigation., 7 pages, 6 figures, preprint

It has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of service and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.

Currently, telecommunication research communities are striving towards the adoption of Zero-touch network and Service Management (ZSM) in Network Function Virtualisation (NFV) orchestration. Contemporary efforts on adopting Machine Learning (ML) and Artificial Intelligence (AI) have caused an upsurge of ZSM application in the VNF space. While ML and AI complement the ZSM goals for building the intelligent NFV orchestration, a deep knowledge about the resource consumption by Network Services (NSs) and its constituent Virtual Network Functions (VNFs) is required, which would enable AI and ML models to manage the available resources better and enhance user experience. In this article, we propose a Novel Autonomous Profiling ( NAP ) method that not only predicts the optimum network load a VNF can support but also estimates the required resources in terms of CPU, Memory, and Network, to meet the performance targets and workload by utilising ML techniques. Our performance evaluation results on real datasets show that the output of NAP can be used in the next generation of NFV orchestration.

Background: New graduate nurses (NGN) are the future of healthcare organizations where a shortage of nurses will soon be reality. The transition from a student to a registered nurse can be a demanding and challenging time, and in order to retain new graduate nurses, the transition should be as smooth as possible. Aim: The aim of this study was to examine preceptors’ education intervention’s impact on NGNs’ orientation period and their clinical learning environment from NGNs’ point of view. Material and Methods: This survey was a part of a longitudinal quasi-experimental intervention study. Participating units were randomized into intervention group and control group. The intervention group’s preceptors (n=174)were provided a face-to-face education about orientation. Results: NGNs (n=72) were relatively satisfied with the received orientation. However, intervention group was more discontented with their orientation experiences than the control group. The control group’s NGNs had longer orientation periods and more feedback discussions. They were also more satisfied with their preceptor’s orientation skills and their working environment was more responsive. Discussion: Even though the impact of the intervention was inconclusive, this study provided us important information about NGNs’ first steps towards the nursing profession by giving us clear improvement targets. We need a culture of feedback, individualized orientation and understanding that orientation should be our common interest. Investing in orientation is investing for the future. Conclusion: This study indicated that the increase in knowledge did not translate into everyday practice. This finding will hopefully give rise to discussion within organizations on how to utilize employees’ gained knowledge more effectively. publishedVersion