Service Requirements Research Articles

Security-Communication-Computation Tradeoff of Split Decisions for Edge Intelligence

Security and privacy are of paramount importance to data-driven edge intelligence services. By offloading computation-intensive model portions to the edge server, split inference can empower low-latency and energy-efficient model inference at resource-constrained devices. The split decision is critical to the communication and computation performances of split inference. However, its impact on security issues has yet to be studied. This article first investigates the security-communicationcomputation tradeoff of split decisions in the inference phase. With the emphasis on security concerns, we summarize the threat models of split inference and illustrate two passive attack mechanisms for recovering the input data and private labels. Case studies are shown to validate the security-communicationcomputation tradeoff and investigate the selection of optimal candidate split points according to the specific device capabilities and service requirements.

Deep reinforcement learning based latency-energy minimization in smart healthcare network

Significant breakthroughs in the Internet of Things (IoT) and 5G technologies have driven several smart healthcare activities, leading to a flood of computationally intensive applications in smart healthcare networks. Mobile Edge Computing (MEC) is considered as an efficient solution to provide powerful computing capabilities to latency or energy sensitive nodes. The low-latency and high-reliability requirements of healthcare application services can be met through optimal offloading and resource allocation for the computational tasks of the nodes. In this study, we established a system model consisting of two types of nodes by considering nondivisible and trade-off computational tasks between latency and energy consumption. To minimize processing cost of the system tasks, a Mixed-Integer Nonlinear Programming (MINLP) task offloading problem is proposed. Furthermore, this problem is decomposed into task offloading decisions and resource allocation problems. The resource allocation problem is solved using traditional optimization algorithms, and the offloading decision problem is solved using a deep reinforcement learning algorithm. We propose an Online Offloading based on the Deep Reinforcement Learning (OO-DRL) algorithm with parallel deep neural networks and a weight-sensitive experience replay mechanism. Simulation results show that, compared with several existing methods, our proposed algorithm can perform real-time task offloading in a smart healthcare network in dynamically varying environments and reduce the system task processing cost.

Optimizing high availability multi-controller placement in SDN/NFV 5G networks: a survey

<p>In meeting the diverse and occasionally conflicting quality of service (QoS) requirements associated with modern communication networks, 5G technology has emerged as a pivotal player. In its architecture, 5G has adopted network function virtualization (NFV) and cloud-based approaches, aiming to simplify network and service deployment, operational processes, and management. The convergence of software defined networking (SDN) and NFV offers an effective solution, enabling scalable and high-performance 5G networks. However, this integration poses critical challenges, with the placement of SDN controllers being a central concern due to its significant impact on network performance, covering aspects such as latency, costs, and energy efficiency. This challenge is known as the controller placement problem (CPP). The central theme of this paper revolves around the intricate relationship between 5G core networks, virtualization technology, and the pressing concern of SDN controller placement, underscoring its significance in the modern networking landscape. We provide a survey of recent methodologies aimed at solving the CPP within the realm of SDN, with a particular focus on resiliency and high availability.</p>

Connection strength analysis for composite fuselage gate structures of civil aircraft

Civil aircraft boarding gate is an important channel for passengers to enter and exit the aircraft, and is also an important part of the aircraft structure, with the function of maintaining cockpit pressure. Traditional boarding doors are made of metal, and with the large number of composite materials used in the bearing structure of civil aircraft, composite boarding doors are also used in new civil aircraft. The strength of the connection inside the new composite boarding gate is related to the safety of passengers and crew. This paper focuses on the connection problem of the internal structure of composite boarding gate, aiming to analyze its connection strength under working conditions. This paper establishes a real finite element model of the boarding gate according to the loading condition and force transmission characteristics of the civil aircraft door and analyzes the connection strength of the structure such as the skin and the transverse and longitudinal beams, the skin and the side frame, the transverse and longitudinal beams connection, and the stop block in turn. The study shows that the strength of the connecting structures of the boarding gate designed by using composite materials is reasonable and can meet the service requirements of the boarding gate, thus demonstrating the rationality of the boarding gate structure designed with composite materials.

Edge Server Deployment for Health Monitoring With Reinforcement Learning in Internet of Medical Things

The Internet of Medical Things (IoMT) has recently gained a lot of interest in the health care industry. IoMT enables real-time and omnipresent monitoring of a patient's health status, resulting in massive amounts of medical data being generated. The centralized massive data processing places enormous strain on the typical cloud computing, rendering it incapable of supporting a variety of real-time health care applications. Therefore, edge computing that moves application programs and data processing from central infrastructure to the edge nodes has attracted wide attention. However, adopting existing edge server (ES) deployment strategies for IoMT is not suitable due to the decentralized and high real-time service requirements of IoMT systems. In particular, traditional ES deployment strategies in IoMT system confront major load imbalance across ESs, latency issues, and energy consumption concerns. To address these challenges, a deployment strategy of ESs based on the state-action-reward-state-action (SARSA) learning, named ESL, is designed. Specifically, ESs are quantified by evaluating the silhouette coefficient (SC) and the sum of squared errors. Then, through fuzzy C-means (FCM) algorithm, the preliminary division of health monitoring units (HMUs) and the initial locations of ESs are obtained. Finally, SARSA learning is adopted to determine the deployment of ESs. Furthermore, extensive experiments and analyses confirm that ESL achieves the core objective of optimizing load balancing among ESs while also optimizing request-response latency and request processing energy consumption.

Preparation and thermal cycling performance of Ce-doped YSZ thermal barrier coatings by a novel cathode plasma electrolytic deposition

The preparation and realization of highly reliable thermal barrier coatings (TBCs) on the internal surface of complex cavities is a bottleneck problem. Traditional coating methods are difficult to deposit and fulfil service requirements. In this study, a novel structure of YSZ and Ce-doped YSZ (CeYSZ) TBCs was prepared by a newly cathode plasma electrolytic deposition (CPED) technology. The results suggested that both the CPED-YSZ and CPED-CeYSZ coatings exhibited porous cross-linked dendritic structures. Because Ce doping significantly improved the phase stability of tetragonal structure and enhanced thermal insulation temperature during a burner-rig test at 1300 °C, resulting in a threefold increase in the thermal cycling life of the CPED-CeYSZ coating compared to the CPED-YSZ coating. This work provided a new strategy for tailoring multiple rare-earth principal components of high-performance TBCs and depositing them on the inner surface of complex cavities with high aspect ratios.

APLIKASI ALAT BANTU ERGONOMIS PADA KERJA MANUAL BERDASARKAN KAJIAN ERGO-MECHANICAL UNTUK PETANI KECIL

Using traditional tools and manual work by small farmers in rural and developing areas is routine in processing agricultural land, especially planting plant seeds. This working model includes moderate to severe workload due to additional workload, namely sunlight when planting. In addition, work is carried out with non-ergonomic work postures, namely non-natural work postures. This causes musculoskeletal complaints to workers due to the non-harmonious tools with workers. Workers' productivity will decrease and have the potential to reduce the quality of life of small farmers themselves. To help the manual work of small farmers to create effective, comfortable, safe, healthy, efficient, and sustainable jobs, an ergo-mechanical application is needed to design work tools. This application is based on studies conducted on the design of work tools from several libraries or research results. The ergo-mechanical study is an integrated system that applies ergonomic and mechanical principles. The ergo-mechanical application produces ergonomic work tools that are comfortable to use by workers, and technical standards include material, product, and service requirements. Mechanically, the application through technical drawings, mechanical planning, and work tool materials to get a strong, lightweight, and safe work tool from hazardous substances, according to technical specifications. Technical standards are related to data sheets or specifications that describe the technical characteristics of a work tool. On the specifications of the work tool design, specifications can be added that the work tools are ergonomic. Ergonomic work tools can produce effective, comfortable, safe, healthy, and efficient work. This results from the compatibility between work tools and workers or the harmonious work tools with workers. Ergo-mechanical creates comfort in work that affects the increase in productivity and quality of life for workers.

Optimized provisioning technique of future services with different QoS requirements in multi-access edge computing

With the present trend towards the use of microservices instead of monolithic software, future applications with diverse computational and delay-sensitive requirements, such as virtual reality (VR) or holographic communications (HC), will be redesigned based on loosely coupled microservices due to their more efficient deployment according to available resources. However, it remains a big challenge to provide such services under stringent quality of service (QoS) requirements at minimum time and cost and with an efficient utilization of the network. Presently, multi-access edge computing (MEC) has been proposed as a promising alternative to deploy future microservice-based applications to achieve faster response times while reducing the overall network load. However, network conditions, edge nodes state and service requests are dynamic, increasing the difficulty for the provisioning of services by MECs. In this context, we propose an optimization deployment heuristic (ODA) to obtain sub-optimal deployments in reasonable time, meeting the requirements of some representative future applications under study. In particular, according to latency requirements, there will be delay-tolerant and delay-sensitive applications, playing a major role regarding the alternatives for the deployment onto single or multiple MEC systems. The evaluation analysis shows that our proposal performs significantly better than other benchmark solutions in most of the metrics used for the experiments, especially in minimizing deployment costs in minimal time.

An Overview of the Recyclability of Alternative Materials for Building Surface Courses at Pavement Structures

This paper overviews the use of several waste materials for the construction and reconstruction of surface courses of asphalt pavements in the framework of sustainable perspectives that are adopted in pavement engineering. Based on a relevant literature search, the most commonly investigated alternative materials include waste plastic, crumb rubber, waste glass, steel slag, and Reclaimed Asphalt Pavement (RAP). Although recycling in pavement engineering is not a novelty, the strict performance requirements of the surface layers required to support a distress-resistant behavior possess continuous research challenges about the mechanical behavioral parameters, such as fatigue, rutting, moisture damage, and serviceability requirements, such as skid resistance. While studies in a laboratory environment mainly dominate, the importance of performance observations of real structures in the field is also pinpointed in an effort to provide a comprehensive overview of the so far knowledge status. Thereafter, this paper discusses peculiar issues and criteria for waste material selection that should balance performance requirements, local availabilities, and potential legislation concerns, thereby maximizing the economic or environmental advantages.

Service Requirement Analysis of Community Scrap Recycling Platform Based on Kano Model

Service Requirement Analysis of Community Scrap Recycling Platform Based on Kano Model

Customer focusion as a guarantee of success in tourism activities

The relevance of this topic is due to the increasing needs of clients, their wishes, travel experience, and as a result, increasing requirements for service. The article suggested three tools needed to achieve, or at least improve, the skills needed to work with clients. An employee working with a client must immediately understand HOW to behave with the client, how to offer him a product and services, and how to build communication.

Dampak Penyaluran Kredit KUR dan KKI terhadap Pendapatan UMKM Tahu dan Tempe Kecamatan Sebulu Kabupaten Kutai Kartanegara

Kredit Usaha Rakyat (KUR) is credit or financing provided by banks to UMKM whose businesses are deemed viable but cannot meet the requirements of other financing services offered by banks. This research aims to determine the impact of KUR and KKI credit distribution on the income of tofu and tempeh UMKM, Sebulu District, Kutai Kertanegara Regency. The method used in this research is a quantitative descriptive method. UMKM sampled in this research were 23. The data analysis used is non-linear regression analysis. The research results show that the income of tofu and tempe UMKM in Sebulu District, Kutai Kartanegara Regency is significantly influenced by raw materials and business capital. The impact of KUR and KKI distribution is an increase in the use of raw materials by 80% and an increase in UMKM turnover which also has an impact on increasing the value of savings for UMKM owners who follow KUR by 41% and those who follow KKI by 48%.

Proportional-fair uplink resource allocation with statistical QoS provisioning for RAN slicing

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

Analysis of Community Satisfaction in Providing Public Services at the Tempe District Office, Wajo Regency

The success of providing public services is largely determined by the level of community satisfaction. Community satisfaction is achieved when service recipients receive services in accordance with what is needed and expected. The aim of this research is to determine and analyze the level of public satisfaction in the provision of public services at the Tempe District Office, Wajo Regency, as well as to analyze and describe the factors that are obstacles and supporters in the provision of public services at the Tempe District Office, Wajo Regency. This type of research is a qualitative case study type approach with data collection techniques through observation, in-depth interviews and documentation studies. The results of the research show that the analysis of community satisfaction in the implementation of public services at the Tempe District Office, Wajo Regency is based on indicators of elements of community satisfaction, namely requirements, systems, mechanisms, procedures, completion time, tariff costs, product specifications, types of services, competency of implementers, behavior of implementers, complaint handling, facilities and infrastructure can be categorized as not fully meeting community satisfaction and service recipients have not received services according to what is needed and expected. This can be seen in the uncertainty of the completion time for population administration services, as well as the lack of population data processing equipment. Apart from that, there is still a lack of socialization to the public regarding service requirements, so that it does not meet the expectations and satisfaction of service users.

Multi-agent DRL-based Multipath Scheduling for Video Streaming with QUIC

The popularization of video streaming brings challenges in satisfying diverse Quality of Service (QoS) requirements. The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, has the potential to improve video streaming performance with multiple simultaneously transmitting paths. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, while applying current multipath schedulers into MPQUIC, our experimental results show that they fail to adapt to various receive buffer sizes of different devices and comprehensive QoS requirements of video streaming. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-agent deep Reinforcement learning (MADRL)-based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and deploy in Dynamic Adaptive Streaming over HTTP system. Then, we compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.

INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION

In the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.

Health service use in major trauma survivors: A population-based cohort study from Ontario, Canada.

Little is known about how major trauma survivors access health services in the years following their injury. Our study sought to characterize patterns of health services use in trauma survivors following discharge from a provincial trauma center and to identify sociodemographic factors associated with service utilization. We conducted a population-based retrospective case-control study using linked administrative data on trauma survivors' population-based controls between April 1, 2011, and March 31, 2021. For each major trauma survivor, we matched four cases based on age and sex. The primary outcome was the composite rate (sum) of health service use episodes including outpatient visits to family physicians and specialists, emergency department visits, and acute care hospital admissions during the 5-year period following discharge from the trauma center. We used multivariate regression to compute rate ratios comparing the rates of health service use in trauma survivors versus controls and to assess for associations between sociodemographic variables and health services use. The study cohort consisted of a total of 273,406 individuals: 55,060 trauma survivors and 218,346 controls. Trauma survivors were predominately males (71%) with a median age of 46 years (interquartile range, 26-65 years). Health service use in trauma survivors peaked within a year of hospital discharge but remained increased throughout the follow-up period. Trauma survivorship was associated with a 56% increase in overall health services use (adjusted rate ratio, 1.56; 95% confidence interval, 1.55-1.57), including an 88% increase in hospital admissions (adjusted rate ratio, 1.88; 95% confidence interval, 1.85-1.92). Male sex and rural residence were associated with a reduced overall use of health services but greater use of emergency department services. Major trauma survivors have long-term health services needs that persist for years after discharge from the trauma center. Future research should focus on the understanding why trauma survivors have prolonged health services requirements and ensure that care needs are aligned with service delivery. Prognostic and Epidemiological; Level IV.

Secure transmission design in cooperative NOMA networks: Orienting toward user requirements

AbstractThis paper presents a full‐duplex and multi‐antenna relay‐aided transmission scheme for the user‐oriented perspective in cooperative non‐orthogonal multiple access (NOMA) systems. Relying on the service requirements, two legitimate users with security demands (SU) and quality of service (QoS) demands (QU) are paired for NOMA execution. The security of SU is enhanced through inverse power allocation of the conventional NOMA. The QoS requirements for QU are guaranteed, simultaneously. Then, the precoding design at the multi‐antenna relay has been exploited to improve the reliability demands of SU. Closed‐form expressions for SU's outage probability (OP) and intercepted probability, as well as QU's OP, are derived. The security–reliability trade‐off is also presented. Additionally, performance analysis under the half‐duplex and multi‐antenna scheme is provided as a benchmark for comparison. Finally, numerical simulations are performed to confirm the accuracy of the theoretical derivations under different schemes. Results demonstrate the superiority of the proposed scheme over benchmark schemes in terms of improving the security and reliability of SU, as well as saving power consumption of the system.

Dynamic hierarchical intrusion detection task offloading in IoT edge networks

AbstractThe Internet of Things (IoT) has gained widespread importance in recent time. However, the related issues of security and privacy persist in such IoT networks. Owing to device limitations in terms of computational power and storage, standard protection approaches cannot be deployed. In this article, we propose a lightweight distributed intrusion detection system (IDS) framework, called FCAFE‐BNET (Fog based Context Aware Feature Extraction using BranchyNET). The proposed FCAFE‐BNET approach considers versatile network conditions, such as varying bandwidths and data loads, while allocating inference tasks to cloud/edge resources. FCAFE‐BNET is able to adjust to dynamic network conditions. This can be advantageous for applications with particular quality of service requirements, such as video streaming or real‐time communication, ensuring a steady and reliable performance. Early exit deep neural networks (DNNs) have been employed for faster inference generation at the edge. Often, the weights that the model learns in the initial layer may be sufficiently qualified to perform the required classification tasks. Instead of using subsequent layers of DNNs for generating the inference, we have employed the early‐exit mechanism in the DNNs. Such DNNs help to predict a wide range of testing samples through these early‐exit branches, upon crossing a threshold. This method maintains the confidence values corresponding to the inference. Employing this approach, we achieved a faster inference, with significantly high accuracy. Comparative studies exploit manual feature extraction techniques, that can potentially overlook certain valuable patterns, thus degrading classification performance. The proposed framework converts textual/tabular data into 2‐D images, allowing the DNN model to autonomously learns its own features. This conversion scheme facilitated the identification of various intrusion types, ranging from 5 to 14 different categories. FCAFE‐BNET works for both network‐based and host‐based IDS: NIDS and HIDS. Our experiments demonstrate that, in comparison with recent approaches, FCAFE‐BNET achieves a 39.12%–50.23% reduction in the total inference time on benchmark real‐world datasets, such as: NSL‐KDD, UNSW‐NB 15, ToN_IoT, and ADFA_LD.

AI-driven resource management strategies for cloud computing systems, services, and applications

Cloud computing is a way for businesses and individuals to It has changed and revolutionized the way we access and use resources. However, efficient resource management in cloud computing systems remains a major challenge due to the scalability, heterogeneity, and dynamic nature of these environments. To address these challenges, artificial intelligence (AI) technology has emerged as an effective solution to improve resource management efficiency. This paper provides an overview of AI-based strategies for efficient resource management in cloud computing systems, services, and applications. This paper first reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. Below is an overview of the various AI techniques used for resource management. B. Algorithms for machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. Next, this paper considers specific AI-based strategies for efficient resource management. These strategies include automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. We also present case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. This paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including B. real-time resource optimization and coordination.